A hybrid algorithm for photovoltaic maximum power point tracking using Artificial Neural Network and Kinetic Gas Molecular Optimization
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Author |
D. Venkatabrahmanaidu, R. Thamizhselvan and Ch. Chengaiah
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e-ISSN |
1819-6608 |
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On Pages
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1026-1037
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Volume No. |
19
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Issue No. |
16
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Issue Date |
November 15, 2024
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DOI |
https://doi.org/10.59018/082433
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Keywords |
maximum power point tracking (MPPT), artificial neural network (ANN), kinetic gas molecule optimization (KGMO), particle swarm optimization (PSO), gravitational search algorithm (GSA) maximum power point tracking (MPPT).
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Abstract
The quest for efficient photovoltaic (PV) energy conversion systems has led to the development of Maximum Power Point Tracking (MPPT) algorithms. In this paper, we propose a novel hybrid approach that combines the power of Artificial Neural Network (ANN) with the optimization capability of Kinetic Gas Molecular Optimization (KGMO) for MPPT. We present a high-level outline of the proposed algorithm along with example MATLAB code snippets for each step, highlighting its potential for improving PV system performance. This paper proposes a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller to extract the maximum power from available solar energy. Firstly, to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current. The output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-KGMO MPPT controller is based on the Kinetic Gas Molecular Optimization (KGMO) Algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Finally, the performance of the proposed control system is confirmed by simulation tests on a 2 kW PV system. In addition, the performance of an ANN-KGMO -based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.
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