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

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  • Md Ahsan Habib

  • Md Sadik Hassan Arik

  • Md Ali Mostakim

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.

Suggested Citation

  • Md Ahsan Habib & Md Sadik Hassan Arik & Md Ali Mostakim, 2024. "AI-Driven Power Quality Analytics and Improvement of Grid Connected Solar Energy Systems," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 213-228.
    • Handle: RePEc:das:njaigs:v:7:y:2024:i:01:p:213-228:id:321
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    References listed on IDEAS

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    1. Kingsley Ukoba & Kehinde O. Olatunji & Eyitayo Adeoye & Tien-Chien Jen & Daniel M. Madyira, 2024. "Optimizing renewable energy systems through artificial intelligence: Review and future prospects," Energy & Environment, , vol. 35(7), pages 3833-3879, November.
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