This paper proposes a scalable model-free multi-agent deep reinforcement learning (MADRL) framework for voltage regulation in grid-interactive efficient buildings. Unlike traditional methods that rely on reactive power control, the proposed approach utilizes active power adjustment through intelligent demand response (DR) scheduling. The architecture features a decentralized control structure, where customer agents optimize their appliance usage based on dynamic incentives from an aggregator agent. The optimization problem considers various constraints such as user comfort, electricity pricing, voltage deviation penalties, and the presence of distributed photovoltaic (PV) generation. A multi-objective function integrating dynamic price signals, user dissatisfaction, and voltage deviation is formulated. The aggregator leverages voltage-aware incentive signals to nudge consumers toward grid-supportive load behaviors. Simulation investigations are curried out to show that the MADRL framework reduces peak and mean load, improves voltage stability, and preserves user privacy. The paper aims to demonstrate the potential of decentralized, model-free DR systems in modern distribution grids.

This study proposes a novel single-phase switched capacitor multilevel inverter structure for symmetrical and asymmetrical operation. The proposed configuration requires two DC sources, twelve switches, two flying capacitors, and a single diode to generate 9-level and 21-level output voltage. The proposed configuration features inherent self-voltage balancing without sensors, ensuring stable operation under varying conditions. The switched-capacitors enhance voltage gain (Vo=10Vdc) while reducing component count and switch stress. A single-carrier pulse width modulation (SC-PWM) method is utilized to minimize computation time and produce gating signals. The proposed topology is designed and simulated in PLECS software, incorporating thermal modeling to assess its efficiency and overall performance. The experimental setup was prepared, and a thorough comparative evaluation was carried out using dual-source-based topologies. The proposed topology demonstrates superior performance and enhanced design characteristics.

As the global transition toward electrification accelerates across the transportation and stationary energy storage sectors, the critical need for accurate end-of-life (EoL) prediction of lithium-ion batteries (LIBs) has become increasingly apparent. Current battery failures impose substantial costs on manufacturers through warranty claims, while creating significant safety risks that threaten both electric vehicle (EV) adoption and grid-scale energy storage deployment. This paper examines the modeling approaches to predict the EoL and the remaining useful life (RUL) of LIBs in EVs. The paper includes data-driven models, physics-based approaches, and hybrid frameworks. Through systematic analysis of recent advances, the paper identifies that hybrid models demonstrate superior performance compared to single-approach methods, effectively addressing the inherent limitations of individual methodologies across diverse operating conditions. Key challenges remain in Battery Management System (BMS) integration complexity, data quality constraints, and real-time computational requirements. The proposed review establishes that next-generation prediction systems and incorporates transfer learning, digital twin technologies, and second-life battery strategies to support sustainable EV adoption and circular economy principles.

Model Predictive Control (MPC) has become a widely adopted control technique for Multilevel Inverters due to its ability to manage multi-objective optimization problems under system constraints. However, a key challenge in MPC implementation lies in selecting appropriate weighting factors for the cost function, as fixed values often lead to suboptimal performance under dynamic operating conditions. Thus, this paper presents a lightweight auto-tuning method for the voltage weighting factor in Finite Control Set MPC (FCS-MPC), applied to a single-phase grid-connected 9-level Crossover Switches Cell inverter. The proposed approach employs low computational complexity machine learning models, Linear Regression and Support Vector Machine, trained offline on a minimal dataset comprising the DC link voltage and reference current. These models are embedded into the control loop to enable real-time adjustment of the voltage weighting factor. The presented comparative simulation results confirm the effectiveness of the proposed technique across a wide range of operating conditions. Compared to more complex AI-based solutions, this work contributes a simple yet effective ML-based tuning strategy that improves control performance with minimal computational overhead.

This paper presents a multi-objective control strategy for a four-port solid-state transformer (SST) interfacing photovoltaic (PV) system, battery energy storage (BESS), utility grid, and a 48V data center load. The proposed control approach simultaneously optimizes multiple objectives, including power allocation and load voltage regulation. The main objectives of the proposed approach are to supply a constant 48V to the data center while maximizing renewable energy utilization, and balancing the demand and supply by dynamically injecting the extra available power into the grid or the battery and consuming it from the grid or the battery if there is no sufficient renewable energy availability. The proposed approach is experimentally implemented and its effectiveness is validated through a set of different case studies.

This paper proposes a DC plasma power supply topology with systematic design and robust voltage control approach. The proposed topology consists of a three-port solid-state transformer (SST), comprising one port for the input source, a second port to deliver high voltage (HV) required for plasma ignition, and a third port for supplying a constant low voltage (LV) for sustained plasma reactor operation. A comprehensive design methodology is presented to determine all passive elements, including the resonant tank components and transformer’s turns ratios. Moreover, a seamless transition algorithm between ignition and operation modes is presented. To achieve micro-scale dynamic response, an inverse model predictive control (IMPC) strategy is employed to identify the necessary switching frequency in feedforward fashion. In parallel, a PI controller is used to eliminate the deviation and steady state error of the output voltage and ensure asymptotic error convergence, even in the presence of parameter uncertainties. The proposed circuit, seamless transition algorithm, and the control technique are implemented on a lab-scale test-bed with voltage range between 0-1200 V.

Transient Stability (TS) remains a critical concern in ensuring secure operation of modern power systems, particularly with the growing complexity introduced by renewable energy integration, power electronic devices, and hybrid AC/DC infrastructures. Traditional methods for transient stability assessment (TSA) and transient stability-constrained optimal power flow (TSC-OPF), such as time-domain simulations and energy function-based techniques, face limitations in scalability, computational efficiency, and real-time applicability. This paper presents a holistic review of Machine Learning (ML) approaches adopted for TSA and TSC-OPF, covering a range of models from traditional learners (e.g., support vector machines, decision trees) to DL (e.g., convolutional neural networks, long short-term memory networks, graph neural networks) and Reinforcement Learning (RL) techniques. For each domain, methodologies will be categorized, highlighting key advancements, and discussing trade-offs in performance. Furthermore, existing challenges are identified and future research directions are proposed, emphasizing on hybrid modeling, uncertainty handling, real-time assessment and RL challenges. This review aims to serve as a timely reference for researchers and practitioners working on data-driven solutions for power system TS.

Transient instability poses a critical challenge to the reliable operation of modern power systems, often leading to large-scale blackouts. Despite the success of data-driven Transient Stability Assessment (TSA), its practical implementation remains limited by challenges in processing high-speed real-time data streams and preserving data privacy. To address these limitations, this article develops a novel Federated Adaptive Random Forest (FedARF) method that integrates federated learning with the Adaptive Random Forest (ARF) model. The proposed decentralized framework incorporates concept drift adaptation mechanisms to accommodate the stochastic and dynamic characteristics of modern power systems. FedARF facilitates distributed knowledge aggregation learned from various heterogeneous local data sensors (clients) to predict and evaluate the TSA status with minimal communication overhead. Comprehensive experiments on the New England 39-Bus system, the IEEE 68-Bus system, and the large-scale ACTIVIgs 25k-Bus system demonstrate the efficiency of the proposed method with an overall accuracy of 99.65%. Compared to traditional centralized forecasting methods, and state-of-the-art models, the proposed approach not only maintains high prediction accuracy but also enhances data privacy preservation while substantially reducing communication bandwidth requirements.

The rapid advancement in electric vehicle (EV) technology stimulated the development of fast chargers, with innovative solutions in power electronics (PEs), communication, protection, and control. This literature review explores the evolving landscape of fast EV charging infrastructures, and focuses on their topologies, advanced PEs solutions, and associated challenges. In addition, this article explores the multifaceted aspects of fast charging systems. In addition, the article discusses the protection systems, communication systems, and control techniques for facilitating seamless, safe, and reliable interaction between the grid, fast EV chargers (FEVCs), and the EV. Furthermore, the review investigates the interaction between the grid and FEVCs through ancillary services, and highlights their potential to enhance grid stability and reliability. Although a significant research is conducted in the literature and a significant progress is achieved, a notable gap persists between the available academic literature and commercially deployed solutions. Investigating this gap is crucial for accelerating the adoption of FEVC technologies and maximizing their benefits for both consumers and grid operators. Therefore, a reference design is synthesized that combines the recommended elements identified throughout the literature.

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction.

Integrating Deep Reinforcement Learning (DRL) into building energy management systems presents a transformative approach to enhancing grid stability and efficiency. Grid-Interactive Efficient Buildings (GEBs), equipped with advanced DRL algorithms, can dynamically optimize their energy consumption and production in response to real-time grid conditions. This paper explores the innovative applications of DRL in GEBs, highlighting its potential to autonomously optimize energy decisions, accommodate the stochastic nature of renewable energy sources, and effectively respond to variable building energy demands. Through a comprehensive analysis, this study not only sheds light on the successes to date but also maps out the significant challenges that must be overcome. By addressing these challenges, DRL for building energy management can fully realize its potential, leading to a more sustainable and efficient energy future.

Virtual impedance-based power sharing between several interconnected grid-forming inverters (GFMI)s in a power electronics-dominated grid (PEDG) does not consider the available power reserves. This non-optimal power-sharing may result in the overloading of nearby inverter-based resources (IBR)s exhibiting lower effective line impedances in the network. This paper proposes an optimal and controllable power-sharing scheme based on a modular artificial neural network (ANN) that can consider various factors like power reserve, line losses, incentives from the grid, etc., and increases the system's scalability. An optimum power allocator (OPA) mechanism that monitors the output active powers and line losses of all the IBRs is proposed to address the issue of uncontrolled power sharing. The OPA dynamically adjusts power allocation weights using linear programming to optimize the overall loss minimization cost function. Moreover, an artificial neural network-based virtual impedance optimizer (ANN-VIO) is also proposed, which estimates the corresponding virtual impedance (VI) value for each GFMI unit in the PEDG. The primary controller of GFMI uses these estimated VI values to adjust its point of common coupling (PCC) voltage reference and attain optimum proportionate power sharing within the overall PEDG. Multiple case studies are performed to validate the effectiveness of the proposed GFMIs power-sharing scheme.

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (ML) and deep learning (DL) models for DT implementation in DABs. Specifically, XG-Boost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler.

This paper presents a grid voltage sensorless model predictive control (MPC) technique for a single-phase fivelevel active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes MPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the MPC. Achieving this involves integrating all controlled parameters into a finite control set MPC (FCS-MPC). Moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using MATLAB/Simulink. Simulation results are carried out to show that the proposed MPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions.

Grid-forming inverters (GFMIs) are promising solutions for voltage and frequency support in upcoming power electronics-dominated grids (PEDG). However, current state-of-the-art decentralized control schemes for GFMIs are designed for operation under normal conditions. These decentralized control schemes could result in an adverse dynamic response if a cluster of GFMIs network disconnects from the rest of the grid due to a fault. The dynamic response of GFMIs could be improved with coordinated control schemes which require communication and make the system more complex. This paper proposes a decentralized Artificial Intelligence (AI)-based method for online fault detection and localization to enhance the dynamic response of GFMIs without using a communication layer. Each GFMI will detect and localize the line tripping based on its output active and reactive power measurement. Then, according to the topology of the grid after line tripping, each GFMI updates its nominal power and inertia to suppress the frequency transient caused by the line tripping. The inertia and damping factor of the GFMIs are then recalculated to ensure optimal operation of the network of inverters after line tripping. Several case studies are presented to validate the effectiveness of the proposed method in the timely detection, localization, and mitigation of adverse frequency transient after line tripping in a communication-less manner.

Partitioning the power system into smaller, manageable units facilitates better grid monitoring and control, thereby improving the grid’s stability and reliability. However, large-scale power networks consist of thousands of nodes and edges, which complicates the process of learning appropriate node embeddings and aggregating information from neighboring nodes. By representing power grids as undirected weighted graphs, this study proposes a novel power network partitioning approach using Graph Neural Networks (GNN). The proposed model simplifies the clustering objective by focusing on a single balancing term, which reduces computational complexity while maintaining competitive clustering performance. The power network is represented as a graph where the proposed GNN uses the normalized graph Laplacian, which effectively captures the complex connectivity of the nodes, instead of the traditional adjacency matrix. Active power levels serve as nodal attributes, ensuring that clusters represent both the physical and operational characteristics of the network. This dual-focused approach promotes a partitioning that is topologically coherent and functionally homogeneous, vital for enhanced grid management. When applied to the IEEE 14, 39, and 118 bus systems, the proposed method has successfully delineated coherent clusters of buses, underlining its potential for improving power grid management. The simulation results confirm the method’s efficacy and applicability.

Electric vehicle (EV) charging stations have been evolving to offer better and more efficient power delivery methods. A key area of research in this field involves predicting the power consumption of EV charging stations. Many researchers have addressed this issue using machine learning and deep learning methods, however, forecasting models struggle to predict individual charging sessions with high resolutions. In this paper, a deep neural network (DNN) is constructed to forecast the EV charging current profile and state of charge (SoC). Charging current and SoC are mathematically formalized and embedded into the DNN’s loss calculation to build a physics-informed neural network (PINN). The performance of the models is assessed through analysis of their prediction error, utilizing actual data from real EV charging sessions.

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network. The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.

Droop-based control is widely used in islanded microgrids to regulate power sharing among distributed generators (DGs) according to their ratings, without the need for communication. However, simultaneous and accurate active and reactive power sharing regulation represents a key challenge for droop-based techniques. This issue arises due to the inconsistent voltage drops in feeder lines and the distinct local loads distributed across the microgrid DGs. To this end, this paper introduces a new solution for droop-based approaches to enhance power sharing accuracy in the context of local loads, without relying on any communication means between DGs. The proposed solution effectiveness is demonstrated through MATLAB/Simulink and is validated for conventional, inverse, and angle droop control.

The integration of the large language model (LLM) technology in electric vehicles (EVs) has sparked a significant leap forward in the evolution of intelligent transportation. LLM technology enables real-time, context-aware communication, thereby elevating the safety and convenience of driving experiences. LLMs play a pivotal role in refining human-vehicle interactions, offering an intuitive and responsive interface for vehicle controls and navigation systems. In addition, LLMs contribute to the sustainable development of EV technology by optimizing energy consumption patterns and supporting the integration of EVs into smart grid systems. To this end, this paper aims to review the essential elements of LLM-based EVs to emphasize their current capabilities toward smart transportation and infrastructure services. This paper explores the multifaceted contributions of LLMs in enhancing functionality, user experience, and technological development of EVs. This tutorial also addresses the challenges and future prospects of LLM applications in EVs, emphasizing their potential to transform EVs into intelligent companions on the road and pave the way for a more sustainable and user-centered future for personal transportation.

The recent surge in distributed energy resources has made voltage fluctuations more complex and unpredictable. Consequently, traditional voltage control (VC) methods such as stochastic programming and robust optimization may struggle to manage rapid and significant fluctuations. Facing this challenge, this paper proposes an efficient dueling deep Q network (Dueling DQN)-based autonomous VC method. This study formulates the VC as a Markov decision process and develops an agent that learns optimal operational strategies to maintain voltage levels within safe limits, ensuring grid stability and reliability. The proposed agent operates within the power system environment, designed to mimic real-world grid conditions, including voltage variability and load fluctuations. The Dueling DQN model processes comprehensive observations, including production levels, loads, and voltage measurements, to predict action values that ensure effective VC. The Dueling DQN architecture, training process, and operational mechanisms based on VC are thoroughly detailed. Extensive case studies performed on the modified IEEE 14-bus system and a reduced IEEE 118-bus system and conducted over numerous episodes, demonstrate that the Dueling DQN agent consistently outperforms deep Q networks derivatives and deep deterministic policy gradient approach.

The efficient and secure operation of power grids is essential for ensuring reliable electricity supply and supporting the integration of renewable energy sources. Yet, the landscape is marred by burgeoning adversarial attacks, particularly targeting power systems employing cutting-edge deep reinforcement learning (DRL) methodologies. This study proposes a proximal policy optimization (PPO) agent against a randomized adversarial opponent aiming to disrupt grid operations. The performance of the PPO agent is assessed across various power grid environments alongside several baseline agents, including the do-nothing agent, the random agent, the topology greedy agent, and the power line switch agent with adversarial training. Over multiple epochs of adversarial training, the average rewards, number of steps to resolution, and computational time are recorded. The simulation results on the IEEE 14-bus system and the reduced IEEE 118-bus system demonstrate a nuanced supremacy and applicability of the PPO algorithm compared to heuristic and randomized approaches. The main contributions of this paper include 1) Introducing an optimized PPO algorithm assessed using two IEEE bus system environments; and 2) Applying an adversarial-training-based DRL to improve the robustness of PPO alorthim’s policies in the electrical grid environment.

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage.

This paper proposes a novel sensorless control method for a simplified single-stage rectifier (SSSR) based electric vehicle (EV) charger. The proposed control strategy consists of three cascaded control loops, PI controller for DC link voltage regulation, another PI controller for grid current regulation, and a model predictive control (MPC) for inductor current regulation. Additionally, a novel state observer technique is proposed specifically for this topology, offering the advantages of simplicity, robustness, reduced number of tuned parameters, and compatibility with low-cost industrial microcontrollers. The effectiveness of the proposed control approach and observer technique is validated through extensive simulations across various scenarios and operating conditions, including boost operation, buck operation, and operation under uncertainties. The obtained results demonstrate the efficacy of the proposed method in achieving precise regulation of DC link voltage, grid current, and inductor current without the need for explicit inductor current sensor feedback.

This paper proposes a method for optimizing the charging process of electric vehicles (EVs) within the context of renewable energy integration. The proposed method continuously monitors the maximum available power from the renewable energy sources (RES), grid conditions, and EV’s requirements. Upon receiving reference charging or power signals from the connected EV, the system computes the surplus or required power and adjusts grid interaction accordingly. The proposed method offers several advantages. Firstly, it optimizes energy harvesting from RESs by dynamically adjusting grid and EV interactions based on real-time monitoring. Secondly, it enhances grid stability by intelligently controlling power flow, injecting or consuming surplus power in response to the grid condition. Thirdly, it leverages the growing number of EVs connected to the grid, effectively utilizing them as distributed energy storage systems. To validate the effectiveness and efficiency of the proposed method, extensive simulation tests are conducted under diverse scenarios.

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs.

The Controller Area Network (CAN) Bus is a commonly used communication protocol for different applications including vehicle controllers and power electronics converters which are the application focus in this paper. One drawback of CAN-bus is the lack of security measures in the protocol, which can be problematic if the communication line is exposed to attackers. This paper proposes a novel method to secure CAN-bus communication that is light-weight and applicable for power converters and microgrid applications. The proposed solution aims to protect against eavesdropping, false data injection, and replay attacks. The security scheme is implemented and deployed on a low-cost microcontroller that is used to control power sharing rectifiers. Experimental results are carried out to show that the proposed method effectively secures the channel against attacks with minimal time and space overhead.

Photovoltaic (PV) power is progressively being subsumed into power grids. Consequently, reliable PV power forecasting (PVPF) has become essential to avoid ramp events that can adversely affect the operations of integrated power systems. This article presents a deep-learning-based algorithm for PVPF. The gated recurrent units (GRU) network was implemented to predict the non-linear spatiotemporal correlations of the weather data, leading to higher reliability of the PV stations. Experimental results obtained from actual testing demonstrate the validity of the GRU networks for accurate PVPF, contributing to the efficient operation and management of smart grids and renewable energy systems. The conducted case study shows that the proposed model outperforms bidirectional long short term memory (BiLSTM) and long short term memory (LSTM) models in terms of computation power, root-mean-square error, and mean absolute error metrics.

This paper presents a model predictive control (MPC) technique for a three-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The proposed control algorithm depends on a single loop control to regulate the DC link voltage, grid currents, and to balance the voltages of the DC side capacitors. This is achieved by incorporating all controlled states into a multi-objective cost function (MOCF), which encompasses the tracking errors of AC currents, DC link voltage, and DC side capacitor voltage balance. The proposed controller is implemented and tested in MATLABI Simulink environment. Investigations are conducted to prove that the proposed MPC successfully meets the control objectives, enabling precise tracking and ability to deal with abnormal grid conditions, including uncertainties, load variations, voltage sags and swells, as well as unbalanced grid voltages.

In this paper, a novel multi-mode converter (MMC) for battery storage application is proposed. The MMC consists of a non-inverting Cuk converter to feed a load, and a bidirectional buck-boost converter to regulate the battery voltage during charging and discharging. The proposed MMC is used to control the power flow between an input source, a battery, and a load. Furthermore, an adaptive selection algorithm is proposed to enable battery charging or discharging based on the source voltage, the state of charge (SoC), and a reference SoC. The proposed converter has many advantages such as simplicity, and supporting many modes of operation. The proposed MMC is controlled using a sliding mode controller and its performance is described theoretically and validated through simulations. The final version of the paper will contain experimental results.

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine’s operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.

This paper proposes a self-tuning current-control algorithm for a three-phase three-level T-type controlled rectifier. The aim of this algorithm is to control the AC-side current without any previous knowledge about the parameters of the system. The dynamic model of the rectifier is assumed as a black box, where all internal parameters are considered unknowns. The proposed algorithm depends on Recursive Least Squares Algorithm (RLSA) to estimate the state-space model of the system. Then, the estimated model is used to derive the gains optimally for a Linear Quadratic Tracker controller (LQT). The obtained results prove the ability of the proposed algorithm to estimate the entire dynamic model accurately and to control the system with an accurate, smooth, and rapid response. In addition to zero tracking error and no overshot.

This paper proposes a method to analyze aging response variance in terms of statistical parameters and geometry specifications of medium voltage power cable. The electric stress distribution in the single-core medium voltage power cable model is logged quantitatively using finite element modeling. The dependable parameters such as electrical conductivity, thermal conductivity, ambient temperature, relative permittivity, defect position and size, act as variables in the model to analyze simulated electric stress in the power cable. The variation in electrical stress distribution under the influence of different factors impact distinctly on cable degradation over time. Moreover, the correlation between these factors is quantified using derived statistical parameters to form a correlation matrix. In this work, both the variation coefficients and mean stress intensity are utilized as response measurement variables. A multivariable linear regression is applied to derive the relationship between the model parameters and the response variables. This study shows that the stress variation is strongly correlated with electrical conductivity and thermal conductivity, while the maximum stress intensity is most impact by defect sizes and position. The implication and consequence of the rest of the model parameters on electric stress distribution are also analyzed and discussed.

Decentralized hierarchical control techniques do not require a communication layer in their secondary control level. These control techniques reduce the vulnerability of microgrids (MGs) to cyber-attacks, reduce data losses, lessen time delays, and mitigate costs for communication infrastructure. In the islanded AC MG, droop control is profusely used at the primary level to achieve accurate power-sharing. However, it may result in steady-state voltage and frequency (V/f) deviations with varying load conditions. The secondary control layer is required to restore these deviations while maintaining droop dictated power levels. The secondary layer supports V/f control at a slower time scale, while the primary droop-based layer supports V/f tracking at a faster time scale. This paper presents a review of the decentralized secondary control systems that interact with droop based primary controllers. The V/f restoration schemes by the secondary control layer are categorized as linear and non-linear technologies. The mathematical formulations for each control scheme are presented, and a tabulated summary of all the control systems. Finally, simulations are conducted for a selected control technique from each category, and the results are presented and discussed.

Quadratic cost function such as Mean Squared Error (MSE) has been a widely used objective function for training deep neural networks to develop energy forecasting models in Smart Grids. In this work, Penalizing Underestimation Logarithmic Squared Error (PULSE), a novel objective function is proposed with the aim of reducing the tendency of deep learning models to underestimate the target variable. Stacked Long Short-Term Memory (LSTM) networks are adopted on the time series load demand data to investigate the performance of the proposed cost function against the widely used MSE cost function. The evaluation is performed using open-source real-world electricity load diagrams dataset covering a period of three years. The performance of the proposed scheme is examined with deep learning models through several experiments. The results demonstrate that the proposed scheme is able to eliminate the tendency to underestimate and provides competitively accurate load demand forecasting results. The results are additionally compared against the state-of-the-art machine learning models developed in the literature. The proposed cost function maintains the RMSE around 4*10-2kWh which is also the RMSE for deep learning models with MSE cost function and delivers 25% improvement in MAPE while also eliminating the underestimation of load demand.

One of the major factors that lead to energy losses for utility distribution systems is electricity or energy theft. Energy theft is tampering with smart meter reading to reduce customer energy usage and reduce electricity bills. A thief customer tends to consume more energy and hence, the theft negatively affects the power supply quality in the form of transformer overload, voltage unbalance, and voltage drop on system buses. Meanwhile, it also causes great economic losses for the business of electric utility. In order to enable efficient energy theft detection, data-driven approaches including utilizing trained deep neural networks are proposed in this paper. The machine learning approaches can detect energy theft involving stealthy connections or meter tampering at the level of smart meters or aggregated levels. In this work, the detection effectiveness of different approaches is evaluated on real case study data at the end consumer level. The challenges of class imbalance and the missing values (around 25% of the whole fields) are addressed in the LSTM-based methodology. In this paper, results are obtained on real energy consumption data to show the higher performance of the proposed solutions compared to previously presented work.

In this paper machine learning (ML) models have been developed for the application of big data load forecasting using parallel computation. The load forecasting models' performance is directly linked to system execution capacity, memory, thread count, balancing the load, and available resources. This paper is focused on two main challenges. The first challenge is to reduce the execution time of the ML models and the second one is to choose the suitable tree-based model for effective load forecasting. The paper conducts a comprehensive evaluation of the load forecasting using real-world data on energy consumption. Comprehensive results are obtained to show that the performance of random search to tune the ML models exhibits competitive performances whilst not losing the accuracy of the models and gaining a competitive advantage on the run time.

This paper proposes a novel sensorless vector control scheme for an open-end stator winding induction motor. The motor is fed by different three-voltage inverters with isolated DC sources. The inverters are controlled using space vector Pulse Width Modulation (PWM). A reduced-order sliding mode observer with smooth function is proposed for speed-sensorless control. The simulation results show the simultaneous correct estimation of both rotor flux and speed. The proposed control scheme is built within the Simulink environment combined with the Real-Time platform. The obtained results confirm the clear efficacy of the proposed control scheme under a variety of operating conditions.

In this paper, a second order sliding mode based Super Twisting (ST) controller is designed and implemented on sensorless five-level packed U cell inverter (PUC5). The aim is to enhance the inverter performance and achieve better robustness. A low complexity modulation strategy is employed. It contains a few logic blocks and two level-shifted carriers, which reduces the computation time and eases the implementation. Moreover, it ensures quick and self-balancing of the inverter capacitor voltage. Employing the proposed super-twisting controller leads to a significant enhancement of dynamic response and steady-state performance of the PUC5. It provides better accuracy in the tracking of the reference current, which results in high power quality, and helps in fast self-balancing of the capacitor voltage. The feasibility and the effectiveness of the proposed controller has been verified by simulation and experimentally. The obtained results show the higher performance of the super twisting controller in stand-alone mode of operation.

Accurate Short-Term Load Forecasting (STLF) has been considered a topic of extreme importance for efficient energy management, reliable energy transactions, and economic operation dispatch in smart grids. However, the continuous instability of the load demand essentially due to the high volatility of weather conditions and customers' demand behavior dramatically affects the STLF accuracy. In order to overcome this problem, five effective Deep Learning (DL) techniques are proposed for multivariate time series STLF based on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and stacked Auto-Encoder (AE). These DL based techniques are consolidated to build stacked Bidirectional GRU (BiGRU), Convolutional LSTM (ConvLSTM), stacked Bidirectional LSTM-AE (BiLSTM-AE), hybrid CNN-LSTM-AE (CNN-LSTM), and LSTM-AE (LSTM-AE) techniques. Simulation studies are conducted to demonstrate the performance superiority of BiLSTM-AE compared to the other DL models. The main contributions of this paper include 1) integrating a variety of deep neural networks for STLF; 2) employing time series as a benchmark to compare between heterogeneous DL architectures; 3) conducting the analyses on real data set.

This paper presents a novel state feedback controller design for a 5-level Packed U-cell (PUC5) inverter based on its average model. The state feedback is designed using a super-twisting sliding mode control (ST-SMC) and a PWM technique. The proposed super-twisting controller (STC) is used to regulate the capacitor voltage at the desired value in order to generate a 5-level output voltage waveform while feeding a smooth current to the load with low harmonics. A simulation and experimental comparative study between the proposed control technique and the conventional SMC and proportional-integral (PI) control techniques is presented. The presented results prove the higher performance of the proposed controller in terms of dynamic performances and power quality.

The back-to-back connection of two three-to-single-phase matrix converters (MCs) through a high-frequency transformer to create the so called MC-based solid state transformer (SST) for interactive grid interfacing is proposed in this paper. The solution provides single-stage bidirectional ac-ac power conversion. There are advantages of no lifetime-limited storage capacitors, light weight, and compact volume. The conventional modulation methods of this MC-SST system require additional control design for power management. Besides, space vector modulation contains sophisticated voltage and current vectors computation and duty cycle composition, considering the two back-to-back connected three-to-single-phase MCs. In this paper, a model predictive control (MPC) is proposed for this MC-SST linking different ac grids. The proposed MPC predicts the state variables based on the discrete model of MC-SST system and the present circuit variables, and then selects an optimal switching state that ensures the smallest value of a cost function, for the next sampling time. Simulation and experimental studies are carried out to demonstrate effectiveness and simplicity of the proposed MPC for such MC-SST grid-interfacing system.

The traditional electric power grid is moving toward smart grid, and with the advent of smart grids, a lot of data is generated in high volumes, velocity and variety. This brings several challenges with real time processing to get meaningful information for enhancing the benefits of smart grid. Furthermore, the application of short-term load forecasting poses additional challenges of being highly uncertain and volatile due to different load profiles. This paper conducts a study on the demand side management and load forecasting in electric power grids with help of the historical data obtained from smart grid. Machine learning models are developed using deep learning to ensure significant improvement in forecast accuracy when compared to benchmark Auto-Regressive Integrated Moving Averages ARIMA analysis. The short-term load forecast data has been merged with weather data from Application Program Interface (API). The discussed system uses an autonomous feedback loop to consider the historical load values as features for training and testing. This paper also applies deep learning methods like pooling based recurrent neural networks which could solve the curse of dimensionality that usually exists with increasing layers in traditional neural network methods. The paper proposes an averaging regression ensembles model for short-term load forecast.

In this paper, a data-driven approach has been used to identify and categorize fault in the electrical power system. The proposed methodology involves efficient analysis of the data with feature vectors including the area or zone of the bus. The training is done on machine learning models to classify and identify the location of the fault. Three-phase, line to ground, line-to-line to ground, line-to-line, loss of line with no fault and loss of load at bus faults are simulated to generate labeled data with type of fault and location of fault. Two algorithms have been proposed to choose the measurements selection strategy, and results have been stated. The proposed methodology proves its validity for identification of the fault without necessary measurement of the voltage of each node. The proposed approach works with a minimum number of buses required to be as few as 5-7% of the measured buses. The accuracy, capabilities, and limitations of the proposed algorithm are verified on IEEE 68 bus model. The highest classification accuracy attained on one of the test cases is 91%.

In this paper, a novel and simple controller for seven-level packed U-cell (PUC) grid-connected inverter is presented. The control method is derived based on sliding mode control theory. Making use of the measured grid voltage and computed reference grid current, the reference value of the inverter output voltage is determined. According to the calculated voltage value, the suitable control set is selected (offline) in order to satisfy the reaching conditions of two separate sliding lines. The sliding lines are defined based on the grid current and the auxiliary capacitor voltage errors. Simulation results are presented to show the effectiveness of the proposed simple controller in tracking the desired values.

This paper presents a Lyapunov stability theory-based control technique for a single-phase quasi-impedance source neutral-point-clamped (qZS-NPC) inverter with LCL filter. The proposed control technique handles two control objectives, which are the grid current control and the dc-link voltage control. The dynamic and steady-state performances of the proposed control method have been demonstrated by simulations using Matlab/Simulink.

The deployment of a high-reliability Multilevel Inverter (MLI) able to operate effectively and unremittingly in case of partial failure is indispensable in high performance and high power grid-connected PV systems. Among the well-known MLI topologies, the Cascaded H-Bridge (CHB) inverter is characterized by its high modularity and fault-tolerance features due to the high flexibility in generating the output voltage levels. However, the effective operation of such topology needs a tight supervision to detect the failure and provide uninterrupted power supply. This paper proposes a novel open-circuit failure detection strategy based on a 1st-Order Dynamic Gains Robust Differentiator (DGRD) for 3-phase 9-cell CHB inverter. The proposed scheme estimates the current 1st derivative in order to easily detect the amplified faults. This technique is considered as an improved version of the sliding mode based Levant derivative approach, where a constant gain is used instead. The presented simulation results confirm the accuracy of the proposed technique in real-time fault detection (distinction between noises and faults to avoid false alarms and misdetection).

In this paper, a model predictive control (MPC) strategy for a grid-tied cascaded full-bridge inverter with neutral-point-clamped legs has been developed. Two strings of photovoltaic (PV) cells are interfaced to the grid by the inverter, while the controller ensures the operation at maximum power conditions. It is thus essential for the developed control method to be capable of independently regulating the two dc sources while balancing the dc-link voltages of the capacitors. In addition to these requirements, the proposed controller addresses the critical issue of the common-mode voltage (CMV) effects. By using the redundant switching states of the inverter topology, MPC reduces the high-frequency CMV components and consequently, the flow of the leakage currents from the PV system to earth ground. The developed control strategy is tested by conducting various experiments in an inverter prototype connected to two PV strings. Additionally, the system performance at steady-state conditions, during abrupt reference changes, and with grid voltage variations is compared with the traditional control strategy.

This paper proposes two second-order continuous-time algorithms to solve the economic power dispatch problem in smart grids. The collective aim is to minimize a sum of generation cost function subject to the power demand and individual generator constraints. First, in the framework of nonsmooth analysis and algebraic graph theory, one distributed second-order algorithm is developed and guaranteed to find an optimal solution. As a result, the power demand constraints can be kept all the time under appropriate initial condition. The second algorithm is under a centralized framework, and the optimal solution is robust in the sense that different initial power conditions do not change the convergence of the optimal solution. Finally, simulation results based on five-unit system, IEEE 30-bus system, and IEEE 300-bus system show the effectiveness and performance of the proposed continuous-time algorithms. The examples also show that the convergence rate of second-order algorithm is faster than that of first-order distributed algorithm.

Smart grids systems generate a large amount of data. Big Data which is an essential element for improving the reliability, stability, and efficiency, and in decreasing the cost of energy use. Reliable operation of smart grid depends on the utilization of various real-time information related to monitoring, communications, control and management systems. This paper gives a common understanding of how big data can impact the reliability and stability of power grid, and investigates in detail a smart grid communication network architecture. In addition, the paper explores and collates the smart energy subsystem, the smart information subsystem, and the smart communication subsystem. This conceptual lens provides deep insights into the reliability challenges and effective solutions toward reliability issues in smart grid to reveal the big data role during the transition, and how it can fuel the organic growth of smart grid.

This paper presents a comprehensive overview of power converters and their control techniques for AC microgrids. The aim is to give an insight and direction for researchers and applications on promising topologies, control, and application within future smart grid. The paper first focuses on presenting various power converter topologies used in AC microgrids. Then, suitable control techniques for grid-connected and islanded operation modes are critically reviewed and analyzed. Finally, challenges of these converter topologies and control techniques are identified along with their potential solutions.

Two control strategies, a finite-state predictive torque control and a linear control with space-vector PWM, are developed and experimentally assessed in this paper. Both strategies are used to control a permanent magnet synchronous motor supplied by a three-level neutral-point-clamped inverter. The high complexity of the drive system and the different nature of the control objectives make the development and implementation of the two strategies a challenge. In addition to the high computation resources that are required, the large number of the inverter voltage vectors further increases the computation load on the processor. This hinders the implementation into conventional hardware means and necessitates the use of advanced controller boards. Therefore, when controlling drives with multilevel inverters, the development of both control techniques requires special care. In this study, the operation of the two controllers is experimentally investigated, and their performances are assessed by considering several parameters under steady state and transient conditions.

A control strategy based on finite-state model predictive control (FS-MPC) for uniform switching transitions distribution and reduced calculation requirements is presented in this paper. FS-MPC evaluates all the available switching states of the controlled topology to find the combination that meets the control objectives. However, the number of switches, along with the computation burden, is significantly increased in multilevel inverters, whereas the switching transitions are not evenly distributed among the switches. By preselecting the evaluated switching states into the cost function of the developed MPC, it is possible to minimize the computational requirements while decreasing the difference in the switching transitions. The performance of the proposed strategy is compared with the classical predictive control and a field-oriented control with space-vector modulation. The methods are applied to an electric drive with a PMSM supplied by a neutral-point-clamped inverter, and the effectiveness of the developed switching-state strategy is validated by examining steady state and transient performances.

The power decoupling strategies of single-phase Z-source/quasi-Z-source inverter (ZSI/qZSI) to handle the inherent double-line-frequency (2ω) power are overviewed. They are categorized as passive and active power decoupling methods. The former suppresses the 2ω ripple by the impedance network, modified modulation methods, or closed-loop damping control. The latter diverts the 2ω ripple to the compensation capacitor of integrated active power filters, through properly operating a half-bridge phase leg. The pros and cons of those 2ω power decoupling methods are discussed and recommendations are given for future development of such single-phase inverters.

This paper proposes a Current Deadbeat Control (DBC) algorithm for a three-phase 9-cells qZS based Cascaded H-Bridge (CHB) inverter to ensure grid current supply with low THD. The studied topology is considered as a single-stage DC/AC topology having the capability of boosting the DC input voltage with high-quality multilevel AC voltage, independent DC-link voltage compensation, and control of the power transfer with high reliability. Moreover, a Repetitive Controller (RC) is connected to the inductor current loop with the aim to reject periodic disturbances (double line-frequency fluctuations). The employed RC has the capability of learning through iterations based on tracking error. During the control, the compensation method uses the error between the DC-link peak voltage reference and the actual value to produce the compensating inductor current reference. Theoretical analysis and simulation results shows that the proposed solution achieves high dynamic performance and reduced cyclical disturbances effects.

Solar systems have become very competitive solutions for residential, commercial, and industrial applications for both standalone and grid connected operations. This paper presents an overview of the current status and future perspectives of solar energy (mainly photovoltaic) technology and the required conversion systems. The focus in the paper is put on the current technology, installations challenges, and future expectations. Various aspects related to the global solar market, the photovoltaic (PV) modules cost and technology, and the power electronics converter systems are addressed. Research trends and recommendations for each of the PV system sectors are also discussed.

This paper considers the problem of designing adaptive learning algorithms to seek the Nash equilibrium (NE) of the constrained energy trading game among individually strategic players with incomplete information. In this game, each player uses the learning automaton scheme to generate the action probability distribution based on his/her private information for maximizing his own averaged utility. It is shown that if one of admissible mixed-strategies converges to the NE with probability one, then the averaged utility and trading quantity almost surely converge to their expected ones, respectively. For the given discontinuous pricing function, the utility function has already been proved to be upper semicontinuous and payoff secure which guarantee the existence of the mixed-strategy NE. By the strict diagonal concavity of the regularized Lagrange function, the uniqueness of NE is also guaranteed. Finally, an adaptive learning algorithm is provided to generate the strategy probability distribution for seeking the mixed-strategy NE.

Big Data is an essential element for energy management and control decision toward improved energy security, efficiency, and decreasing costs of energy use. Power distribution network is required to deliver electric energy reliability with reduced complexity and to be part of future smart grid. Therefore, in this paper Big Data related to the distribution generation systems will be discussed and illustrated within the context of smart grid principle. The paper work is to study the impact of adopting big data on energy management systems and to show the importance of the big data in strategic decision-making. The paper will highlight the Big Data issues and challenges associated with it in the energy management and control decisions in power distribution networks.

This paper presents a model predictive control (MPC) of five-level H-bridge neutral-point-clamped (NPC) quasi-impedance source inverter (qZSI). The proposed control technique is designed to handle three control objectives with simple and effective approach. The output current, the input current and the capacitor voltage are the control objectives of the proposed MPC algorithm. To fulfill these control objectives, multi-objective based cost function is employed. Furthermore, qZSI has been combined with the 5-Level H-bridge NPC inverter so as to obtain power converter topology with buck/boost and dc/ac conversion functionality in a single stage for high power PV applications. Simulation results verify the proposed multi-objective MPC algorithm and inverter topology.

This paper presents a grid-connected five-level neutral-point-clamped (NPC)/H-Bridge quasi-impedance source inverter (qZSI) and its control scheme for use in PV applications. The proposed topology is combination of a three-phase qZS network and NPC/H-Bridge inverter that is suitable for high power applications. The proposed inverter transfers dc power into the ac power without dc/dc boost converter. To control the proposed topology, PI based control scheme is developed. A modified level shifted sinusoidal pulse width modulation (LS-SPWM) technique is employed to generate gate signals for inverter. The effective and simple control algorithm is also employed to regulate the inverter output current. The performance of the proposed system has been validated by simulation results. These results show excellent performance in terms of steady-state and dynamic response.

In this paper, the control of a single-phase cascaded H-bridge neutral-point clamped inverter is investigated. The grid-tied inverter is employed to interface Photovoltaic (PV) panels to the utility grid. A MPC and a classical controller are presented and their effectiveness is illustrated by being compared in terms of several quantitative and qualitative performance indices. The computation burden of the microprocessor, dynamic response and THD compliance with the international standards are some of the considered quantitative characteristics. On the other hand, ease of control development, adaptability to different operating conditions, neutral-point potential balancing along with independent control of each inverter cell are some of the qualitative indices to be investigated. The performances of both controllers are validated by extracting power from two PV strings.

An active power filter (APF) integrated quasi-Z-source cascaded multilevel inverter (qZS-CMI) is proposed in this paper. The purpose of incorporating the APF is to independently divert the second-order harmonic (2ω) power from dc side to APF's capacitor, for each of the cascaded single-phase quasi-Z-source inverter (qZSI) modules. The APF capacitor allows highly fluctuated ac voltage and current, with a small capacitance. The each module's qZS inductors and capacitors only handle the switching-frequency voltage and current ripple. Thus, the impedance values are as small as a three-phase qZSI, avoiding bulky inductors and capacitors in high-power applications. Operating principle and 2ω power buffering ability of the APF integrated qZS-CMI are analyzed. Design of the APF circuit parameters is illustrated. Simulation results demonstrate the validity of the proposed approach.

This paper presents a hybrid method of active power and reactive power control strategy with Quasi Z-source inverter (QZSI) in single phase grid connected photovoltaic (PV) systems. The maximum power from the PV panel is extracted and most of it is supplied as active power to the grid. The major portion of required reactive power exchange to the grid is done by means of thyristor switched capacitor (TSC) and thyristor switched reactor (TSR) to improve the generation capacity of the PV system. In the proposed topology inverter shares the minimum reactive power and large change in reactive power will be delivered/absorbed by the TSC-TSR to get smoother operation in reactive power control. The DC voltage controller and the AC current controller is discussed in brief. The mathematical analysis for grid synchronization method is presented in detail. The control algorithm for active power and reactive power is analyzed and discussed in the paper. The simulation results are shown for validating the proposed concept.

Grid-connected energy storage systems have been implemented in ac power systems as uninterruptable power supplies (UPS). Batteries and bi-directional power converters provide electrical power when off-grid and recharge when grid-connected. In this paper, a packed U cells (PUC) seven-level inverter has been selected as the grid-interface due to the lower cost and fewer number of components compared to other bi-directional topologies. Additionally, the PUC has higher power quality when compared to the traditional H-bridge. Compared to the traditional PI controller, Model Predictive Control (MPC) is attracting more interest due to its good dynamic response and high accuracy of reference tracking. Through the minimization of a user-defined cost function, the proposed MPC technique can simultaneously achieve unity power factor, low total harmonics distortion of the grid-side current and balance the PUC capacitor's voltages at the grid side, and control bi-directional power flow in the batteries-PUC system. The presented topology and proposed control technique are verified by simulating a 600 W reduced-scale prototype. The theoretical principles are validated by implementing the controller on the prototype using dSPACE 1007 platform.

The single-phase quasi-Z source inverter (qZSI) topology has recently attracted attention for single-phase grid-tie photovoltaic (PV) applications. However, due to the inherent second-harmonic power flow in single-phase systems, a large qZS network is required to reduce the second-harmonic component of currents and voltages on the dc side. Minimization of the qZS network remains an open issue. This paper proposes a technique that minimizes the qZS capacitance and inductance of the single-phase qZSI topology by employing dc-side low-frequency current ripple damping control. Through analysis of power flow, a second-harmonic power model is derived and the ripple power is analyzed for minimization of the qZS network. A current ripple damping control is proposed to ensure suppression of second-harmonic power flow through the inductors. Simulation and experimental results verify the theoretical analysis, damping control, and the proposed design minimization of the qZS network for the single-phase topology.

A hybrid pulsewidth modulated single-phase quasi-Z-source grid-tie photovoltaic (PV) power system is proposed. The hybrid pulse-width modulation (HPWM) combines the pulse-width modulation (PWM) and the pulse-amplitude modulation (PAM). The PWM works when the ac output voltage is lower than the dc source voltage; otherwise, the PAM operates the single-phase quasi-Z-source inverter (qZSI). The HPWM leads to the reduction of power loss, and the quasi-Z-source capacitance and inductance. An effective control strategy is proposed for the new PV power system to manage the maximum power point tracking (MPPT) of PV panel, grid-tie power injection, and dc-link voltage. A grid-tie current controller, combining a repetitive controller and a proportional-resonant regulator, achieves strong harmonic suppression, fast dynamic, and zero tracking error. A 500-W prototype is built to verify the new system. Power loss estimation and impedance design are detailed. Experimental tests validate the HPWM, new PV power system with higher efficiency, and the related control method.

The active power filter (APF) that consists of a half-bridge leg and an ac capacitor is integrated in the single-phase quasi-Z-source inverter (qZSI) in this paper to avoid the second harmonic power flowing into the dc side. The capacitor of APF buffers the second harmonic power of the load, and the ac capacitor allows highly pulsating ac voltage, so that the capacitances of both dc and ac sides can be small. A model predictive direct power control (DPC) is further proposed to achieve the purpose of this new topology through predicting the capacitor voltage of APF at each sampling period and ensuring the APF power to track the second harmonic power of single-phase qZSI. Simulation and experimental results verify the model predictive DPC for the APF-integrated single-phase qZSI.

In this paper, discontinuous space vector pulse width modulation for a five-phase quasi Z-source inverter is presented. Two schemes are introduced for the continuous SVM of five-phase qZSI (SVQ1 & SVQ2) and two schemes of discontinuous SVM (DSVQ1 & DSVQ2) are proposed SVQ1 method is based on subtracting the shoot-through state interval from the zero state interval only. While SVQ2 subtract the shoot-through state interval from all active and zero states intervals. The discontinuous SVQ schemes are based on eliminating one of the zero vectors which will lead to 20% reduction in number of switchings. These schemes are simulated using Matlab/Simulink. An experimental setup is developed to validate and test the proposed schemes.

This paper presents a model predictive control (MPC) scheme for quasi-Z source (qZS) three-phase four-leg inverters. In order to cope with drawbacks of the traditional voltage source inverters (VSI)s, the qZS three-phase four-leg inverter topology is proposed in this study. To improve control capability of the controller, the MPC scheme is used. The proposed MPC is based mainly on the discrete-time model of the whole system. The proposed controller handles each phase current independently. As a result of this, the proposed qZS four-leg inverter has fault tolerant capability, for example if one leg fails the others can work normally. Simulation studies were performed to verify the steady-state and transient-state performances of the proposed control strategy under balanced/unbalanced reference currents and load conditions.

Quazi Z-Source inverter (qZSI) is a promising inverter topology that can buck or boost input voltage without a DC-DC converter and hence can be used in transformerless configuration. This is a high efficiency solution because of single stage processing and conversion. In this paper a novel modified qZSI topology is proposed and new modulation technique is presented to reduce common mode leakage current that is generally present in non-isolated transformers feeding utility grid. The proposed inverter offers a, high efficiency and a high reliability solution in a single-phase solar photovoltaic system for grid integration. Presented topology, (1) uses phase-leg shoot through for boosting the DC voltage to the required level which eliminates the additional DC-DC converter, (2) eliminates the PWM dead-time and provides freewheeling through additional switches (smoothens output AC grid current), (3) minimizes leakage current, (4) avoids body diode conduction for main devices increasing the efficiency of the system because body diode have poor reverse recovery characteristics, (5) effectively utilizes magnetic components, (6) common mode leakage current will be reduced to 0.2% of the rated current which is negligibly small. Simulation and experimental results are verified for a 120V (peak), 15A (peak) at 0.99pf.

Due to variability of solar energy resources, maximum power point tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point (MPP) and maximize the energy harvest. Many standards are developed to ensure the safe and efficient power generation under dynamic weather conditions. This paper presents a high efficiency fixed-step model predictive control (MPC) technique to employ the MPPT for photovoltaic applications. The MPP operating point is determined by using perturb and observe (P&O) technique. The proposed fixed-step predictive model based MPPT presents significant advantages in dynamic response and power ripple at steady state. A characteristic of MPC is the use of system models for selecting optimal actuations, thus evaluating the effect of model parameter mismatch on control effectiveness is of interest. In this paper, the load model is eliminated from the proposed MPC formulation by using an observer-based technique. The performance of the proposed observer-based MPC-MPPT is evaluated on the basis of European Efficiency Test, EN 50530 that assesses the performance of PV systems under dynamic environment conditions. The proposed MPC-MPPT technique for a flyback converter is implemented using dSPACE DS1007.

Perfectly balanced supply voltages are not possible in practice. Therefore, detection, discrimination and diagnosis of stator winding turn fault in the presence of unbalanced supply voltages for three-phase induction motors is needed. In this paper a novel approach is presented for stator winding turn incipient faults detection in the presence of different levels of voltage unbalance and at different load conditions. The proposed method investigates and utilizes the ratio between third harmonic and fundamental voltage and current waveform. Fast Fourier Transform (FFT) magnitude components of the stator currents and voltages are utilized for detection and estimation of different insulation failure percentages in the presence of unbalanced supply voltages. The method uses artificial neural networks (ANN) and is tested through simulation and experimental investigations. The proposed approach presents a high degree of accuracy in detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages condition. The method discriminates between the effect of incipient stator winding turn fault and those due to unbalanced supply voltage. In addition, the proposed approach gives a more significant and reliable indicator for detection and diagnosis of stator winding turn faults in the presence of unbalanced supply voltages conditions.

Direct matrix Converter is considered as a powerful tool for AC/AC power conversion providing AC output voltage and frequency control It also has many features such as a bidirectional power flow, a compact size and a direct conversion capability In this paper, matrix converter is used to convert a five phase input voltage into three phase output voltage with controlling output current amplitude and frequency. Five-to-three phase matrix converter is used to control a three phase induction motor. Using a five-to-three phase matrix converter enables using multiphase generation units and at the same times there is no need to change existing three phase loads. Predictive Torque Control (PTC) algorithm is used to control the induction motor fed by a matrix converter. The performance of the proposed speed control system is verified by a MATLAB simulation of a 4 kW induction motor fed by a five-to-three phase matrix The simulation results during different operation modes verify the validity of the proposed closed loop speed control method.

This paper presents a novel seven-phase open-end winding drive system supplied by the dual non-square matrix converter. The input to each of the matrix converter is three phase utility grid system and the output is seven phase voltages with variable voltage and frequency. The two matrix converters feeding a seven-phase load are supplied from a common single three-phase utility source of 50 Hz. Space vector based PWM algorithm is developed to control the dual three to seven-phase matrix converter. The proposed control technique eliminates the common-mode voltage across the machine winding and hence no zero sequence currents will be produced and therefore no isolated supply is required for the dual three-to-seven phase matrix converter system. Further the proposed technique will enhance the seven phase machine phase voltage to 1.95 times the voltage produced by a single three to seven phase matrix converter system in the linear modulation range. The paper presents the analytical approach to obtain the expression of modulating signals that are used to generate the switching pulses for the matrix converter. Simulation results are presented to support the idea of the proposed modulation scheme.

This paper presents a novel topology for a dual three to n-phase direct matrix converter. The term "n" can be used for any number of output phases. The dual matrix converter topology is applied for open-end winding drive system in order to enhance output voltage and simultaneously eliminate common-mode voltage at the machine terminals. The input to each of the matrix converter is three phase utility grid system, whereas the output can be configured to any number of phases with variable voltage and frequency. Generalized carrier based pulse width modulation (PWM) technique is developed to control the dual matrix converters. A dual three to nine-phase matrix converter is utilized for discussion and analysis. The two matrix converters feeding the nine-phase open-end load are supplied from a common single three-phase utility source of 50 Hz. A simple R-L load is considered in the paper. The paper presents an analytical approach to obtain the expression of modulating signals that are used to generate switching pulses for the matrix converter. Simulation and experimental results are presented to support the idea of the proposed modulation scheme.

This paper presents a model predictive control technique for standalone doubly fed induction generators (DFIGs) without using position sensor. The technique uses a discrete-time model of the system to predict the future value of the rotor current for all possible voltage vectors generated by the rotor side converter (RSC). In this study, due to computational simplicity, the absolute error is selected as a quality function. Also, rotor position phase locked loop (RP-PLL) algorithm is used to achieve sensorless operation. The proposed MPC with RP-PLL sensorless algorithm is designed and simulated in Matlab &Simulink. Simulation results, including constant speed and load changes, and also variable rotor speed and constant load, are presented. The simulation results have proven excellent performance of the proposed MPC with RP-PLL sensorless algorithm, both of load and speed changes conditions.

This paper presents a single-phase zero-voltage switching (ZVS) AC-link inverter for PV-grid connection at maximum power operation. The PV modules charge an AC-link until it reaches a certain reference current waveform; while, in the same time, the PV modules is connected to the grid inverter via a series inductor. Then the PV modules are disconnected and the charged AC-link discharges into the grid. The proposed controller employs a conventional MPPT to maximize the PV output power. Then the controller generates the link's reference current to fulfill both of MPPT and the desired active/reactive power needs to be injected into the grid. The proposed topology along with the proposed controlling strategy achieves a flexible and continuous power flow features. A detailed descriptive figure of the inverter's operating modes and their switching strategy is presented in this manuscript. Furthermore, a simulation model is developed in MATLAB/Simulink environment for the overall system and the results are addressed. Results show that the proposed AC-link inverter successfully achieves PV-grid integration at good dynamic and steady-state performances.

This work proposes a new control scheme for electrical drives system, named cascaded predictive speed control (PSC). The strategy seeks to maintain the simplicity of the classic predictive control while excluding linear or other controllers. The control strategy has a cascade architecture, similar to the techniques of classical control (FOC or DTC). The outer loop controls the speed of the machine, determining a reference torque through a mechanical dynamic model, which allows tracking the speed reference. The inner loop controls the stator current with a cost function that selects the state of the converter which generates the best tracking references for the stator current synchronous components. Preliminary simulation results confirm the effectiveness of this approach, which produces produces a high quality drive control.

Motor drive system is considered the most important asset in industrial applications. Detection of broken rotor bars has long been important but difficult job in detection area of incipient motor faults. The need for highly efficient motor control drive systems becomes more and more important. Motors are controlled in closed-loop or open-loop modes of operation. This paper develops a novel approach for fault-detection scheme of broken rotor bar faults for three-phase induction motor using stator current signal. The empirical mode decomposition (EMD) combined with Wigner-Ville distribution (WVD) has been employed for the analysis of stator current signal. Artificial neural network is then used for pattern recognition of broken rotor bar signature. The proposed algorithm offers high performance in detecting broken rotor bar fault. Both simulation and experimental results show that stator current-based monitoring in conjunction with Winger-Ville distribution based on EMD yields a reliable indicator for detection and diagnosis of broken rotor bar faults using artificial neural network. All simulations in this paper are conducted using finite element analysis software. Experimental results validate the simulation and analytical results.

This paper presents a reactive power compensation technique using model predictive control (MPC) of a matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). Although ubiquitous in power electronic converters, e-caps have well-known failure modes and wear-out mechanisms. Therefore, the capacitors used to store energy in a voltage-sourced inverter (VSI) reactive power compensator require continuous monitoring and periodic replacement, both of which significantly increase the cost of the traditional load compensation technique. MPC of the matrix converter provides reactive power compensation by controlling the input reactive power and the output current to the inductive storage elements. Thus, compared to VSI techniques, the proposed reactive power compensation technique is more reliable and has a longer expected service life that is not limited by failure and wear-out modes of capacitors.

An effective control method, including system-level control and pulsewidth modulation for quasi-Z-source cascade multilevel inverter (qZS-CMI) based grid-tie photovoltaic (PV) power system is proposed. The system-level control achieves the grid-tie current injection, independent maximum power point tracking (MPPT) for separate PV panels, and dc-link voltage balance for all quasi-Z-source H-bridge inverter (qZS-HBI) modules. The complete design process is disclosed. A multilevel space vector modulation (SVM) for the single-phase qZS-CMI is proposed to fulfill the synthetization of the step-like voltage waveforms. Simulation and experiment based on a seven-level prototype are carried out to validate the proposed methods.

This paper proposes space vector pulse width modulation (SVPWM) technique for a direct matrix converter with five-phase input and three-phase output. This topology of matrix converter is developed exclusively for feeding three-phase stiff grid system with five-phase variable input supply. One of the presumed applications is wind electric energy generation system. The paper presents the complete space vector model of the five-to-three-phase matrix converter topology. The major breakthrough of the proposed control scheme is the enhanced input to output voltage transfer ratio. The maximum output phase voltage can go up to 104.4% of the input phase voltage in the linear modulation range and hence can act as a boost converter. The space vector model yields 215total switching combinations, which reduce to 125 states (for SVPWM implementation) considering the imposed constraints, out of which 120 are active and 5 are zero vectors. However, for SVPWM implementation only 30 active and 5 zero vectors can be used. A generalized formula for maximum modulation index for n-phase input and 3-phase output is also formulated. The SVPWM algorithm is presented in the paper, and the viability of the proposed solution is proved using analytical, simulation and real-time approach.

This paper proposes the space vector pulse width modulation technique of a matrix converter topology with three phase fixed grid input and seven phase variable voltage variable frequency output. Voltage source inverter is generally used for feeding multi-phase drives. Multi-phase matrix converter is proposed in this paper as a new solution for feeding multi phase system. The paper develops the complete space vector algorithm of the proposed multi-phase matrix converter topology. The developed space vector model suggests 221possible switching combination vectors. After imposing mathematical constraints the number of switching combinations reduce to 2184 active and three zero vectors. The complete space vector PWM algorithm is developed in the paper. The feasibility of the proposed algorithm is proved using analytical, simulation and real-time approach.

This paper presents a dual-mode controller to improve the performance of maximum power point tracking in grid-connected photovoltaic inverters. Most of grid-connected PV applications require a controllable active/reactive power generation. The proposed method employs both of the load angle and the inverter modulation index into two control modes: Active power mode, and reactive power mode. Such technique enables an efficient and flexible active/reactive power control beside MPPT function using a single-stage VSI. A simulation model is developed for the overall system to investigate the performance of the proposed dual-mode controller. Furthermore, the controller is designed and implemented using a digital signal processor (DSP). Algorithms are configured on a fixed point DSP TMS320F2812. Moreover, the controller is simulated in MATLAB/Simulink environment and is experimentally investigated using a breadboard built especially for the case study. The results show good dynamic and steady-state performances.

Large-scale grid-connected photovoltaic (PV) energy conversion systems operate at low voltage and are interfaced to medium-voltage and high-voltage ac utility grids through one or two step-up voltage transformer stages. In addition, the power conversion is performed with either a single stage dc-ac converter (central inverter) or a two stage dc-dc/dc-ac (string or multi-string inverter). However, prime solar irradiation regions in the world are not always located close to available utility lines, and in some cases are far away from main consumption areas. Furthermore, long overhead transmissions lines (>400km) and underwater transmission lines above 70kM, HVDC has become the most cost-effective solution. Among HVDC technologies, voltage source converter based HVDC system, mainly based on the modular multilevel converter (MMC), have become popular due to smaller filters, multi-network connection and decoupling of active and reactive power. This paper explores a new large-scale PV plant configuration based on a dc-dc stage interfaced directly to an MMC based HVDC system. Since PV systems are dc by nature, the proposed solution has several advantages, particularly if combined directly with the HVDC power station. Some power circuit topologies are presented, including their corresponding control schemes. Simulation results are presented to provide a preliminary evaluation on the operation and performance of the proposed system.

In this paper, a predictive torque control (PTC) algorithm is introduced for controlling an induction motor (IM) fed by a bidirectional quasi Z-source inverter (BQZSI). A torque, stator flux and capacitor voltage magnitudes control algorithm evaluates a cost function, based on a simple discrete models of the IM and the BQZSI, for each BQZSI available switching state. The voltage vector with the lowest torque, a stator flux and capacitor voltage magnitude errors is selected to be applied in the next sampling interval. The proposed PTC algorithm, with its single structure, can control both sides of the BQZSI to optimize the motor performance with an extremely simple and versatile control algorithm and without any dynamic response limitation caused by the cascaded control structure. A high degree of flexibility is obtained with the proposed control technique due its online optimization algorithm. This paper proposed a new adjustable speed drive system based on a three-phase BQZSI feeding an induction motor. Where, the interesting advantages of the PTC algorithm are combined by the advantages of one of the most interesting power electronics converters, the BQZSI. Simulation results for a 4 kW IM are presented to validate the new proposed electrical drive system.

This paper proposes a new three-phase ac-ac converter topology based on the Z-source concept and the conventional direct matrix converter (DMC), it is called quasi-Z-Source direct matrix converter (QZSDMC). It could provide buck-boost function and make the role of frequency changer. Compared with the traditional ac-dc-ac converter, it uses fewer devices and realizes direct ac-ac power conversion, so as to have higher efficiency and better circuit characteristics. Compared with the traditional direct matrix converter, it provides wider voltage regulation range and improves input-output voltage transformation ratio. The proposed converter is tested as a grid connected converter for interfacing renewable energy sources. The circuit topology, operating principle, control method and simulation results are given to verify the feasibility of the proposed converter.

This paper proposes a new four-quadrant adjustable speed drive (ASD) system based on an induction motor fed by a three-phase quasi-Z-source direct matrix converter (QZSDMC). The quasi-Z-source (QZS) network is used to overcome the limitation of the voltage gain of the traditional direct matrix converter (DMC). The QZSDMC can provide a buck-boost operation by controlling the shoot-through (ST) duty ratio. In addition, this paper presents a four-quadrant speed control method based on vector control technique which is able to control the motor speed from zero to the rated speed with the full load torque during motoring and regenerating operation modes. The simulation results validate the proposed QZSDMC based adjustable speed drive system. The proposed ASD system overcomes the voltage gain limitation of the traditional direct matrix converter with improved efficiency and higher readability compared to the traditional back-to-back converter configuration.

This paper proposes a novel three-to-five phase bi-directional partial resonant AC Link converter. The converter has three phase input with five phase output operating at variable voltage and variable frequency. The proposed topology uses two bi-directional switches for each leg of the converter resulting in a total of 16 switches. The high frequency charging and discharging of the link inductor is the main criteria for power transfer from input to output. In each cycle a small interval is allocated for resonances which enables the switches for soft zero voltage turn on and turn off. The converter has the ability for buck and boost operation. This paper proposes the basic control algorithm by considering only the large vectors of this converter topology. Simulation and real-time results are shown for the verification of the proposed converter.

This paper proposes technique to obtain ten-phase output from three-phase input supply system using special and novel transformer connection. With the proposed supply system, pure ten-phase sine-wave voltage/current is obtained that can be used for the motor testing purposes and obtaining dual five-phase supply. The proposed transformer connection yield ten-phase output while the input is grid supplied three phases. Another application area of such transformer can be ten-phase power transmission and rectifier system. In this work an open ended five-phase induction motor is run on the sinusoidal output of a three-to-ten-phase converting transformer. Also two five-phase induction motors with single sided connection are supplied from the proposed transformer connection. Analytical results are validated using simulation and finally by experimental approach.

Model predictive control has emerged as a very powerful method for controlling of electrical energy. One of the major advantages for this control is the performance of the power converters become more better comparing with the traditional modulation control techniques. In this paper, a model predictive control is used to drive a three phase grid connected Quasi-Z-Source Inverter (qZSI) to improve the performance of the three phase injected output current. This technique uses a model of the qZSI and capacitor voltage, input inductor current and AC three phase output load currents to predict the behavior of the measured parameters. The resulting closed-loop system achieves high dynamic performance for all controlled parameters. The total system has been analyzed, simulated by using MATLAB/SIMULINK program then implement by using dSPACE 1103 to prove the idea.

This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.

This paper proposes a new closed loop speed control of an induction motor fed by a bidirectional quasi Z-source inverter (BqZSI), the speed control is based on the indirect field oriented control (IFOC) strategy. The IFOC is implemented based on a voltage pulse width modulation (PWM) with voltage decoupling compensation to insert the shoot-through (ST) state within the switching signals. A dual loop controller is designed, based on a third order small signal model, to control the BqZSI capacitor voltage. The proposed speed control method, with reduced DC input voltage compared with the standard adjustable speed drives (ASD) using voltage source inverter (VSI), are able to change the motor speed from zero to the rated speed with the rated load torque. The performance of the proposed speed control methods is verified by MATLAB simulation of a 15 kW induction motor fed by a BqZSI. The simulation results during different operation modes verify the validity of the proposed closed loop speed control method.

As the development of Z-source / quasi-Z-source inverter (ZSI/qZSI), it appears several modified Space Vector Modulations (SVMs) for controlling them. Here, three typical SVMs are investigated and another extended one is also presented. The inductor current ripple, capacitor voltage gain, efficiency, and other performances of these control schemes are analyzed respectively. Simulation and experimental results validate the simplicity, reliability and efficiency of these SVM techniques for ZSI/qZSI, and the distinguished characteristics are reflected as well.

This paper proposes a three-phase quasi-Z-source matrix converter based induction motor adjustable-speed drives. The quasi-Z-source unique impedance network is utilized to overcome the limitation of the voltage gain of traditional matrix converter. The quasi-Z-source matrix converter can provide buck-boost function by controlling shoot-through duty ratio and modulation index. Analysis base on state-space equation shows the relationship among the shoot-through duty ratio and boost factor as well as voltage gain. Also, the proposed speed control method is able to change the motor speed from zero to the rated speed with the full load torque. The operating principle and system analysis are presented in detail. The simulation results validate the proposed quasi-Z-source matrix converter based adjustable speed drives.

This paper proposes a modification in the maximum power point tracking (MPPT) by using model predictive control (MPC). The modification scheme of the MPPT control is based on the perturb and observe algorithm (P&O). This modified control is implemented on the dc-dc multilevel boost converter (MLBC) to increase the response of the controller to extract the maximum power from the photovoltaic (PV) module and to boost a small dc voltage of it. The total system consisting of a PV model, a MLBC and the modified MPPT has been analyzed and then simulated with changing the solar radiation and the temperature. The proposed control scheme is implemented under program MATLAB/SIMULINK and the obtained results are validated with real time simulation using dSPACE 1103 ControlDesk. The real time simulation results have been provided for principle validation.

This paper presents a current control scheme with instantaneous reactive power minimization for an indirect matrix converter. The strategy uses the commutation state of the converter in the subsequent sampling time according to an optimization algorithm given by a simple cost function and the discrete system model. Using this strategy, harmonics in the input current generated by the resonance of the input filter are strongly reduced. Simulation and experimental results with a laboratory prototype are provided in order to validate the control scheme, and the effects of a distorted source voltage and filter resonance are analyzed.