Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Ruijin Zhu, Bo Tang and Wenhai Wei
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Ruijin Zhu: School of Electrical Engineering, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, China
Bo Tang: School of Electrical Engineering, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, China
Wenhai Wei: Integrated Service Center of State Grid Tibet Electric Power Supply Company, Lhasa 850000, China

Energies, 2022, vol. 15, issue 6, 1-15

Abstract: Reactive power optimization of distribution networks is of great significance to improve power quality and reduce power loss. However, traditional methods for reactive power optimization of distribution networks either consume a lot of calculation time or have limited accuracy. In this paper, a novel data-driven-based approach is proposed to simultaneously improve the accuracy and reduce calculation time for reactive power optimization using ensemble learning. Specifically, k-fold cross-validation is used to train multiple sub-models, which are merged to obtain high-quality optimization results through the proposed ensemble framework. The simulation results show that the proposed approach outperforms popular baselines, such as light gradient boosting machine, convolutional neural network, case-based reasoning, and multi-layer perceptron. Moreover, the calculation time is much lower than the traditional heuristic methods, such as the genetic algorithm.

Keywords: ensemble learning; reactive power optimization; distribution networks; data-driven; cross-validation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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