Machine learning approaches for decentralized volt-VAr control in PV-rich distribution network
Rafiei, Seide Saba
Rafiei, Seide Saba
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Abstract
The increasing integration of renewable energy sources introduces chal- lenges, such as voltage fluctuations in power distribution networks. While optimal Volt/VAr control addresses these issues, it typically depends on computationally intensive centralized optimization and makes real-time implementation challenging. This study explores machine learning tech- niques, including linear regression and multi-layer perceptron (MLP), to approximate centralized optimization outcomes and enable efficient de- centralized reactive power control. Using simulated data from a 141-bus network generated via MATPOWER under various operating conditions, preliminary findings show that linear regression provides limited predic- tive accuracy, whereas MLP models significantly enhance predictive per- formance, improving accuracy by 60–92%.
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Date
2025-06-02
Student Status
Undergraduate
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Poster Presentation
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Electrical Engineering
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College of Engineering and Mathematical Sciences
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Engineering and Math Science