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Ensemble Learning-Based Reactive Power Optimization for Distribution Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ruijin Zhu & Bo Tang & Wenhai Wei, 2022. "Ensemble Learning-Based Reactive Power Optimization for Distribution Networks," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1966-:d:766688
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    References listed on IDEAS

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    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    2. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model," Energies, MDPI, vol. 12(7), pages 1-15, March.
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