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All Journal International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Sureshkumar, Sathya
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Electrical & Electronics Engineering

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Leveraging machine learning for sustainable integration of renewable energy generation Sreenivasan, Pushpa; Ganesan, Keerthiga; Fawad, Iffath; Sureshkumar, Sathya; Dhandapani, Kirubakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1347-1355

Abstract

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.
Machine learning-based real-time power stability optimization for photovoltaic systems using hybrid inductor-capacitor patterns Kathirvel, Jayashree; Pushpa, S.; Kavitha, P.; Sureshkumar, Sathya; Andi, Kannan; Pramasivam, Prabakaran
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp248-256

Abstract

Photovoltaic (PV) systems often face real-time power stability challenges due to rapid fluctuations in solar irradiance and varying load conditions, which conventional control strategies struggle to manage effectively. Addressing this limitation, the present study proposes a novel machine learning-based control framework integrated with a hybrid inductor-capacitor (LC) network to enhance dynamic power regulation. The proposed system employs predictive algorithms to adjust LC parameters in real time, enabling adaptive voltage and current stabilization during transient conditions. Simulation results validate the model's effectiveness, showing a 58% reduction in power fluctuation (from 12% to 5%) and consistent improvement in voltage stability index (VSI), maintaining values above 0.95 compared to 0.88-0.93 in traditional systems. Moreover, the approach reduces computation time by 66% (150 ms versus 450 ms for PID-based systems), supporting faster and more efficient control actions. These outcomes demonstrate that the proposed intelligent control strategy significantly improves energy efficiency, voltage stability, and responsiveness in PV systems, offering a scalable solution for reliable grid integration of renewable energy sources.
Co-Authors Andi, Kannan Dhandapani, Kirubakaran Fawad, Iffath Ganesan, Keerthiga Kathirvel, Jayashree Kavitha, P. Pramasivam, Prabakaran Pushpa, S. Sreenivasan, Pushpa