To augment the photovoltaic (PV) power generation conversion, a maximum power point tracking (MPPT) technique plays a very significant role. This article introduces a hybrid MPPT algorithm integrating modified invasive weed optimization (MIWO) and perturb and observe (P&O) technique under the rapid weather change and partial shading scenarios for the efficient extraction of the maximum power from the standalone PV-based hybrid system. MIWO handles the initial stages of MPPT followed by the application of the P&O algorithm at the final stages in view of acquiring the rapid global peak and maximal PV power. The studied microgrid comprises of the PV system, battery, electrolyzer, fuel cell, and load. A coordinated dc-voltage regulation and power management strategy between each subsystem of the hybrid microgrid is implemented to save the battery from the undesirable charging/discharging operation. Additionally, with the monitoring of dc voltage, the dc/dc converter associated with the battery and dc link plays as an MPPT circuit of the PV without the requirement of an extra dedicated circuit. Takagi–Sugeno (TS) fuzzy controller is adopted for suppressing/mitigating the voltage oscillations of the microgrid during the variations in the solar irradiance/temperature and power demand. The results clearly exhibit the superior performance of the proposed methodology compared with some of the existing techniques.

The increasing adoption of renewable energy sources (RES), such as solar photovoltaics and wind turbines, is transforming electricity generation. However, integrating RES within DC microgrids (DCM) for applications such as fast DC charging in electric vehicles (EVs) presents challenges, including low inertia, power fluctuations, and voltage instability. This study addresses these challenges with novel control strategies and optimization algorithms. A hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach is used to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. In addition, a comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. Crucially, a hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging.

Solar Photovoltaic (PV) power is the most common and efficient renewable energy source. The potential for utilizing solar PV energy on Earth is enormous. PV module characteristics significantly influence extracting maximum output from current solar power installations. Solar insolation and panel temperature substantially influence the voltage-current (V I) and power–voltage (P V) properties. The degree to which the system’s efficiency may be improved is determined by the accuracy with which the maximum power point tracking (MPPT) controller follows the nonlinear PV characteristics. This comprehensive investigation delves into the solar PV system dynamics, highlighting how modules induce power loss because of partial shading-induced peaks. Modified Adaptive Jaya Optimization (MAJO) is a novel meta-heuristic optimization approach designed to monitor maximum global power. MAJO boasts advantages over the conventional Deterministic Jaya Optimization (DM-Jaya) method under partial shading configurations (PSCs), exhibiting quicker convergence times and requiring fewer computing operations. MATLAB/Simulink and experimental scenarios are simulated and assessed under various environmental conditions, including step changes in solar irradiance, partial shading, and load variation, to evaluate the effectiveness of the proposed MAJO algorithm. The acquired findings are compared with the DM-Jaya and the Modified particle swarm optimization (MPSO), Maximum power trapezium (MPT) and Voltage window search (VSW) suggesting and demonstrating that the proposed MAJO algorithm yields better MPPT performance. Rigorous testing with a prototype experimental setup yielded a remarkable 99.9% tracking efficiency for the studied PSCs, accompanied by reduced settling times of 0.6s and convergence time of 0.154s. Additionally, the efficacy of the MAJO in terms of faster convergence characteristics, and attaining the optimal solution has been investigated on several unconstrained benchmark functions.

Solar photovoltaic (PV) power has several advantages such as free availability, absence of rotating parts, can be easily integrated with building architecture, and need little maintenance. However, the PV cell current–voltage (I–V) characteristics are non-linear and power generated from a PV array depends on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, partial shading, and the presence of system uncertainties and load variation conditions of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is realized that the RIO algorithm has improved search performance in terms of finding the optimal solution and faster convergence characteristics than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO, perturb and observe (P&O), and incremental conductance (IC)-based MPPT schemes.

The large‐scale wind energy conversion systems (WECSs) based on doubly‐fed induction generators (DFIGs) are very popular in recent years due to the numerous technical and economic benefits. With the increasing penetration level of wind energy, the latest grid codes require the DFIG‐based WECSs to remain connected to the grid under grid fault scenarios and deliver the required reactive power into the grid. However, the direct connection of the stator of the DFIG to the grid makes it prone to grid disturbances, especially to voltage sag. This study proposes a modified demagnetisation control strategy to enhance the low‐voltage ride‐through (LVRT) capability of the DFIG under grid faults. The proposed control strategy is implemented in a coordinated approach by using the existing demagnetisation control and the addition of an external resistance in the stator side of the DFIG. The demagnetisation control damps the direct current component of the stator flux and the external resistance accelerates the damping of the transient flux by decreasing the time constant and hence, enhancing the LVRT capability of DFIG. The effectiveness of the proposed control strategy is demonstrated under both symmetrical and asymmetrical grid faults simulated system through MATLAB/Simulink®. The comparative results justify the merits of the proposed methodology.