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Artificial Neural Network Optimized by Grey Wolf Optimizer for Maximum Power Point Tracking in Solar PV Systems.

Najaad OubeBlika
Volume 5

Abstract

Maximum Power Point Tracking (MPPT) is a fundamental control function in solar photovoltaic (PV) systems, as it ensures that PV modules operate at their highest possible power output under varying environmental and load conditions. The nonlinear current–voltage and power–voltage characteristics of PV arrays are strongly influenced by solar irradiance, temperature, shading patterns, module degradation, and load variations. Conventional MPPT algorithms such as Perturb and Observe, Incremental Conductance, and hill-climbing techniques are widely used because of their simplicity; however, they often suffer from oscillations around the maximum power point, slow convergence under rapidly changing weather conditions, and difficulty in locating the global maximum power point under partial shading conditions. To overcome these limitations, this paper proposes an Artificial Neural Network optimized by the Grey Wolf Optimizer for efficient and accurate MPPT in solar PV systems.

The proposed framework combines the nonlinear mapping capability of Artificial Neural Networks (ANNs) with the global search strength of the Grey Wolf Optimizer (GWO). The ANN model is trained to estimate the optimal duty cycle or reference voltage corresponding to the maximum power point using input variables such as PV voltage, PV current, solar irradiance, module temperature, and historical power measurements. To enhance learning performance and reduce dependence on manual parameter tuning, GWO is employed to optimize the ANN architecture and training parameters, including the number of hidden neurons, learning rate, activation function configuration, initial weights, biases, and regularization coefficients. By simulating the leadership hierarchy and hunting behavior of grey wolves, the GWO algorithm efficiently searches for near-optimal ANN configurations that improve tracking accuracy, convergence speed, and robustness under dynamic operating conditions.

A comprehensive PV system model is developed using solar array characteristics, DC–DC boost converter dynamics, pulse-width modulation control, and variable resistive or grid-connected load conditions. The proposed GWO-ANN MPPT controller is evaluated under diverse environmental scenarios, including uniform irradiance, rapidly changing irradiance, temperature fluctuations, partial shading patterns, and load disturbances. The training and testing datasets are generated from simulated and experimental PV operating conditions to cover a wide range of current–voltage and power–voltage behaviors. Data preprocessing includes normalization, noise filtering, feature scaling, and scenario balancing to ensure stable ANN learning and reliable generalization.

The performance of the proposed controller is assessed using key MPPT evaluation metrics, including tracking efficiency, convergence time, steady-state oscillation, output power ripple, global maximum power point detection accuracy, dynamic response, and robustness under partial shading. Comparative analysis is conducted against conventional MPPT techniques, standalone ANN-based MPPT, fuzzy logic control, particle swarm optimization-based MPPT, and other metaheuristic-enhanced intelligent controllers. Experimental results are expected to demonstrate that the proposed GWO-optimized ANN achieves faster convergence, higher tracking efficiency, reduced power oscillations, and improved global maximum power point identification compared with traditional and non-optimized approaches.

The proposed GWO-ANN MPPT method provides a reliable, adaptive, and computationally efficient solution for improving the energy harvesting capability of solar PV systems. By integrating intelligent neural prediction with metaheuristic optimization, the controller can respond effectively to nonlinear PV behavior and environmental uncertainty. Its implementation can enhance PV conversion efficiency, reduce energy losses, improve system stability, and support the deployment of high-performance solar energy systems in standalone, grid-connected, and smart microgrid applications.

Keywords

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