AI-Driven Power Quality Analytics and Improvement of Grid Connected Solar Energy Systems

Md Ahsan Habib (), Md Sadik Hassan Arik () and Md Ali Mostakim ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 7, issue 01, 213-228

Abstract: Grid-connected solar photovoltaic (PV) systems introduce significant power quality (PQ) challenges, including voltage fluctuations, harmonics, and frequency instability. Traditional PQ mitigation techniques struggle with real-time adaptation, leading to inefficiencies. This paper proposes an AI-driven PQ analytics framework using machine learning (ML) and deep learning (DL) for real-time detection, classification, and mitigation of PQ disturbances. Signal processing techniques like Fourier Transform (FT) and Wavelet Transform (WT) are employed for feature extraction. A MATLAB/Simulink-based simulation demonstrates that the proposed AI framework reduces total harmonic distortion (THD) from 7.5% to 2.1% and improves voltage stability by 20%. Comparative analysis highlights the superiority of AI-based control over conventional methods. The results confirm that AI-driven PQ enhancement offers a scalable and adaptive approach for solar-integrated grids. Future work will focus on IoT-based PQ monitoring and hybrid AI optimization.

Keywords: AI-driven power quality; grid-connected solar systems; machine learning; deep learning; total harmonic distortion (THD); predictive control (search for similar items in EconPapers)
Date: 2024
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