An intelligent control strategy is proposed in this paper which suggests the Optimum Power Quality Enhancement (OPQE) of grid-connected hybrid power systems with solar photovoltaic, wind turbines, and battery storage. Unified Power Quality Conditioner with Active and Reactive power (UPQC-PQ) is designed with Atom Search Optimization (ASO) based Fractional-order Proportional Integral Derivative (FOPID) controller in the proposed Hybrid Renewable Energy Sources (HRES) system. The main aim is to regulate voltage while reducing power loss and reducing Total Harmonic Distortion (THD). UPQC-PQ is used to mitigate the Power Quality (PQ) problems such as sag, swell, interruptions, real power, reactive power and THD reductions related to voltage/current by using ASO based FOPID controller. The developed technique is demonstrated in various modes: simultaneous to improve PQ reinforcement and RES power injection, PRES > 0, PRES = 0. The results are then compared to those obtained using previous literature methods such as PI controller, GSA, BBO, GWO, ESA, RFA, and GA and found the proposed approach is efficient. The MATLAB/Simulink work framework is used to create the model.

This paper attempted to assess the supremacy of designed higher-order degree of freedom (DOF) proportional(P)-integral(I)-derivative(D) (PID) (3DOF-PID) controller making use of a new meta-heuristic optimization approach of water cycle algorithm (WCA) in the study of load frequency control. For this, two test system models of dual area hydro-thermal system system and multiarea multisource systems are believed for investigative purposes. The investigation is performed by imparting the area-1 of test system models with a disturbance of 10% step load (10%SLP). Parameters of 3DOF-PID are rendered optimally over the index of integral square error. Efficacy of the presented 3DOF-PID regulator fine-tuned with WCA is demonstrated with other control schemes that have been implemented on the same test systems. Moreover, superconducting magnetic energy storage and Thyristor controlled series compensator territorial schema is implemented for the MSMA system for performance enhancement. Finally, the robustness of the presented approach is demonstrated through sensitivity analysis. Graphical abstract

The brushless DC motor (BLDC) has gained significant popularity in industrial settings due to its notable attributes such as low inertia, rapid response, high power density, exceptional dependability, and reputation for being conservation-free. Typically, these are equipped by a tight-switching PWM inverter, which results in significant switching losses. Consequently, the dissipation of switching loss necessitates the use of sizable heat sinks, resulting in an increase in both the physical dimensions and mass of the drive system. Numerous researchers have developed soft switching inverters with the aim of minimizing switching losses. The utilization of a soft-switching circuit may give rise to additional issues, including heightened voltage stress, incomplete pulse width modulation control, and intricate control scheme or implementation. The present study introduces a basic soft switch inverter design that is suitable for employment in BLDC drive systems powered by batteries. The inverter exhibits low loss for power switching and voltage stress is less on the main switches, while also featuring a straightforward control scheme that is easily implementable. Upon conducting analytical analysis, simulation results were presented by evaluating the theoretical analysis.

As electric vehicles and renewable energy systems become more widespread, there is an increasing need for DC–DC converters that can provide higher voltage gain, better efficiency, and improved reliability. This paper presents a comprehensive review of the latest advancements in enhanced gain DC–DC converter technologies. It covers a range of topologies including interleaved, coupled-inductor, switched-capacitor, multiport, and resonant converters, each suited for different application requirements. The review also explores the role of emerging semiconductor materials such as silicon carbide (SiC) and gallium nitride (GaN), as well as the integration of modern control strategies like model predictive control (MPC), fuzzy logic, and sliding mode control. Artificial intelligence (AI) and digital twin technologies are also discussed as tools for improving real-time performance and predictive maintenance. Through comparative studies and application-specific recommendations, this paper identifies key research gaps and future directions that could enhance scalability, cost-effectiveness, and thermal performance in power conversion systems for EVs and renewable energy.

Integration of renewable energy sources (RES) to the grid in today’s electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.