Hybrid Adaptive Fuzzy–Grey Wolf Optimizer MPPT for PV Systems under Dynamic Irradiance Conditions

Jendra Sesoca; Nurin Fitriana; Tri Kristianti; Nanta Sigit

doi:10.63922/ijevss.v5i02.3493

Jendra Sesoca Electrical Engineering, Universitas Wisnuwardhana, Malang, Indonesia.
Nurin Fitriana Electrical Engineering, Universitas Wisnuwardhana, Malang, Indonesia.
Tri Kristianti Electrical Engineering, Universitas Wisnuwardhana, Malang, Indonesia.
Nanta Sigit Industrial Engineering, Universitas Wisnuwardhana, Malang, Indonesia

DOI: https://doi.org/10.63922/ijevss.v5i02.3493

Keywords: Photovoltaic System, Maximum Power Point Tracking, Adaptive Fuzzy Logic Controller, Grey Wolf Optimizer, Dynamic Irradiance, Renewable Energy.

Abstract

The increasing global demand for electrical energy and the environmental impact caused by fossil fuel consumption have accelerated the development of renewable energy technologies, particularly photovoltaic (PV) systems. However, the efficiency of PV systems is highly affected by environmental conditions such as irradiance and temperature variations. Under dynamic irradiance conditions, the output characteristics of photovoltaic systems become nonlinear, causing continuous shifts in the Maximum Power Point (MPP). Conventional Maximum Power Point Tracking (MPPT) methods, such as Perturb and Observe (P&O) and Incremental Conductance (INC), often experience limitations including oscillation, slow convergence speed, and reduced tracking accuracy during rapid irradiance changes. Therefore, an intelligent and adaptive MPPT method is required to improve the performance and stability of PV systems under dynamic operating conditions. This research proposes a Hybrid Adaptive Fuzzy–Grey Wolf Optimizer (AF-GWO) MPPT method for photovoltaic systems operating under dynamic irradiance conditions. The proposed method combines the adaptive decision-making capability of the Fuzzy Logic Controller (FLC) with the optimization performance of the Grey Wolf Optimizer (GWO) algorithm. The Adaptive Fuzzy Logic Controller is utilized to manage the nonlinear characteristics of the PV system, while the GWO algorithm is employed to optimize the membership functions and controller parameters for improved tracking performance. The entire system is modeled and simulated using MATLAB/Simulink. Several simulation scenarios were conducted under constant and rapidly changing irradiance conditions to evaluate the effectiveness of the proposed method. The performance of the Hybrid AF-GWO MPPT was compared with conventional MPPT techniques based on parameters including tracking efficiency, rise time, settling time, tracking error, and power oscillation. The simulation results demonstrated that the proposed Hybrid AF-GWO MPPT method achieved superior performance compared to conventional approaches. The proposed controller provided faster convergence toward the Maximum Power Point, reduced steady-state oscillations, improved tracking stability, and achieved tracking efficiency above 98% under dynamic irradiance conditions. The findings of this study indicate that the Hybrid Adaptive Fuzzy–Grey Wolf Optimizer MPPT method is highly effective for enhancing photovoltaic system performance under real environmental conditions. This research contributes academically to the development of intelligent hybrid MPPT techniques based on artificial intelligence and metaheuristic optimization. Practically, the proposed method has strong potential for implementation in advanced renewable energy systems to improve the efficiency, stability, and reliability of photovoltaic power generation.

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