Virtual synchronous generator (VSG) controllers-based grid forming (GFM) converters have proven their capability in weak grids, however, their response in strong grids should be enhanced. Also, realizing DC-link control in these controllers is necessary in solar applications. These VSG controllers shall realize power synchronization loop of GFM converters for solar plants. This paper investigates the transient response of selected DC-link voltage controllers under different line impedances. It also proposes a modified virtual synchronous (mViSync) controller which is robust in both weak and strong grids. The transient response of the mViSync is then analyzed and compared to other controllers. Simulation results of the GFM converter based solar plant under altered conditions are performed. The results confirm the faster dynamic response and robustness of the proposed controller in both weak and strong grids.

Interconnecting DC microgrids (MGs) into a cluster enhances renewable energy utilization and improves power supply reliability by enabling power flow between them. Effective management of DC MG clusters requires a control system designed for rapid response to fluctuating renewable source behavior and load demands. Traditional control methods lack the ability to pre-specify desired system performance, including convergence time and transient/steady-state behavior. Therefore, this work explores a prescribed performance function-based predefined time (PPF-PDT) control for optimizing the power dispatch of interconnected DC MGs according to the user-assigned preplanned desired performance. This scheme comprises secondary and tertiary control layers to handle the optimal operation for individual MGs and interconnected MGs, respectively, using a dual-layer sparse cyber network. In each MG, the secondary control matches the incremental costs of all distributed generation units while stabilizing the MG’s average voltage to the assigned voltage level within an adjustable predefined settling time independent of initial states. Adopting PPF significantly enhances transient and steady-state behavior, ensuring the tracking errors remain within desired performance limits. Additionally, distributed tertiary controllers across multiple MGs adjust their voltage references to optimize the exchanged power among them within a user-assigned tunable settling time. A thorough Lyapunov analysis verifies the stability of the proposed control algorithm within the predefined time and confines the tracking errors within acceptable bounds. Extensive simulation and experimental studies confirm the feasibility of the control strategy under various conditions.

This article introduces a novel prescribed time-based method for analyzing the convergence of evolutionary game dynamics in an information lossy network. Traditional game theory limits players to two choices, i.e., either cooperation or defection. However, player behavior in real-world scenarios is often multidimensional and complex; therefore, this work employs a continuous action iterated dilemma that allows players to choose a wider range of strategies. Moreover, traditional convergence analysis often relies on Jacobian matrices, which entail complex derivations. In contrast, the proposed strategy employs a time generator-based protocol that achieves agreement between all the players at a prescribed time, explicitly set by the user through a time parameter within the protocol. A comprehensive Lyapunov analysis affirms the prescribed time convergence even when the network is exposed to information loss during data transfer. Numerical simulations illustrate that the proposed scheme leads to a faster agreement at the preassigned time and with a better resilience performance compared to existing methods.

Efficiently planning an Unmanned Aerial Vehicle (UAV) path is crucial, especially in dynamic settings where potential threats are prevalent. A Dynamic Path Planner (DPP) for UAV using the Spherical Vector-based Particle Swarm Optimisation (SPSO) technique is proposed in this study. The UAV is supposed to go from a starting point to an end point through an optimal path according to some flight criteria. Path length, Safety, Attitude and Path Smoothness are all taken into account upon deciding how an optimal path should be. The path is constructed as a set of way-points that stands as re-planaing checkpoints. At each path way-point, threats are allowed some constrained random motion, where their exact positions are updated and fed to the SPSO-solver. Four test scenarios are carried out using real digital elevation models. Each test gives different priorities to path length and safety, in order to show how well the SPSO-DPP is capable of generating a safe yet efficient path segments. Finally, a comparison is made to reveal the persistent overall superior performance of the SPSO, in a dynamic environment, over both the Particle Swarm Optimisation (PSO) and the Genetic Algorithm (GA). The methods are compared directly, by averaging costs over multiple runs, and by considering different challenging levels of obstacle motion. SPSO outperformed both PSO and GA, showcasing cost re-ductions ranging from 330% to 675% compared to both algorithms.

This paper presents a circuit-based modeling of MPPT controller for lead-acid battery charging in PV standalone systems using MATLAB/Simulink. The LLC resonant converter is employed as the DC-DC converter for the charge controller due to its efficiency advantages. However, most well-known MPPT algorithms are designed for PWM converters, whereas the LLC resonant converter uses PFM. Applying a PWM-based MPPT method to an LLC converter could might cause the loss of its soft switching characteristics. Therefore, an MPPT algorithm based on PFM is necessary. This paper proposes an optimal frequency control MPPT algorithm that adjusts the frequency offset around the resonant frequency using an improved P&O method. Simulation results are provided to demonstrate the algorithm’s validity and its capability to track the maximum power point under varying solar irradiance and temperature conditions.

Electric vehicles (EVs) offer a sustainable choice to traditional vehicles by reducing greenhouse gas emissions and improving air quality but also contribute to grid flexibility and stability through smart charging and vehicle-to-grid technologies. This paper conducts a mini review on integrating EVs with renewable energy sources and their impacts on power systems, environment, health, economy, and social welfare. For an informed understanding of the structure and growth of the literature along with recent research topics, the review is enriched with a data-driven and visualized bibliometric analysis of publications retrieved from Scopus over the past five years using VOSviewer software. The study delves into the economic implications of EVs, revealing both consumer cost savings and broader economic benefits, including reduced oil demand and associated environmental impacts. Also, the review discusses the significant health benefits of transitioning to EVs, such as the reduction in transportation-related emissions and their contribution to public health improvement. The review's findings indicate a significant interest within the scientific community regarding the study and resolution of various issues surrounding the integration of EVs into contemporary power grids alongside renewable sources, which aligns with the recent surge in scholarly publications. Also, by analyzing the current trends, challenges, and future directions, the study sheds light on the need for continued interdisciplinary research collaboration and policy support for a more sustainable and resilient energy future.

Incorporating electric vehicles (EVs) on a large scale into the distribution grid is a challenging task. Feeder overloads, system power losses, and voltage violations could all arise because of the unregulated charging of EVs. A centralized and coordinated control approach can monitor and manage the large number of EVs, however, requires high bandwidth and reliable communication channel. Proportional based decentralized charging control reduces charging current with the decreased local voltage that provides significant disadvantages to the downstream EVs. Therefore, this work developed a modified proportional decentralized control approach for EV battery charging that can improve the local grid voltage under heavy loading without significant charging current reduction of downstream EVs. A SiC-MOSFET switch-based H-bridge converter is utilized to experimentally validate the grid connected EV system. The proposed system is initially developed in MATLAB Simulink, and a SiC-MOSFET switch-based H-bridge converter was developed for laboratory validation.

This study delves into the effects of integrating electric vehicles (EVs) into power networks, enriched with photovoltaic (PV) systems and wind turbines, alongside energy storage solutions. With the uptick in EV adoption aimed at reducing CO2 emissions and fossil fuel use for environmental benefits, this paper offers a comprehensive comparison of EV integration techniques in the context of PV and wind energy systems, and superconducting magnetic energy storage (SMES) systems. It addresses challenges such as power loss, voltage instability, load balancing, and reactive power compensation across varying scenarios; hence, unveiling the pivotal role of SMES in enhancing grid resilience. A coordinated control strategy based-fuzzy logic control is proposed for optimizing the charging/discharging activities of SMES units and EV batteries, aiming to enhance grid management and efficiency. The FLC crucial function hinges on the electricity price and the active power signals at the grid-interface with devices and residential loads. This proposed control system meticulously orchestrates power sharing among the PV and wind systems, EVs, and the SMES, promoting optimal grid performance. The effectiveness of this integrated control framework is validated through simulation analyses using Matlab/Simulink, showcasing improvements in power system dynamics, stability, and overall sustainability.

This article intends to demonstrate a real-time sensorless predictive control for a Line-Start Permanent Magnet Synchronous Machine (LSPMSM) with a speed observer based on the Model Reference Adaptive System (MRAS). The investigated control approach is evaluated under diverse scenarios in simulation and experimental studies to give an in-depth analysis. The LSPMSM’s dynamic and steady-state performance characteristics are thoroughly examined. A step speed case was investigated. Similarly, the low and high-speed responses were assessed. The LSPMSM’s capacity to handle sudden load increases in synchronous mode was also evaluated. When compared to sensor-equipped predictive control, the real-time implementation via the dSPACE dS1103 digital controller applied to a 1.5$ kW $LSPMSM drive system indicates that the proposed design strategy achieves sensorless control without degrading machine performance in terms of operational speed, dynamic torque and flux responses, steady-state torque and flux ripples, current total harmonic distortion, and converter switching frequency.Note to Practitioners—LSPMSM usually are connected directly to the 3-phase supply. But it is important to note that they have both the qualities of IM and PMSM. The motivation for this research is to investigate how we can utilize the LSPMSM for the variable speed drive operation. Since, PMSM requires sophisticated startup mechanism, but produces similar steady-state performance to LSPMSM, this research helps evaluate the performance of the predictive control for LSPMSM without the speed sensor.

The weighting factor (WF) setting is one of the most challenging tasks related to finite control set model predictive control (FCS-MPC) algorithms in different applications. Recently, a new method has been proposed for weighting factor elimination (WFE) in predictive current control (PCC) of asymmetric six-phase induction motor (A6PIM). This method is based on adopting the double dq (2dq) modeling method for prediction and optimization steps. As a result, the proposed cost function consists of four stator current components with the same priority. Thus, equal weighting factors can be assumed. In this paper, the performance of WFE method is assessed experimentally at different operating conditions. Different figures of merit are used to further compare it with the conventional voltage space decomposition (VSD)-based method. The WFE method is found to have reduced average switching frequency and better current quality. By highlighting the potential and limitations of WFE method it can be used properly based on different applications requirements.

The objective of this work is to design a wireless communicating heliostat system using microcontrollers and Zigbee communications protocol. The Arduino microcontrollers which are connected to the Zigbee modules (XBee) communicate wirelessly which eliminates the use of cables to transfer data between the heliostats and the central controller. This wireless feature is highly desired in full scale heliostat systems. The constructed prototype features four mirrors. Given the coordinates of the heliostat and the source of heat, a tracking algorithm is implemented to direct the heliostats towards the source of heat for optimum reflection of light beam into the tower. In addition, temperature sensors and photoresistors are installed in the tower to monitor the temperature and light intensity, respectively. Finally, a graphical user interface was developed to allow the user to control the operation and monitor the sensors reading.

Predictive torque control (PTC) gains a lot of interest lately. Although the flux-weighting factor is the only tuning parameter in PTC, its selection is not a trivial task. This paper proposes an improved multi-objective fuzzy decision based method to select the best compromise voltage vector. The salient feature of the proposed approach lies in its capability to avoid high torque ripple and the need of extra priority factors, which increase the complexity of the technique. In the proposed approach, the membership functions are normalized using the global optimal values. As a result, an efficient compromise solution between torque and flux ripples minimization is automatically achieved. An experimental set up is used to validate the proposed method. Torque ripple, flux ripple, current total harmonic distortion, and average frequency are used as criteria for performance comparison to a number of reported methods in literature. The results show considerable improvement in torque ripple with slightly increased flux ripple proving the simplicity and compromising ability of the proposed approach.

Flexible AC Transmission Systems (FACTS) are recently employed to overcome the power system stability challenges. Optimal number, device location, and setting parameters of FACTS devices in large-Scale Interconnected Networks (LSIN) are considered a complex multi-objective requirement. In this paper, an advanced technique for optimal numbering, location, and sizing of Static-VAR Compensator (SVC) in LSIN is developed. The technique utilizes a line stability index for optimal numbering and location of the SVC. The optimal sizing is achieved via Cuckoo Search (CS) heuristic optimization technique. The proposed technique is applied to an IEEE-57 bus system in case of outage of line 50 and IEEE-39 bus system with penetration of wind energy generation. The simulation work, of the proposed technique applied to the two different networks, is implemented within Matlab™ program. The proposed technique enhances the voltage profile, system stability and reduces the overall power losses as per the study cases. The results obtained show the effectiveness of the proposed technique to be utilized for LSIN.

Line start permanent magnet synchronous motors (LSPMSMs) are now being widely used in the industry because of their attractive features such as, high efficiency, high power factor as well as high power density. Accurate modeling of LSPMSMs is the first step in recognizing abnormalities in such motors. This paper presents a step towards an accurate mathematical modelling of LSPMS motor under asymmetrical stator windings condition. The developed mathematical model has been implemented using MATLAB/SIMULINK. A 4hp, 4-pole, 380V LSPMS motor is used in the simulation of the developed model to investigate the performance of the motor under different loading levels and stator winding asymmetric conditions. Simulation results show that under asymmetrical stator winding oscillation are clear in the electromagnetic torque. In addition, due to this asymmetry in stator windings, the torque experience high oscillations at steady state when compared to the transient period.

This paper presents a study of tuning the existing power system stabilizers (PSSs) of the Saudi Electricity Company (SEC) power system and its effect on increasing the power transfer limit of the interconnection between Eastern Operating Area (SECE-OA) and Central Operating Area (SEC-COA). This work is also investigating the optimal location of installing additional power system stabilizers (PSSs) in SEC-EOA / SEC-COA system in order to enhance the damping characteristics of low frequency oscillations. The severely disturbed machines for a major fault disturbance have been identified. The parameters of the existing PSSs in addition to the candidate stabilizers have been tuned and optimized. The effectiveness of the suggested technique in enhancing the power system dynamic stability and extending the power transfer capability limit of the SEC-EOA and the SEC-COA power system was verified through a comprehensive linear analysis and time-domain nonlinear simulation.

In this paper Improved Strength Pareto Evolutionary Algorithm (SPEA2) is presented and developed for Multiobjective Optimal Power Flow (OPF) problem. The generation OPF optimization problem is formulated as a nonlinear constrained multiobjective problem where the generation real power and the system voltage stability are optimized concurrently. Truncation algorithms are used to manage the Pareto-Optimal set size. The best compromise solution is extracted using fuzzy set theory. The SPEA2 performance results were compared to Strength Pareto Evolutionary Algorithm (SPEA) performance results. The results exhibit the capabilities of the proposed approach in produce well-distributed Pareto-optimal solutions for the subject multiobjective OPF optimization problem.

This paper investigates the application of differential evolution (DE) algorithm for the problem of optimal placement of phasor measurement units (PMUs) in an electric power network. The problem is to determine the minimum number of PMUs and their respective locations to make the system observable. In order to cope with the continuous changes in the power system's topology, the optimization problem is formulated considering not only the new PMUs to be installed but also the existing ones to be retained or relocated. Additionally, the new formulation takes into account the locations, if any, at which PMUs shall or shall not be placed. The effectiveness of the proposed method is verified via IEEE 14-bus, 30-bus, 39-bus and 57-bus standard systems.

In this paper, a multi-objective particle swarm optimization (MOPSO) technique is proposed for solving the optimal power flow (OPF) problem in a deregulated environment. The OPF problem is formulated as a nonlinear constrained multi-objective optimization problem where the fuel cost and wheeling cost are to be optimized simultaneously. MVA-km method is used to calculate the wheeling cost in the system. The proposed approach handles the problem as a true multi-objective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal solutions of the multi-objective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multi-objective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm.

In Genetic Algorithms (GAs) technique, offspring chromosomes are created by merging two parent chromosomes using a crossover operator or modifying an existing chromosome using a mutation operator. However, in scheduling problems in which the genes represent activities' start times, the crossover and mutation operators may cause violation of the precedence relationships in the offspring chromosomes. This paper proposes improved crossover and mutation algorithms to directly devise feasible offspring chromosomes. The proposed algorithms employed the traditional Free Float (FF) and a newly-introduced Backward Free Float (BFF). The obtained results exhibited robustness of the proposed algorithms to reduce the computational costs, and high effectiveness to search for optimal solutions. Moreover, validation was performed by comparing the results against the exact solutions obtained by the Integer Programming (IP) technique.

In this paper assessment of different genetic algorithm (GA) selection, crossover and mutation techniques in term of convergence to the optimal solution for single objective reactive power optimization problem is presented and investigated. The problem is formulated as a nonlinear optimization problem with equality and inequality constraints. Also, in this paper a simple cost appraisal for the potential annual cost saving of these GA techniques due to reactive power optimization will be conducted. Wale & Hale 6 bus system was used in this paper study.

In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents a two-level of nondominated solutions approach to MOPSO. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial test problems. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.

In this paper, strength Pareto evolutionary algorithm (SPEA) for optimal reactive power (VAR) dispatch problem is presented. The optimal VAR dispatch problem is formulated as a nonlinear constrained multiobjective optimization problem where the real power loss and the voltage stability are to be optimized simultaneously. The proposed approach handles the problem as a true multiobjective optimization problem. A hierarchical clustering algorithm is imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise solution over the tradeoff curve. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal solutions of the multiobjective VAR dispatch problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multiobjective VAR dispatch problem are confirmed.

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.