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AI-Driven Power Electronic Systems for Intelligent Renewable Energy Integration in Future Grids

by Shovon Roy, Farjana Kamal Konok, Md Sahidullah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 8
Year of Publication: 2025
Authors: Shovon Roy, Farjana Kamal Konok, Md Sahidullah
10.5120/ijca2025925029

Shovon Roy, Farjana Kamal Konok, Md Sahidullah . AI-Driven Power Electronic Systems for Intelligent Renewable Energy Integration in Future Grids. International Journal of Computer Applications. 187, 8 ( May 2025), 66-74. DOI=10.5120/ijca2025925029

@article{ 10.5120/ijca2025925029,
author = { Shovon Roy, Farjana Kamal Konok, Md Sahidullah },
title = { AI-Driven Power Electronic Systems for Intelligent Renewable Energy Integration in Future Grids },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 8 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 66-74 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number8/ai-driven-power-electronic-systems-for-intelligent-renewable-energy-integration-in-future-grids/ },
doi = { 10.5120/ijca2025925029 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-06-01T00:56:05.861871+05:30
%A Shovon Roy
%A Farjana Kamal Konok
%A Md Sahidullah
%T AI-Driven Power Electronic Systems for Intelligent Renewable Energy Integration in Future Grids
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 8
%P 66-74
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increasing demand for efficient, resilient, and intelligent renewable energy management systems has posed significant challenges to conventional grid infrastructure, particularly in dynamic load handling and power quality assurance. This research explores the integration of artificial intelligence into power electronics to optimize renewable energy system performance, focusing on real-time control, forecasting, and fault detection. A comprehensive AI-powered model combining Long Short-Term Memory (LSTM) for demand forecasting, intelligent Maximum Power Point Tracking (MPPT), and an AI-based fault detection algorithm was developed and simulated under various grid scenarios. The proposed system was evaluated using critical performance metrics such as energy conversion efficiency, Total Harmonic Distortion (THD), voltage and frequency deviation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and response time. Results demonstrated a substantial improvement in efficiency from 82.6% to 93.8%, THD reduction from 6.3% to 2.4%, forecasting accuracy with RMSE and MAE lowered to 0.54 kW and 0.36 kW respectively, and a faster response time of 0.4 seconds to system disturbances. These findings highlight the system's ability to enhance power stability, improve prediction accuracy, and respond swiftly to faults, making it ideal for modern smart grid applications. The novelty of this research lies in its holistic AI-driven approach that simultaneously addresses prediction, control, and protection challenges in renewable grids. This work significantly contributes to the advancement of smart energy technologies, offering a scalable and adaptive solution for sustainable power systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence power electronics renewable energy forecasting MPPT grid stability smart grid

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