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Enhancing Solar Photovoltaic Systems through Advanced MPPT Control: A Comparative Analysis of AI-Based Techniques and A Novel ML-Based SVR Model for Optimal Performance and Stability

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 1
Year of Publication : 2024
Authors : S.V. Kirubakaran, S. Singaravelu
10.14445/23488549/IJECE-V11I1P104
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How to Cite?

S.V. Kirubakaran, S. Singaravelu, "Enhancing Solar Photovoltaic Systems through Advanced MPPT Control: A Comparative Analysis of AI-Based Techniques and A Novel ML-Based SVR Model for Optimal Performance and Stability," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 1, pp. 39-52, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I1P104

Abstract:

This paper addresses the critical need to achieve consistently stabilized output power in solar Photovoltaic (PV) systems, which is achieved through the implementation of Maximum Power Point Tracking (MPPT) mechanisms. Recent research findings consistently highlight the superiority of MPPT controllers employing Artificial Intelligence (AI) techniques over traditional MPPT methods. In response, this study proposes a novel approach that integrates Machine Learning (ML), specifically a Support Vector Regression (SVR) MPPT controller. The core objective is to rigorously benchmark the effectiveness of the suggested ML-based SVR MPPT controller against well-established AI-based MPPT counterparts. This comparative analysis spans vital performance indicators, including Mean Efficiency (ME), Settling Time (Ts), Rise Time (tr), Peak Time (Tp), and Percentage Overshoot (PO). Through meticulous investigation, this paper not only contributes to the ongoing evolution of modern MPPT techniques but also offers intricate insights into the distinct advantages of AI-based and ML-based strategies in significantly enhancing the overall performance and adaptability of MPPT controllers. This analysis employs a single junction Gallium Arsenide (GaAs) solar cell known for its elevated efficiency in constructing a 2KW solar panel. Additionally, an optimized DC-DC boost converter is integrated into the setup. The SVR tool is trained and tested using diverse temperature and irradiance data sets to detect the PV panel’s maximum power and voltage under specific conditions. The optimum DC-DC boost converter’s Duty Cycle (D) control for MPPT is made by the detected values from the SVR algorithm. An energy-efficient GaAs cell-based PV system is enabled using the proposed ML-based SVR MPPT controller, which forces the PV panel to operate the detected Maximum Power Point (MPP). The proposed SVR algorithm offers better stability and operates at 96.6% of mean efficiency, irrespective of climatic changes. This work is further extended for comparison with Perturb and Observe (P&O) and Fuzzy Logic Control (FLC) to evaluate the effectiveness of the proposed work.

Keywords:

Artificial Intelligence, Efficiency, Machine Learning, MPPT, Support Vector Regression, GaAs.

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