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

Xiang Zheng, Huifang Wang, Kuan Jiang and Benteng He
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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

Energies, 2019, vol. 12, issue 19, 1-15

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.

Keywords: distribution system; inverter-interfaced distributed generator (IIDG); short-circuit current prediction; feature analysis; XGBoost method (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: 2019
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