The power versus voltage curves of solar photovoltaic panels form several peaks under fractional (partial) shading conditions. Traditional maximum output power tracking (MPPT) techniques fail to achieve global peak power at the output terminals. The proposed Cat Swarm Optimization (CSO) method intends to apply MPPT techniques to extract the global maxima from the shaded photovoltaic systems. CSO is a robust and powerful metaheuristic swarm-based optimization technique that has received very positive feedback since its emergence. It has been used to solve a variety of optimization issues, and several variations have been developed. The CSO-based maximum power tracking technique can successfully tackle two major issues of the PV system during shading conditions, including random oscillations caused by conventional tracking techniques and power loss. The proposed techniques have been extensively used in comparison to conventional algorithms like the Perturb and the Observe (P and O) technique. The main objective is to achieve a tracking speed for extracting the Maximum Power Point (MPP) from the solar Photovoltaic (PV) system under fractional shading conditions by using CSO. Modeling of the solar photovoltaic array in the MATLAB/Simulink platform comprises a photovoltaic module, a switching converter (Boost Converter), and the load. The PSO and CSO techniques are applied to the PV module under different weather conditions. The PSO algorithm is compared to the CSO algorithm according to simulation results, revealing that the CSO algorithm can provide better accuracy and a faster tracking speed.

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This paper proposes the application of a metaheuristic algorithm inspired by the social behavior of chimps in nature, called Chimp Optimization Algorithm (ChOA), for the maximum power point tracking of solar photovoltaic (PV) strings. In this algorithm, the chimps hunting process is mathematically articulated, and new mechanisms are designed to perform the exploration and exploitation. To evaluate the ChOA, it is applied to some fixed dimension benchmark functions and engineering problem application of tracking maximum power from solar PV systems under partial shading conditions. Partial shading condition is a common problem that appears in the solar PV modules installed in domestic areas. This shading alters the power developed by the solar PV panel, and exhibits multiple peaks on the power variation with voltage (P-V) characteristic curve. The dynamics of the solar PV system have been considered, and the mathematical model of a single objective function has been framed for tuning the optimal control parameter with the suggested algorithm. Implementing various practical shading patterns of solar PV systems with the ChOA algorithm has shown improved solar power point tracking performance compared to other algorithms in the literature.

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In recent times, regional renewable energy sources (RES) are increasingly integrated with the existing electric power grid by onboarding certain superior power quality features, thereby helping them in meeting massive electrical demand. This grid integration drastically reduces the use of fossil fuels, besides preventing environmental hazards as well. However, in grid‐connected systems, with RES such as photovoltaic (PV) systems and wind energy systems, many power quality issues (PQ) still crop up. In this paper, Gray Wolf Optimization (GWO), in conjunction with a static synchronous compensator (STATCOM) is suggested to address such challenges in the RES integrated grid‐connected PV systems. The intermittent nature of solar PV power under shading conditions can be harnessed by using the GWO approach. STATCOM is one of many specialized power electronic devices that effectively reduce power quality concerns such as voltage sag/swell and Total Harmonic Distortions. The fast‐acting PI controller is intended to drive a STATCOM by using some tuned parameters for maintaining the DC link voltage as constant. The best findings are attained after these results are compared with PI. The test system is modeled using the MATLAB/Simulink environment.