The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions.

In this paper, an energy management system (EMS) using intelligent Lyapunov based adaptive fuzzy controller is designed for standalone microgrid having photovoltaic and wind turbines as primary sources, while battery storage system and diesel generator are available as secondary sources. Moreover, any remnant power available in the system is guided by the controller towards the dump load. The centralized controller based on intelligent adaptive fuzzy system helps to optimize the power flow in the system during high and low penetration of renewable energy resources under variable load conditions. Additionally, voltage regulation is carried out using nonlinear Lyapunov controller which kept the output voltages in desired range. The proposed control system helps in increasing the efficiency and reliability of standalone microgrid. Furthermore, the proposed system is validated by comparing the simulation and hardware results with the PID and sliding mode controller, which are consider as benchmark controller techniques for standalone microgrid. The outcome of the system validates the superiority of proposed techniques and achieved global asymptotic stability under variable load conditions.

The importance of microgrids has been acknowledged with the increasing amount of research in direct current (DC) microgrids. The main reason for this is the straightforward structure and efficient performance. In this research article, double integral sliding mode controllers (DISMCs) have been proposed for energy harvesting and DC microgrid management involving renewable sources and a hybrid energy storage system (HESS). DISMC offers a better dynamic response and reduced amount of chattering than the traditional sliding mode controllers. In the first stage, the state differential model for the grid was derived. Then, the nonlinear control laws were proposed for the PV system and hybrid energy storage system to achieve the main objective of voltage regulation at the DC link. In the later part, the system’s asymptotic stability was proven using Lyapunov stability criteria. Finally, an energy management algorithm was provided to ensure the DC microgrid’s smooth operation within the safe operating limit. The proposed system’s effectiveness was validated by implementing on MATLAB/Simulink software and comparing against sliding mode control and Lyapunov redesign. Moreover, to ensure the proposed controller’s practical viability for this scheme, it has been tested on real-time hardware-in-the-loop test bench.

Conventional electricity generation methods are under the major revolution, and microgrids established on renewable energy sources are playing a vital role in this power generation transformation. This study proposes a hybrid AC/DC microgrid with a barrier function-based adaptive sliding mode controller, in which 8 kW wind energy system and 4.5 kW photovoltaic energy system perform as the hybrid RESs, and 33 Ah of battery works as the energy storage system. Barrier function-based adaptive sliding mode controller ensures the convergence of the system’s output variable independent of the knowledge of the upper bound of the disturbances. Firstly, global mathematical modeling of the suggested system is ensured. Then, the control laws are defined, providing the DC bus voltage regulation during islanding mode and AC/DC link bus voltage regulation during the grid-connected mode. The proposed barrier function-based adaptive sliding mode controller technique is analyzed through 20 s simulations on MATLAB/Simulink, which validates the controller’s robustness and effectiveness. Furthermore, a comparison of the proposed controller is made with the proportional integral derivative controller, Lyapunov controller, and sliding mode controller. In the end, hardware-in-loop tests are performed using C2000 Delfino MCU F28379D LaunchPad, showing the proposed structure’s real-time performance.