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A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks

Author

Listed:
  • Xiang Zheng

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Huifang Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Kuan Jiang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Benteng He

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

The traditional mechanism models used in short-circuit current calculations have shortcomings in terms of accuracy and speed for distribution systems with inverter-interfaced distributed generators (IIDGs). Faced with this issue, this paper proposes a novel data-driven short-circuit current prediction method for active distribution systems. This method can be used to accurately predict the short-circuit current flowing through a specified measurement point when a fault occurs at any position in the distribution network. By analyzing the features related to the short-circuit current in active distribution networks, feature combination is introduced to reflect the short-circuit current. Specifically, the short-circuit current where IIDGs are not connected into the system is treated as the key feature. The accuracy and efficiency of the proposed method are verified using the IEEE 34-node test system. The requirement of the sample sizes for distribution systems of different scale is further analyzed by using the additional IEEE 13-node and 69-node test systems. The applicability of the proposed method in large-scale distribution network with high penetration of IIDGs is verified as well.

Suggested Citation

  • Xiang Zheng & Huifang Wang & Kuan Jiang & Benteng He, 2019. "A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks," Energies, MDPI, vol. 12(19), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3793-:d:274026
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    References listed on IDEAS

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    1. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
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