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A Study on the Development of Machine-Learning Based Load Transfer Detection Algorithm for Distribution Planning

Author

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  • Jun-Hyeok Kim

    (Department of Electrical and Electronic Engineering, Sungkyunkwan University, Suwon 16419, Korea
    Smart Power Distribution Laboratory, Korea Electric Power Corporation Research Institute, Daejeon 34056, Korea)

  • Byung-Sung Lee

    (Smart Power Distribution Laboratory, Korea Electric Power Corporation Research Institute, Daejeon 34056, Korea)

  • Chul-Hwan Kim

    (Department of Electrical and Electronic Engineering, Sungkyunkwan University, Suwon 16419, Korea)

Abstract

Distribution planning refers to the act of estimating the risks of distribution systems that may arise in the future and establishing investment plans to cope with them. Forecasted loads are one of the most typical variables used to analyze the risk of the distribution system, thus the efficiency of distribution planning may vary depending on its accuracy. For these reasons, a lot of studies are also being conducted to perform load prediction by incorporating the latest methods, such as machine learning (ML). However, the unchangeable fact is that no matter what prediction method is used, the accuracy and reliability of the predicted load can vary depending on the reliability of the data used. In particular, the detection of temporary load increases, due to load transfer that can occur frequently in the distribution system are essential for securing high-quality data. Therefore, in this study, a LSTM (Long Short-Term Memory) based load transfer detection model was proposed, and the appropriateness and reliability of the proposed method were analyzed by comparing actual planned load transfer data with the estimated load transfer results from the proposed model. It was also shown that the proposed model can improve the efficiency and reliability of the distribution planning by reasonably removing load variations, due to load transfer.

Suggested Citation

  • Jun-Hyeok Kim & Byung-Sung Lee & Chul-Hwan Kim, 2020. "A Study on the Development of Machine-Learning Based Load Transfer Detection Algorithm for Distribution Planning," Energies, MDPI, vol. 13(17), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4358-:d:403158
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    References listed on IDEAS

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    Cited by:

    1. Xuejun Zheng & Shaorong Wang & Zia Ullah & Mengmeng Xiao & Chang Ye & Zhangping Lei, 2021. "A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone," Energies, MDPI, vol. 14(12), pages 1-16, June.

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