Solar photovoltaic (PV)-powered electric vehicles (EVs) have gained greater significance in the present-day era of transportation across the globe. This proposed work presents an analysis of a five-level reduced switched-capacitor multilevel inverter (RSC-MLI)-powered permanent magnet synchronous motor (PMSM) drive for solar PV-powered battery vehicles enabled by a rat swarm optimization (RSO) maximum power point tracking (MPPT) control mechanism. The system proposed in this paper integrates solar PV arrays and battery storage systems for efficient power transfer to EVs for propulsion. In order to achieve fast, accurate tracking of the optimal maximum power point, the RSO technique is used. A five-level RSC-MLI is used in this study, which enables boosting the voltage and lowering switching losses in the system. The performance of the PMSM is further analyzed to obtain constant parameters, such as the velocity and torque of the electric vehicle.

This paper presents the application of the artificial bee colony (ABC) algorithm to the optimization of an axially symmetric electromagnetic gripper. The optimization software consists of two modules: (a) the optimization solver, and (b) the lumped model of the gripper. The structure of the gripper was described using four decision variables. The correctness of the optimization procedure was tested using the Michalewicz function. The total mass (core mass and copper mass) of the optimized gripper was assumed as the objective function. Additionally, two constraints were assumed concerning the minimum force generated by the gripper and the maximum magnetic flux density in the core. A series of optimization calculations was performed. The results from the ABC algorithm were compared with those from the particle swarm algorithm. Selected optimization results were selected and discussed.

This paper presents a data-driven stability assessment of the IEEE 68-bus, 16-machine, 5-area system under high renewable energy penetration. Low-inertia conditions are emulated by varying renewable integration from 20% to 80%. Rotor angle dynamics are analyzed under multiple disturbances, and a dataset is generated to represent diverse operating conditions. Machine learning-based classification and clustering techniques are employed to identify system states. A hybrid deep learning framework combining Graph Neural Networks (GNN) and Long Short-Term Memory (LSTM) networks captures spatial and temporal dynamics. The proposed method achieves 98% accuracy and enables fast, reliable stability assessment for modern low-inertia power systems.

This paper proposes a Z source inverter assistedelectromagnetic energy harvesting system. Electrical and mechanicalcomponents in an energy harvesting system produceselectrical energy when tuned optimally. Adjustment of dampingand resonant frequency is a crucial parameter in operatingan energy harvesting system. This paper proposes a Z sourceinverter assisted system, where the independent control freedomof Z source inverter has been utilized to regulate theoutput power. The two key components, damping and resonantfrequency, have been regulated and therefore, an improvedperformance of the energy harvesting system has been achieved.The simulation based validation establishes the effectiveness ofproposed architecture and control under operating conditions ofvarying frequency and varying amplitude.

This article aims to employ the fitness distance balance-based chimp optimization algorithm (FDBChOA), an efficient metaheuristic exploration approach that can be applied to tackle global optimization issues and optimize parameters for Mono-Crystalline PERC solar photovoltaic modules. These modules possess better energy conversion efficiency, which enables them to produce more electricity from the same quantity of sunlight. They need less installation area because of their efficiency, making them suitable for limited area rooftops. The suggested algorithm's search performance has been tested in the single and double diode models of Mono-Crystalline PERC solar photovoltaic cells. This paper considers squared errors at three critical operating points of the considered photovoltaic module as an objective for extracting parameters. The simulation results indicated that the FDBChOA algorithm achieved higher-quality solutions than the standalone ChOA algorithm for the parameter extraction problem.

The increasing integration of energy storage is transforming the operations of today’s electricity markets. This review analyses the problems linked to the variability of renewable energy sources and the integration of distributed energy resources into existing power systems. It underlines the market model gap, which is able to deal with the operational problems that such variability creates. Additionally, this review shows that optimizing the utilization and management of energy storage systems leads to improved grid reliability, system economy, and economic resilience. A comprehensive study is performed for both conventional and deregulated markets, capturing the details of the relevant policy context and other modeling elements, including optimization methods, stochastic modeling, game theory, and artificial intelligence. Relevant Australian and Japanese real-world case studies have been analysed to demonstrate the practical application of these systems and their market activities and storage optimization strategies. This review also identifies important technological drivers, analyses new technologies, and assesses the environmental and social impacts of large-scale energy storage systems. All of these works are accompanied by conclusions meant to outline the gaps that future works should target.

This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments.

High-gain converters are well-established circuit designs that find practical use in industrial and commercial settings, particularly in applications demanding high power ratings, such as Fuel Cell Electric Vehicles (FCEV) and grid-connected Renewable Energy Sources (RES). High-gain topologies from a single source pose reliability issue in RES applications due to increased device count and stress. In the event of source failure, these topologies may lead to an energy supply gap for the loads. Addressing this challenge, integrating diverse energy sources with step-up voltage capability stands as a promising solution for both DC microgrid and Electric Vehicle (EV) applications. In this study, a Dual-Input Single-Output (DISO) converter is introduced to integrate various sources and achieve an increased output voltage gain by charging the inductors in parallel and discharging them in series. Moreover, if any sources fail, the converter can supply the energy to the load from the available source and it can be operated in bidirectional mode. This paper also extensively discusses theoretical analysis, considerations related to design and circuit modeling. Furthermore, include a comparison of this converter with several other topologies. It examined to validated with a 250 W laboratory prototype.

The paper presents optimization algorithms for the optimal design of a BLDC motor with an outer-rotor for an electric bike. The universal model of the BLDC motor was elaborated. To optimization, the whale optimization algorithm was employed. The designed motor was described by five design variables. Two optimization problems are solved. The two-module software for optimization of the BLDC motor was elaborated. Selected optimization results was selected and discussed

The manufacturers of photovoltaic (PV) panel give the data of three major points on I-V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed Marine Predators Algorithm (MPA) and Success History based Adaptive Differential Evolution (SHADE) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I-V characteristics. Hence, the parameters extracted by using proposed method results in the I-V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid Marine Predators - Success History based Adaptive Differential Evolution (MP-SHADE) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-SHADE algorithm.

Boost converters often face challenges such as sluggish dynamic behavior, inadequate voltage regulation, and variations in input voltage and load current. These issues necessitate the need for closed-loop operation. Nature-inspired optimization algorithms (NIOA) have demonstrated their effectiveness in delivering enhanced solutions for various engineering problems. Several studies have been conducted on the use of proportional-integral-derivative (PID) controllers for controlling boost converters, as documented in the literature. Some studies have shown that using fractional order PID (FO-PID) controllers can lead to better performance than traditional PID controllers. Nevertheless, implementing FO-PID can be quite complex. Considering the widespread use of commercial PID controllers in industrial settings, this study focuses on finding the best tuning for these controllers in DC-DC boost converters. The approach used is particle swarm optimization (PSO) based on integral performance criteria. Simulation results indicate that the proposed controller achieves superior performance, evidenced by the lowest settling time, overshoot, integral absolute error (IAE), and integral squared error (ISE) values under varying input voltage and load current conditions, compared to both PID and FO-PID controllers. These findings have been confirmed through hardware implementation, which demonstrates the effectiveness of the proposed controller.

The DC-DC converters have been used extensively in various industrial applications such as consumer electronics, aerospace, electric vehicles, and renewable energy systems. The reliability ensures that the converter continues to operate safely and efficiently despite the presence of faults. Enhancing the reliability of DC -DC converters is a challenging task, as power switches are the most fragile components that can be affected by faults in the system. Hence, to address these challenges and ensure the safety of the converter, it is vital to implement appropriate fast fault -diagnosis techniques and fault -tolerant strategies. Many new network topologies have been presented in literature, which lead to a shift from single input-single output to multiport converters. These converters are suitable for integrating different energy sources. However, the majority of them are operated using a time-sharing method, in which only one energy source is used at a time, and the others are inactive at any specified duty cycle. Therefore, the converter and input sources are underutilized in the conventional time-sharing approach. This paper proposes a new multi -input single -output (MISO) converter topology with fully integrated switch fault tolerance. It can perform multi -input buck, boost, and buck -boost operations. More importantly, the converter can operate uninterruptedly for single or multiple switch faults. Using simulation and experimental results, a 400 W prototype circuit is designed to analyze the converter's reliability and performance.

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels.

This paper presents an analysis and optimal design of the brushed permanent magnet motor for automotive applications. The mathematical model describing the phenomenon in the design motor was developed in the 2D finite element method (FEM). The optimization was made using the Taguchi method. Taguchi method optimization requires solving the analysis task for a determined number of experiments, and the number of experiments is closely related to the number of design variables. The optimized brushed permanent magnet motor was described by three design variables. In the conducted research the objective function is constructed by three or four components described the motor parameters and mass of permanent magnet material used to construction of the motor. Selected results of optimization were presented and discussed

The current task explores automatic generation control knowledge under old-style circumstances for a triple-arena scheme. Sources in area-1 are thermal-solar thermal (ST); thermalgeothermal power plant (GPP) in area-2 and thermal-hydro in area-3. An original endeavour has been set out to execute a new performance index named hybrid peak area integral squared error (HPA-ISE) and two-stage controller with amalgamation of proportional-integral and fractional order proportional-derivative, hence named as PI(FOPD). The performance of PI(FOPD) has been compared with varied controllers like proportional-integral (PI), proportional-integralderivative (PID). Various investigation express excellency of PI(FOPD) controller over other controller from outlook regarding lessened level of peak anomalies and time duration for settling. Thus, PI(FOPD) controller’s excellent performance is stated when comparison is undergone for a three-area basic thermal system. The above said controller’s gains and related parameters are developed by the aid of Artificial Rabbit Optimization (ARO). Also, studies with HPA-ISE enhances system dynamics over ISE. Moreover, a study on various area capacity ratios (ACR) suggests that high ACR shows better dynamics. The basic thermal system is united with renewable sources ST in area-1 also GPP in area-2. Also, hydro unit is installed in area-3. The performance of this new combination of system is compared with the basic thermal system using PI(FOPD) controller. It is detected that dynamic presentation of new system is improved. Action in existence of redox flow battery is also examined which provides with noteworthy outcome. PI(FOPD) parameters values at nominal condition are appropriate for higher value of disturbance without need for optimization.

This article investigates the impact of a high-voltage direct-current (HVDC) link and a wind turbine system (WTS) on the dynamics of a three-area thermal automatic generation control (AGC) system. A novel controller, the cascade of proportional-integral (PI) and tiltintegralderivative (TID) with filter coefficient (N) (PI-TIDN) controller is projected. The WTS units are subjected to various wind velocity scenarios, including fixed and random wind velocities. The controller parameters are concurrently enhanced using the hybrid crowsearch algorithm (HCSA). The system dynamics corresponding to the PI-TIDN controller are superior to those of PIDN and TIDN controllers. Additionally, studies with different wind velocities demonstrate that responses with fixed wind velocities are better than those with random wind velocities. Moreover, integrating WTS units with the thermal system improves dynamics compared to the thermal system alone. It is also apparent that the parallel AC-HVDC system enhances dynamics. Furthermore, sensitivity analysis exposes that the PI-TIDN controller values at nominal settings are vigorous and do not require retuning.

AbstractHere, different hybrid optimization algorithms have been applied to the constrained optimization of the line‐start permanent magnet synchronous motor. The optimal results for the hybrid Cuckoo Search and hybrid Grey Wolf Optimization have been compared. In the primary objective function, the parameters describing motor functional parameters in the steady state have been included. Moreover, the non‐linear constraint function has been taken into account. The optimization procedures have been elaborated in the Delphi and Matlab environments. The mathematical model of the line‐start permanent magnet motor has been developed in the Maxwell environment. Selected optimization results are presented and discussed.

Metaheuristic optimization algorithms (MOAs) are widely used to optimize the design process of engineering problems [...]

Numerous reviews are available in the literature on PV inverter topologies. These reviews have intensively investigated the available PV inverter topologies from their modulation techniques, control strategies, cost, and performance aspects. However, their compliance with industrial standards has not been investigated in detail so far in the literature. There are various standards such as North American standards (UL1741, IEEE1547, and CSA 22.2) and Australian and European safety standards and grid codes, which include IEC 62109 and VDE. These standards provide detailed guidelines and expectations to be fulfilled by a PV inverter topology. Adherence to these standards is essential and crucial for the successful operation of PV inverters, be it a standalone or grid-tied mode of operation. This paper investigates different PV inverter topologies from the aspect of their adherence to different standards. Both standalone and grid-tied mode of operation-linked conditions have been checked for different topologies. This investigation will help power engineers in selecting suitable PV inverter topology for their specific applications.

For extracting the equivalent circuit parameters of solar photovoltaic (PV) panels, a unique bio-inspired swarm intelligence optimisation algorithm (OA) called the dandelion optimisation algorithm (DOA) is proposed in this study. The suggested approach has been used to analyse well-known single-diode (SD) and double-diode (DD) PV models for several PV module types, including monocrystalline SF430M, polycrystalline SG350P, and thin-film Shell ST40. The DOA is adopted by minimizing the sum of the squares of the errors at three locations (short-circuit, open-circuit, and maximum power points). Different runs are conducted to analyse the nature of the extracted parameters and the V–I characteristics of the PV panels under consideration. Obtained results show that for Mono SF430M, the error in the SD model is 2.5118e-19, and the error in the DD model is 2.0463e-22; for Poly SG350P, the error in the SD model is 9.4824e-21, and the error in the DD model is 2.1134e-20; for thin-film Shell ST40, the error in the SD model is 1.7621e-20, and the error in DD model is 7.9361e-22. The parameters produced from the suggested method yield the least amount of error across several executions, which suggests its better implementation in the current situation. Furthermore, statistical analysis of the SD and DD models using DOA is also carried out and compared with two hybrid OAs in the literature. Statistical results show that the standard deviation, sum, mean, and variance of various PV panels using DOA are lower compared to those of the other two hybrid OAs.