Converter-interfaced renewable energy system (CiRES) must operate reliably, yet dynamic and uncertain conditions, including external disturbances, circuit parameter variations, nonlinear loads, security threats, etc., often introduce strong nonlinearity and intermittency. Artificial neural networks (ANNs) are increasingly adopted to address these challenges and are widely applied in CiRES. This paper presents a structured overview of ANN applications across multiple domains in CiRES, including adaptive control, anomaly detection, energy and battery management, impedance identification, maximum power point tracking and power quality improvement. By organizing these applications in a modular CiRES framework, this review highlights cross-module interactions and points to opportunities for intelligent coordination and resilient system design under practical sensing and computational constraints.

Conventional metro security screening systems rely on isolated X-ray devices and human operators, resulting in coordination delays, inconsistent detection performance, and limited throughput under high passenger flow. To address these limitations, this paper proposes an edge-cloud collaborative AIoT framework that integrates X-ray inspection, robot-side visual perception, safety-score-based fusion, CTAM-based task scheduling, and humanoid intervention into a closed-loop perception–decision–execution workflow. For robust carried-item detection in crowded and occluded checkpoint scenarios, an Object-Aware Enhanced (OAE) framework is developed, in which Keypoint-Guided Attention (KGA) uses human pose keypoints to enhance passenger–object interaction features, and a Carrying-State Classifier (CSC) refines physically inconsistent detections based on human–object carrying relationships. A safety-score-based multimodal fusion strategy further combines X-ray density cues and robot-side visual confidence into a unified safety-screening score for re-check and warning decisions. In addition, a Collaborative Task Allocation Model (CTAM) is formulated to coordinate speech, gesture, locomotion, warning-light, and conveyor-control resources under concurrent security events. Experiments conducted in a controlled real-world metro-checkpoint setting show that the proposed OAE model achieves 46.9% AP, 69.4% AP50, and 60.1% AR. The fusion strategy achieves an AUC of 0.92. Under the tested controlled metro-checkpoint scenarios, the complete system reduces the average processing time by 46.1% and improves the estimated hourly throughput by 85.5% compared with manual screening.

Extreme weather events, such as heatwaves, cold snaps, and storms, frequently cause sudden and unpredictable shifts in electricity consumption, significantly complicating accurate load forecasting. Existing forecasting methods, predominantly offline-trained deep learning models, struggle to rapidly adapt to these abrupt changes due to limitations in real-time processing and the issue of catastrophic forgetting, and they rarely capture the uncertainties inherent in load predictions under extreme weather conditions. To overcome these challenges, this study proposes a novel complementary online learning network (COLNet) explicitly designed for probabilistic load forecasting during extreme weather events. The key innovations of COLNet include the following: first, a fast adaptation mechanism to rapidly assimilate new load patterns; second, an associative memory module to preserve historical load information and mitigate catastrophic forgetting; finally, a weather-aware gating mechanism that dynamically incorporates real-time meteorological variables, enhancing the model's sensitivity and forecasting robustness. Extensive comparative evaluations using real-world hourly datasets from the 2022 Australian floods, covering three affected regions over about 14 months, and from the 2021 Texas cold snap, comprising statewide load over about 24 months with a test set covering the mid-February event, are conducted. These evaluations confirm that COLNet substantially outperforms state-of-the-art methods, achieving mean absolute percentage error of 3.205%, 2.457%, and 4.882% during the flood period and 0.84% on the Texas event, corresponding to an average reduction of about 21% in point error relative to the strongest baselines and about 19% in probabilistic error, thereby improving both accuracy and uncertainty quantifications.

Topology changes in a distribution network are common due to planned reconfigurations and unintentional switching events during practical operations. Topology changes make it challenging for existing optimization- and learning-based distributed system state estimation methods to maintain accuracy. This difficulty arises from the lack of accurate structural information for the new topology and the absence of labeled data (recorded state variables) for model retraining. To this end, this article proposes an unsupervised-on-target learning-based state estimation method for the distribution network after topology changes without relying on the topology information and labeled data. In particular, a bipartite graph learning (BGL) method with rank constraints is first designed to learn the representation of each topology with a restricted set of measurements. Then, the Euclidean distance is employed to select the best-matched source domain historical topology according to the representation learned by the BGL. To extract invariant causal structures across the two topologies, a causality-inspired sparse structure learning for domain adaptation network is further designed. It relaxes the correlations between the selected historical and new topologies into an associative structure, represented by attention scores derived from the proposed inter- and intravariable attention networks. This allows the leverage of the causality to enhance the state estimation performance of the distribution network after topology changes without relying on accurate topology information and recorded labels used for training. The comparison results on two standard IEEE test systems validate the efficacy of the proposed method.

Islanded microgrid formation (IMF) enhances active distribution network resilience but is challenging for current mathematical optimization methods due to its mixed-integer nonlinear nature. Deep reinforcement learning (DRL) also struggles with complex IMF problems due to action space explosion and environmental nonstationarity. This article proposes a multiagent DRL (MADRL) method for IMF, incorporating probability space optimization and random sequential updating to address IMF problems with numerous switches. Specifically, a multiagent framework models each controllable switch as an independent agent, and a specific actor-critic network architecture is designed for discrete control problems. To achieve unbiased policy gradient estimation, a probability space optimization loss function is devised to replace the Gumbel-Softmax-based gradient estimation in existing action-aware discrete DRL algorithms. Combined with the random sequential updating mechanism, the environmental nonstationarity issue faced by each agent is effectively mitigated. This results in a discrete MADRL-based IMF strategy with high computational efficiency and stable convergence, even when numerous switches are involved. Case studies on a modified IEEE 123-node system demonstrate that this method achieves optimality ratios of 96.85% and 97.41% in 16-switch and 23-switch scenarios, respectively.

The control of heat–electricity-integrated multiple microgrid (MMG) systems is greatly challenged by anomalous measurements and inaccurate physical electricity and heat network models. Through the systematic integration of graph surrogate models, trajectory history information, and confederate image (CI) technology based distributed multiagent deep reinforcement learning (MADRL), we propose a robust coordinated control approach for the optimization of MMG systems. Each MG in the MMG system is first represented as a graph with tree topology that is processed by a graph neural network (GNN)-based module to produce robust representations of the measurements. Subsequently, the GNN-based module produces information that is fed into a fully connected layers module to model realistic power and thermal flow using historical data in a supervised manner, thereby forming the graph surrogate models. Before the MADRL training, the GNN-based module from trained surrogate models is embedded in the policy network of MADRL. With the support of CI, the state information and information from the GNN-based module are proceeded by the extracting trajectory history feature module. This process endows the MADRL-based controller with the ability to identify and correct anomalous measurements. The information from the GNN-based module further enhances the robustness against anomalous measurements. The trained surrogate models provide the reward signal to MADRL during MADRL training. It enables the proposed approach to be independent on accurate MMG parameter estimates. The effectiveness of the proposed approach is validated by the simulation results.

This article proposes a generalizable nonintrusive load monitoring (NILM) framework to address nonindependent and identically distributed (Non-I.I.D.) data challenges in heterogeneous energy consumption sectors. The proposed framework uses adversarial feature augmentation based on the observed states of appliances in source sectors, and implements unsupervised domain adaptation for NILM tasks in target. A ConvNet feature extractor is built to extract the features of source samples, which are then, integrated with observed labels for adversarial data augmentation in the feature space. The generated features are used to establish a domain-invariant NILM algorithm in an unlabeled manner. Feature augmentation and domain invariant feature extractors are employed to learn effective feature mapping between the source and target sectors, thereby, accomplishing NILM tasks in Non-I.I.D. samples without additional labels. The experimental results validate the effectiveness of the proposed framework in three scenarios consisting of multiple datasets, with the best performance compared to the five state-of-the-art models.

Precise short-term residential load forecasting (STRLF) is essential for maintaining stable and cost-effective operations on the demand side. Both spatial and temporal information are important for the STRLF tasks, but effectively extracting them remains a significant challenge due to the highly volatile and stochastic nature of residential consumption patterns. To this end, this article proposes pyramidal attention (PATNet), a low-complexity transformer network with spatiotemporal PATNet, to explore multiresolution spatiotemporal representations of residential load series and forecast multiple residential loads several steps ahead. Specifically, the temporal and spatial patterns of residential load series are, first, formulated as a temporal pyramidal graph and a spatial pyramidal graph according to the periodic characteristics of load time series and the spatial correlations of different residential units, respectively. Two types of low-complexity attention mechanisms—temporal and spatial PATNet—are, then, specifically designed for the temporal and spatial pyramidal graphs such that the short- and long-range temporal dependencies and dynamic spatial correlations among various groups of residents can be captured. Moreover, to enhance multistep forecast performance, we design a gated fusion unit that is capable of adaptively fusing extracted spatiotemporal information and a transform attention block that can translate historical loads into future forecasts. Numerical simulations using several real-world residential load datasets demonstrate that the proposed framework outperforms state-of-the-art load prediction methods by 7.98% at least in single-step forecasting and 11.38% at least in multistep forecasting.

The district heating network (DHN) can provide flexibility for electric power system (EPS) to accommodate power because of its slow dynamic and thermal energy storage characteristics. However, the traditional flexibility quantifications neglect the thermal temporal-spatial dynamic propagation and uncertainty parameters of DHN, resulting in inaccurate assessment of available flexibility capabilities. To address this issue, this article proposes a dynamic flexibility quantification method. First, three flexibility metrics including capacity, amplitude, and duration of power integration are modeled by simultaneously considering the temperature temporal and spatial dynamic propagation. Then, the discretized criteria are designed to reasonably linearize the temporal and spatial dynamic variables in the metrics. Thus, the evolution of flexibility metrics over time and space can be captured. Furthermore, the uncertainty parameters (e.g., mass flow, thermal resistance) in the metrics are modeled to account for their impact on the flexibility results. To this end, the maximum entropy principle combined with the probabilistic cumulant is developed to construct the probability distributions of metrics. With these effects, the flexibility provided by DHN can be elaborately quantified. Finally, case studies and simulation analysis are carried out on China Luhua network and Denmark 61-node network to verify the effectiveness of the proposed quantification method.

Hybrid-bridge based dual-active-bridge (DAB) converter comprised of three-level (3L) and two-level (2L) full-bridge (FB) is promising to be a good candidate for medium voltage DC (MVDC) grids. By applying a corresponding modulation strategy proposed in this paper with multiple working patterns, a wide voltage conversion gain can be obtained. However, given that the conventional neutral-point-clamped (NPC) DAB converter has potential risk losing the control of the voltage conversion gain in backward and forward modes, an asymmetric active-neutral-clamped (A-ANPC) 3L structure is employed in the proposed converter to overcome this issue. Afterwards, to ensure a smooth working pattern transition, a simplified control strategy is proposed. The backflow power remains constant regardless of working conditions, and the zero-voltage-switching (ZVS) can be achieved for all power switches under the proposed modulation strategy. The operation principle, characteristics, and performances of the proposed converter are analyzed in detail and experimentally verified.

This article proposes a robust multiarea distribution system state estimation method for interval estimation of state variables based on a physics-informed decentralized graphical representation network and Gaussian process (GP)-aided multiarea state estimators. The real-time and pseudomeasurements are first cast to a graph with tree topology and a graph attention-based representation network is employed to capture the structural information between measurements from the historical data. A centralized pretraining and distributed inference framework is developed to extract essential global information from historical data and extend it to various subregions. Then, the robust nodal features extracted by the graphical network are fed into the GP with a multitask kernel for multiarea state estimation. The adopted kernel can find relevance between tasks for different subregions that are useful for the multiarea state estimation. The embedding of structural information in the representation network enables the proposed method to achieve robustness in the presence of outliers. The adopted kernel further allows us to reduce the reliance on network communication and achieve accurate multiarea state estimation. It also offers the ability to quantify the uncertainty of state variables, yielding more valuable estimation outcomes. Experimental results demonstrate the effectiveness of the proposed method in handling abnormal data and accurately quantifying the uncertainty of state variables.

For distributed solid electricity thermal storage aggregators (DSETSA), the uncertainty of the marginal clearing price may lead to the problem of multibidding scenarios (including successful, part successful, and failed biddings) in the electricity spot market. Moreover, the marginal operating cost affecting the bidding revenue in the spot market is not considered in the existing methods, which challenge the bidding of the aggregators. To address the challenge, this article proposes an optimized operation framework for DSETSA. First, based on the incomplete information characteristics of the spot market, an optimal bidding model which incorporates marginal operating cost constraints for DSETSA under multibidding scenarios is proposed to increase the operation profit. Second, the DSETSA's multibidding scenario problem induced by the uncertainty of the marginal clearing price in the electricity spot market is cast into a probability distribution representation using the Bayesian incomplete information theory to increase the chances of winning bids. Finally, framework establishes the relationship between the bidding price, electricity demand, and public traded electricity in the spot market. The effectiveness of the proposed framework is demonstrated through simulations.

Precise multienergy load forecasting (MELF) significantly contributes to the stable and economic operation of integrated energy systems (IES). However, existing MELF approaches exhibit three primary limitations: (i) naively aggregate all input features without explicit mechanisms to capture complex coupling relationships between multiple energy loads; (ii) incapable of fully exploiting the local load characteristics of each individual task; (iii) provide only deterministic forecasting results. To address these limitations, in this article, we propose a global–local probabilistic multi-energy load forecasting framework based on hybrid attention mechanism-enabled Transformer (HAT) network and sparse variational Gaussian process (SVGP)-aided residual learning method. Specifically, HAT is first utilized to capture the consumption behavior of the multi-energy loads. It employs a temporal attention module to extract the load patterns of each task and a task attention module to explicitly capture the coupling relationships between different tasks. The multiple pieces of information are fused through a gated fusion unit for the joint predictions of multiple loads. Then, an SVGP with a composite kernel is adopted to learn the local load characteristics specific to each individual task by modeling the residual of the forecasting outcomes. This further enhances the performance of the proposed method and allows us to achieve effective quantification of the forecasting uncertainties. Numerical simulations using real IES load data reveal that the proposed framework outperforms state-of-the-art deterministic load forecasting by 11% at least in mean absolute percentage error (MAPE) and probabilistic load forecasting by 5% at least in both pinball loss and Winkler score metrics.

This paper proposes a modeling method for the input series and output parallel (ISOP) dual active bridge (DAB) converter under triple phase shift (TPS) modulation. Moreover, the reduced-order state-space average (RSSA) model is established to further obtain its equivalent small signal circuit model both for DAB and ISOP DAB converter. The model shows that the ISOP-DAB converter is a multi-input and multi-output (MIMO) system, the decoupling algorithm is adopted to convert the MIMO system to single-input and single-output (SISO) system, which is convenient for the control parameter design of the controller. Besides, the decoupled transfer function of ISOP system is approximately first order system. Finally, the correctness of the modeling method is validated by simulation in time and frequency domain.

With renewable energy becoming more and more important, the heat and electricity integrated energy system (HE-IES) has been widely used because of its potential benefit in accommodating renewable energy. This work for the first time formulated a dynamic optimal energy flow (OEF) problem for the HE-IES with hybrid AC/DC sources. Firstly, to effectively and accurately assess the operation state of district heating network (DHN), the temperature dynamics are focused and solved with a set of difference equations where an adaptive discretization method is proposed. The method based on the delay characteristics of pipelines is proposed to select temporal and spatial steps for reasonable approximation of DHN, and to achieve satisfactory accuracy results with a lower computational burden. Secondly, a two-stage OEF model for HE-IES with new linear approximations is developed. In this model, a detailed converter station model coupled AC grid and DC grid is investigated, and a linearization constraint method is proposed to handle the highly nonlinear of converter station by adding extended branches to the AC grid. Finally, simulations demonstrate that the effectiveness of the proposed model, which can provide more intact information of state variables for optimal planning.Note to Practitioners—This work is motivated by the demand for optimal energy management in heat and electricity integrated energy system (HE-IES). On the one hand, the slow dynamic characteristic of the district heating network (DHN) is crucial for calculating the optimal results. On the other hand, the improvement of electronic technology has diversified energy supplies and enriched the way of using electricity, such as converter-based generators for electric power grid (EPG) and converter-based boilers for DHN. Therefore, the model of HE-IES exhibits highly nonlinearity, which brings huge challenges to optimal energy flow analysis, especially for the real-time OEF analysis with state information updated every 15–30 min. Existing studies focus on computing optimal energy flow without considering the slow dynamic characteristic of DHN or the hybrid AC/DC scenarios brought by the converter-based sources. This paper presents an optimal energy flow problem that integrates the slow dynamic characteristic and the converter-based sources to retain the complete state information of the system as much as possible in the optimization process. The work is used on an integrated IEEE-33 bus and Denmark-61 node system to show the effectiveness.

Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This article proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric machines under varying working conditions with limited data. The fault diagnosis under various working conditions is cast as a few-shot classification problem, which is solved using a model-agnostic meta-learning-based model. Specifically, a meta-learner is first trained using a series of interrelated fault-diagnosis tasks under various working conditions. During this stage, the gradient-by-gradient rule is utilized for parameter optimization to achieve an effective representation of these tasks. Then, the parameters of the meta-learner are refined on a new task. This technique can achieve fast adaptation to new tasks by utilizing only few-shot samples. The proposed method can obtain high fault-detection accuracy under various working conditions when only a limited amount of data is available. Comparative tests among various methods were carried out on the Case Western Reserve University Bearing Dataset and the Paderborn University Rolling Bearing Dataset. The results show that the proposed model performs better than other state-of-the-art methods under various working conditions; our method has stronger generalization ability and faster adaptation ability. The fault diagnosis accuracy for both datasets was at least 99%, which proves that the proposed strategy can be flexibly applied to various scenarios.

Modern home energy management systems (HEMSs) have great flexibility of energy consumption for customers, but at the same time, bear a range of problems, such as the high system complexity, uncertainty and time-varying nature of load consumptions, and renewable sources generation. This has brought great challenges for the real-time control. To solve these problems, we propose an HEMS that integrates a kernel-based real-time adaptive dynamic programming (K-RT-ADP) with a new preprocessing short-term prediction technique. For the preprocessing short-term prediction, we propose a gated recurrent unit-bidirectional encoder representations from the transformer (GRU-BERT) model to improve the forecasting accuracy of electrical loads and renewable energy generation. In particular, we classify household appliances into the temperature-sensitive loads, human activity sensitive loads, and insensitive/constant loads. The GRU-BERT model can incorporate weather and human activity information to predict load consumption and solar generation. For real-time control, we propose and employ the K-RT-ADP HEMS based on the GRU-BERT prediction algorithm. The objective of the K-RT-ADP HEMS is to minimize the electricity cost and maximize the solar energy utilization. To enhance the nonlinear approximation ability and generalization ability of the adaptive dynamic programming (ADP) algorithm, the K-RT-ADP algorithm leverages kernel mapping instead of neural networks. Hardware-in-the-loop experiments demonstrate the superiority of the proposed K-RT-ADP HEMS over the traditional ADP control through comparison.

The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties.

The uncertainties of charging behavior of electric vehicle (EV) owners have a negative impact on the loss of life (LOL) of distribution transformer. This article proposes a decentralized EV charging framework for optimization of the LOL of distribution transformer considering the dissatisfactions of EV owners. Specifically, long-short-term memory (LSTM) neural network is first utilized to capture the uncertainties caused by the load demand and electricity price. After that, each EV is modeled as an intelligent agent and a multiagent deep reinforcement learning approach is applied to solve the coordinated charging problem based on the forecasting information by the LSTM network. All the agents are trained in a centralized manner to develop coordinated control strategies while informing decisions based on local information when finishing the training process. The proposed approach can achieve coordinated charging management of EVs based on local information, which helps preserve the privacy of EV owners, reduce the cost induced by the deployment of communication devices, and avoid single-point failure. In addition, the parameter space noise and deep dense architecture in reinforcement learning are introduced to overcome premature convergence, training instability, and inefficiency due to the large action space of multiagent scenario. Comparative tests are carried out among several benchmarks utilizing real-world data to illustrate the effectiveness of the proposed approach.

Unique fault behaviors of renewable energy sources (RESs) may lead to the misoperation of traditional pilot protection. To cope with this issue, this article proposes a new pilot protection method using the improved Euclidean distance. For normal operation or external faults, the currents on both ends are completely opposite, so the Euclidean distance of current absolute values on both ends is equal to 0. However, it will be much larger than 0 for internal faults because transient currents on both ends will have a big difference at this time. Therefore, internal and external faults can be detected reliably. In order to facilitate the setting calculation, the Euclidean distance is normalized, and a stability factor is introduced to avoid invalid calculation results. The proposed method can be applied to different RES types and different fault ride through strategies. Meanwhile, it can withstand larger fault resistance and noise interference. Compared with other methods using the RES fault currents, this approach can operate correctly without any additional criteria when the circuit breaker recloses on a permanent fault or RESs output a low power. PSCAD simulation and real-time digital simulator experiment verify this method.

AC side impedance modelling of an HVDC converter station usually ignores the DC-side dynamics, instead, the dynamics characteristic of cable and another converter station is represented as a constant power source or a constant voltage source. The influence of DC-side power network dynamics for AC-side small-signal stability is not yet well analysed. This paper presents a three-port transfer matrix model of the three-phase voltage source converter (VSC) which intuitively characterizes both AC- and DC-side dynamics by relating AC- and DC-side current and voltages. This model not only derive the equivalent impedance of the DC-side power network but also easily obtain a modified AC impedance model of VSC considering the impedance characteristic of the DC-side network. With comparing the impedance characteristics of the modified impedance model and the original impedance model, this paper shows that if the DC-side dynamics are ignored in an HVDC system, it may lead to an incorrect result of stability assessment. The proposed three-port transfer matrix model simplifies the impedance modelling process of the HVDC transmission system and the modified AC impedance model provides accurate stability analysis of the HVDC transmission system. Simulation results are given to validate the correctness of the proposed impedance model and the effectiveness of the modified AC impedance model for stability analysis.

Various DC circuit breakers (DCCBs) have been widely proposed for the DC fault protection of high-voltage direct-current (HVDC) grids. In recent years, hybrid DCCBs (HCBs) have been paid significant attentions due to their features of low power losses and fast dynamic response. However, several aspects regarding the design of HCB should be further addressed. For instance, the requirement of deploying a large surge arrester to dissipate the large fault current energy should be further addressed and the strategy to perform zero-voltage switching (ZVS) of semiconductor devices during the post-fault restoration processes should be investigated. In this paper, a dual-bridge hybrid DC circuit breaker (DB-HCB) with freewheeling diode branches is proposed to address the above issues. The operation principle of the proposed DB-HCB for pole-to-ground and pole-to-pole faults is presented. Compared with other HCBs, the capacity of the surge arrester is obviously reduced, so that the capital cost and volume of the proposed DB-HCB is decreased. Moreover, the ZVS is implemented during the post-fault restoration processes. Simulation results in PSCAD/EMTDC are given to validate the effectiveness of the proposed DB-HCB.

The refined estimation of inertia can provide a reliable basis for power system operation and control. In this article, a data-driven approach for the estimation of inertia is proposed, and it can estimate the effective inertia of different areas in the interconnected power systems. Based on eigenstructure analysis, the intrinsic relationships between inertia and the eigenvalue and eigenvector are analyzed using a linearized dynamic equation. Furthermore, detailed mathematical expressions between inertia and the eigenvalue and eigenvector are established. In addition, dynamic mode decomposition is introduced to extract eigenvalues and eigenvectors from the synchronized measurements to ensure that the scheme proposed in this article can estimate the effective inertia by using only the outputs measured by the phase measurement unit. The effectiveness of the proposed approach is demonstrated through numerical simulations on the IEEE 16-machine 5-area test system and the real measurements of an actual power system.

This paper presents a double-stage voltage control method based on model predictive control to address the voltage regulation in distribution network with high penetration of distributed generation. In the first stage, the operation times of transformer with on-load tap changer and switchable capacitor banks are minimized in hourly timescale. In the second stage, the controller minimizes the distributed generation curtailment while guaranteeing bus voltages within allowed limits with a control period of 1 min. In this paper, an analytical method is applied to calculate the voltage sensitivities with respect to power injections and tap changes. Numerical simulations of a modified IEEE-33 bus system are performed to validate the proposed method.

This paper proposes a zero-voltage and zero-current switching (ZVZCS) DC/DC converter characterized by 1) capacitive output and 2) input-series output-parallel (ISOP) structure for the high input voltage and high-power applications. The proposed converter can reduce the circulating current and duty cycle loss in comparison with the conventional converter with the inductive output. More significantly, the proposed converter can also effectively reduce the ripple current on the secondary clamping capacitor. Therefore, the proposed converter can improve the reliability of the secondary clamping capacitor. The characteristics and performances of the proposed converter are analyzed in detail. Finally, the simulation results verify the proposed converter.

This paper presents an optimization method of active and reactive power dispatch among multi-paralleled voltage source grid-connected inverters (GCIs) considering stability enhancement in low-frequency range. DQ impedance model of GCI with outer power control loop, inner current control loop and phase-locked loop (PLL) is first established. Then, effects of active and reactive power references on terminal impedance frequency characteristics of GCI, e.g., passivity, are theoretically derived. It's found that high active power reference tends to destabilize the power system, and high reactive power reference tends to stabilize the power system. On the basis of it, an active and reactive power dispatch method for stability enhancement in low-frequency range is proposed, where more/less active power and less/more reactive power are allocated to the GCIs with narrow/wide bandwidths of power control loop and PLL. Simulation is performed on a five-GCIs-based power system to validate effectiveness of the proposed active and reactive power dispatch method considering low-frequency stability enhancement.

In order to reduce the levelised cost of energy (LCOE) of the offshore wind farm, many optimization works should be done to reduce the investment and increase the energy production. As one of the main expenses, the electrical system can take up more than 15% of the total investment while cable costs take a large proportion. In order to make a cost-effective wind farm, the cable connection layout should be optimized. This paper proposes a novel way for offshore wind farm cable connection layout design. The LCOE, which concerns three aspects: electrical power losses, power captured by wind turbines (WT) and investment, is selected to set up the objective function. Since all the optimization variables are integers, a heuristic algorithm, integer particle swarm optimization algorithm (IPSO), is adopted to find a near optimal solution. To improve the performance of the IPSO, an adaptive method for parameter control is used to help to find a better solution. Comparisons are made with results obtained by the Norwegian center for offshore wind energy (NORCOWE) reference wind farm and the presented method. From the simulation, it can be noticed that the presented approach can help to find a cable connection scheme which can reduce the LCOE by 1.75%.

A novel dual-flux-modulator magnetic-geared (DFM-MG)machine with high torque capability and high PM use efficiency is proposed. The stator of the proposed DFM-MG machine could not only be used to accommodate the armature winding, but also has the ability to enhance the magnetic gearing effect as an additional flux modulator. In the proposed DFM-MG machine, the low-speed rotor (LSR)with spoke-array permanent magnets (PMs)is sandwiched between the stator and flux modulator. This design will bring the benefits of significant reduction of magnetic flux leakage between adjacent PMs on the LSR, and large enhancement of the useful magnetic field harmonics. Moreover, the location of the stator enable it to be part of the magnetic circuit of the magnetic flux excited by either high-speed rotor (HSR)PM or LSR PM, so that the armature winding could be coupled with all of the PM in the proposed machine to achieve the electromechanical energy conversion. The operating principle and electromagnetic performance of the proposed DFMMG machine are investigated by dividing it into one vernier PM machine, one PM brushless machine and one magnetic gear. Finally, the predicted performance of the proposed DFM-MGM is validated by the measurements on a proof-of-concept prototype machine.

This paper presents a reduced-order modelling method for grid-connected inverter (GCI) with long transmission cable (LTC). State-space models of GCI and LTC are first obtained from frequency characteristics of terminal impedance by applying vector fitting (VF) algorithm rather than conventional mathematical modelling. Then, Prony analysis (PA) and balanced truncation (BT) algorithms are employed to find an optimized order number of state-space model. Finally, a reduced-order model of GCI with LTC is established by removing non-dominant poles of original state-space model. Simulation results show that the proposed reduced-order modelling method is able to accurately obtain dominated poles of system, and reveal practical terminal impedance characteristics. The proposed method may simplify modelling procedure and improve computational efficiency for small signal stability analysis.

The modular multilevel converter (MMC) is attractive for medium-or high-power applications because of the advantages of its high modularity, availability, and high power quality. Reliability is one of the most important challenges for the MMC consisting of a large number of submodules (SMs). The capacitor monitoring in each SM of the MMC is an important issue, which would affect the performance of the MMC. This paper proposed an effective monitoring method for the capacitance in each SM of the MMC. The proposed method reveals the relationship between the arm average capacitance and the capacitance in each SM of the arm, which can be used for capacitance estimation with a simple algorithm. The simulation studies with the professional tool PSCAD/EMTDC are conducted with the proposed method. The study results show the effectiveness of the proposed method.

Traditional power systems have become outdated, unable to provide the clean energy enforced by current regulations. In addition, the intermittent renewable energy and the distributed generation make it difficult for the current electricity grid to reliably respond to increased energy demands. For that, a heat and power integrated energy system (IES) can provide better results compared to individually operating the two sub-systems. In this paper, a detailed sequential modeling procedure is presented. In addition, an optimal power flow solution (OPF) algorithm, suitable for islanded IES is also presented. The proposed method is utilized in an IES consisted of 11 electrical buses and 11 heating nodes. The study demonstrates that during the islanded mode of operation a more economical and valid solution, compared to the standard OPF that only considers the electric part, can be achieved.

The input capacitors' voltages are unbalanced under the conventional control strategy in a dual half-bridge cascaded three-level (TL) DC/DC converter, which would affect the high voltage stresses on the capacitors. This paper proposes a pulse-wide modulation (PWM) strategy with two working modes for the dual half-bridge cascaded TL DC/DC converter, which can realize the zero-voltage switching (ZVS). More significantly, a capacitor voltage balance control is proposed by alternating the two working modes of the proposed ZVS PWM strategy, which can eliminate the voltage unbalance on the four input capacitors. Therefore, the proposed control strategy can improve the converter's performances in: 1) reducing the switching losses and noises of the power switches; and 2) reducing the voltage stresses on the input capacitors. Finally, the simulation results are conducted to verify the proposed control strategy.

A novel topology is proposed for three-phase electric spring (TPES) to achieve specific functionalities. With respect to the existing one, the novel topology contains an additional three-phase transformer with the primaries located at the position of the non-critical three-phase load (NCL) of the existing topology and its secondaries connected to the new three-phase NCL, thus forming a new three-phase smart load (SL). To control the novel topology, the so-called modified δ control utilized for the single-phase electric springs is extended to the three-phase case. Thanks to these solutions, TPES exhibits a behavior entirely different from the existing ones. For instance, under a fluctuation of the line voltages, it exhibits a change of the voltages across NCL that has the same trend as the line voltages. Moreover, it regulates the voltages across the three-phase critical load even under both imbalanced and distorted grid conditions. The effectiveness of the novel TPES topology and of its control is validated by simulation results.

In this paper, a double phase-shift control strategy is proposed for the full-bridge three-level (FBTL) DC/DC converter applied into DC distribution systems with the medium DC bus voltage. By utilizing the proposed control strategy, the voltage change rate dv/dt and voltage stress of the transformer can be effectively reduced, which means that the reliability and EMC of the FBTL DC/DC converter can be improved. The operation principle and performances of the proposed control strategy is analyzed in detail. Finally, the simulation results are presented to verify the proposed control strategy.

The modular multilevel converter (MMC) becomes attractive in the medium- and high-power application with high modularity. In this paper, the MMC is proposed to be applied in the variable speed wind turbine (VSWT) based on the full-scale back-to-back (BTB) power converter, where the generator-side converter is the three-level neutral-point-clamped (NPC) converter and the grid-side converter is the three-phase MMC. A control strategy is also presented for the MMC based VSWT for the second-order harmonic current elimination. The proposed MMC based VSWT and control not only mitigate the voltage ripple in the dc-link of the power converter to improve system performance, but also ensure the grid-side current balancing to increase the generated power of the wind turbine under the unbalanced grid fault, in comparison with the conventional VSWT based on BTB three-level NPC converters. The simulation studies of the proposed MMC based VSWT and control are conducted with a detailed PSCAD/EMTDC model, and the results show their effectiveness.

Due to the increasing penetration of wind power, operating characteristic of wind turbines may cause great concerns to frequency stability of the power system with large-scale wind power integration. Wind power plant participation in frequency regulation and the coordination with stored energy of the power system will be a trend in the future. However, the traditional wind turbines may not have the capability of frequency regulation. The issue of how wind power plants can participate in frequency regulation becomes research hotspot in recent years. Currently, most of frequency control methods of wind turbines are designed to have a new control loop from the existing control modules, such as speed control, droop control and pitch angle control. Theses novel control methods could realize the frequency support in some cases. However, these single-functional control loops have some drawbacks and could not solve the issues of frequency control effectively. For instance, the speed control could only work under the rated wind speed and the pitch control may not have fast-speed response ability to support the frequency control when the system frequency starts to fall. In this paper, a comprehensive review is presented regarding the latest studies about the multi-controller method of variable speed wind turbines in order to solve the problems of primary and secondary frequency control.

A control scheme is proposed for the three-phase electric springs (TPESs), based on the recently introduced δ control and an improved phase locked loop (PLL). The scheme utilizes three independent δ controls, one for each phase, to stabilize the voltages across the critical loads (CLs) while handing over imbalance and distortion of the microgrid voltages, in addition to their fluctuations, to the non-critical loads (NCLs). Instrumental in achieving this outcome is the operation of the improved PLL that separates the fundamental component of the positive sequence of the microgrid voltages from the other sequences and harmonics. The scheme is also able to stabilize the CL voltages under imbalance of CLs and NCLs. Simulation results obtained under various conditions of imbalance of the microgrid voltages and/ or the loads, and of distortion of the microgrid voltages, taken individually and together, show the effectiveness of the proposed control scheme of TPESs.

This paper addresses a state-space-based harmonic stability analysis of paralleled grid-connected inverters system. A small signal model of individual inverter is developed, where LCL filter, the equivalent delay of control system, and current controller are modeled. Then, the overall small signal model of paralleled grid-connected inverters is built. Finally, the state-space-based stability analysis approach is developed to explain the harmonic resonance phenomenon. The eigenvalue traces associated with time delay and coupled grid impedance are obtained, which accounts for how the unstable inverter produces the harmonic resonance and leads to the instability of whole paralleled system. The proposed approach reveals the contributions of the grid impedance as well as the coupled effect on other grid-connected inverters under different grid conditions. Simulation and experimental results are provided to verify the proposed harmonic stability assessment method.

Due to the increasing size of offshore wind farm, the impact of the wake effect on energy yields become more and more evident. The seafloor topography would limit the layout of the wind farm so that irregular layout is usually adopted in large scale offshore wind farm. However, the calculation for the energy yields in irregular wind farm considering wake effect would be difficult. In this paper, a mathematical model which includes the impacts of the variation of both wind direction and velocity on wake effect is established. Based on the wake model, a binary matrix method is proposed for the energy yields calculation for irregular wind farms. The results show that the proposed wake model is effective in calculating the wind speed deficit. The calculation framework is applicable for energy yields calculation in irregular wind farms.

The study of wind power system strongly relies on simulations in all kinds of methods. In industry, the feasibility and efficiency of wind power projects also will be verified by simulations at first. However, taking time cost and economy into consideration, simulations in large scales often sacrifice model details or computing precision in order to gain acceptable results in higher simulating speed and lower hardware costs. To balance the contradiction of costs and performance, in this paper, a novel distributed simulation platform based on PC network and Matlab is proposed. Compared with other simulation approaches, this platform can improve the speed of simulations in large scales without sacrificing details or precision largely. By means of connected computers and paralleled models, it becomes easier to study further about harmonics, control strategies in current experiment conditions. Through the data interfaces, the platform can import practical data to simulate environment situations, faults and devices, which makes the simulation much more close to reality and forms a test-bed for wind farms and power systems as well. Thus, The platform can connect to certain Supervisory Control and Data Acquisition (SCADA) systems and Energy Management System (EMS), etc. to realize non-real-time semi-physical simulation for wind farm and power system control researches.

This paper presents an overview of researches on power system stability with high wind power penetration including analyzing methods and improvement approaches. Power system stability issues can be classified diversely according to different considerations. Each classified issue has special analyzing methods and stability improvement approaches. With increasing wind power penetration, system balancing and the reduced inertia may cause a big threaten for stable operation of power systems. To mitigate or eliminate the wind impacts for high wind penetration systems, although the practical and reliable choices currently are the strong outside connections or sufficient reserve capacity constructions, many novel theories and approaches are invented to investigate the stability issues, looking forward to an extra-high penetration or totally renewable resource based power systems. These analyzing methods and stabilization techniques are presented and discussed in this paper.

Constant power load converter will introduce unstable factor to the cascaded system. Exchanging the position of constant power load converter as the source converter can improve system stability. Active solutions to modify the negative impedance of constant power load converter can also make the system more stable. The concept of coordinative front-to-end impedance control is proposed in this paper, which can assist the cascaded system with better stability. This paper compares converter impedance behaviors between different control methods, and shows that the front-to-end impedance controller can make the system more stable. The conclusions have been validated by simulation results.

This paper presents an eigenvalue-based harmonic stability analysis method for inverter-fed power systems. A full-order small-signal model for a droop-controlled Distributed Generation (DG) inverter is built first, including the time delay of digital control system, inner current and voltage control loops, and outer droop-based power control loop. Based on the inverter model, an overall small-signal model of a two-inverter-fed system is then established, and the eigenvalue-based stability analysis is subsequently performed to assess the influence of controller parameters on the harmonic resonance and instability in the power system. Eigenvalues associated with time delay of inverter and inner controller parameters is obtained, which shows the time delay has an important effect on harmonic instability of inverter-fed power systems. Simulation results are given for validating the proposed harmonic stability analysis method.

The investment of large scale offshore wind farms is high in which the electrical system has a significant contribution to the total cost. As one of the key components, the cost of the connection cables affects the initial investment a lot. The development of cable manufacturing provides a vast choice space and a great opportunity to optimize the system cost while meets the operational requirements of the offshore wind farms and the connected power systems. In this paper, a new cost model for AC-cable is proposed and the optimal cable selection framework is established using the optimization platform in Matlab. A real offshore wind farm is chosen as the study case to demonstrate the proposed method. Furthermore, the optimization is also applied to an offshore wind farm under development. It can be observed from the results that the proposed optimal cable selection framework is an efficient and systematical way for the optimal selection of cables in large scale offshore wind farms.

With the increasing integration of the wind power into the power system, wind farm are required to be controlled as a single unit and have all the same control tasks as conventional power plants. The wind farm central controller receives control orders from Transmission System Operator (TSO), then dispatch the wind power reference to each wind turbine. One of the most commonly used dispatch methods is to dispatch the wind power reference to each wind turbine proportional to each wind turbine's available wind power without the consideration of the wake effect. The wake which depends on the thrust efficient of upstream wind turbines in the wind farm influences the downstream wind speed which determines the available wind power of the downstream wind turbine. Optimize the wind power production of each wind turbine in the wind farm by the optimization of the pitch angle and tip-speed-ratio of each turbine can increase the total available wind power of the wind farm. This paper proposed a new dispatch method which dispatches the wind power reference to each wind turbine proportional to each wind turbine's optimal wind power to increase the available wind power of the whole wind farm. Particle Swarm Optimization (PSO) is used to obtain the optimal wind power for each wind turbine. A case study is carried out. The available wind power of the wind farm was compared between the traditional dispatch method and the proposed dispatch method with the consideration of the wake effect.

The paper deals with control techniques for minimizing the operating loss of doubly fed induction generator based wind generation systems when providing reactive power. The proposed method achieves its goal through controlling the rotor side q-axis current in the synchronous reference frame. The formula for the control reference is explicitly deduced in this paper considering the losses of the generator, the power electronic devices and the filter. Three control strategies are compared with the proposed method under different wind speeds and different reactive power references. The simulation results validate the effectiveness of the proposed method.

A static synchronous series compensator (SSSC) has the ability to emulate a reactance in series with the connected transmission line. A static synchronous compensator (STATCOM) is able to provide the reactive power to an electricity network. When fed with some supplementary signals from the connected power system, both SSSC and STATCOM are able to participate in the power system inter-area oscillation damping by changing the compensated reactance or the provided reactive power. This paper analyses the influence of SSSC and STATCOM on power system small signal stability. The damping controller schemes for SSSC and STATCOM are presented and discussed. The IEEE 39-bus New England system model as the test system is built in DIgSIELNT PowerFactory, in which the damping control strategies for both SSSC and STATCOM are validated by time domain simulations and modal analysis. Furthermore, comparison studies show that the SSSC is a better solution in term of equipment capabilities and costs.

In some networks Distributed Generators (DGs) are phasing out conventional power plants in terms of power production but still large efforts are required for providing ancillary services. In this paper the usage of fast response units like a Micro Turbines (MT) and a stationary Electric Vehicle Battery (EVB) is proposed for providing primary regulation in grid connected mode and for hierarchically manage an islanded LV distribution feeder. The unit models are described and a novel EVB model directly based on manufacturer's data is proposed and evaluated comparing its performances with SimPowerSystems library block. Moreover a voltage dependant power term is applied to the Voltage-Source Converter (VSC) control scheme of the EVB for improving the performances of the islanded feeder. The control is tested in case of under frequency and consequent load shedding occurring at the residential feeder of CIGRE C6.04.02 benchmark network.

Residue method is a commonly used approach to design the parameters of a power system stabilizer (PSS). In this paper, a residue identification method is adopted to obtain the system residues for different input-output pairs, using the system measurements data from time domain simulations. Then a coordinated approach for multiple PSS selection and parameter design based on residue method is proposed and formatted as an optimization problem. Particle swarm optimization (PSO) is adopted in this coordination process to find suitable parameters for PSSs so that the dominant oscillation modes can be well damped; while locations and input signals of PSSs are selected to keep PSS outputs small. The IEEE 39-bus New England system model as the test system is built in DIgSIELNT PowerFactory 14.0, in which the proposed coordination method is validated by time domain simulations and modal analysis.

With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.

This paper deals with a doubly-fed induction generator-based (DFIG) wind turbine system under grid fault conditions such as: unbalanced grid voltage, three-phase grid fault, using a high order sliding mode control (SMC). A second order sliding mode controller, which is robust with respect to matched internal or external disturbances, fast transient response and finite reaching time, is employed to reduce chattering phenomenon caused by high frequency switching of SMC, which serious exists in lower order SMC, and to overcome parameter dependence of traditional proportional integral (PI) control. In order to improve control performance of the overall system, electromagnetic power and active power oscillations elimination strategies are proposed respectively. Lastly, the effective of the proposed control strategy is verified by the simulation results of a 2 MW DFIG system.

The switched reluctance generation (SRG) mode is widely applied in the doubly salient Electromagnetic machines (DSEMs). A novel half-controlled PWM rectifier (HCPWMR) for DSEM has a promising development prospect in DSEM. With a 12/8-pole DSEM prototype, the finite element method is used in the simulation. The simulation results of HCPWMR are compared with the SRG mode. The working principle of HCPWMR is analyzed. It is concluded that a DSEM with HCPWMR mode can improve the output power.