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Open AccessArticle

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

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Energies 2019, 12(19), 3793; https://doi.org/10.3390/en12193793
Received: 2 September 2019 / Revised: 20 September 2019 / Accepted: 2 October 2019 / Published: 7 October 2019
(This article belongs to the Section Smart Grids and Microgrids)
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. View Full-Text
Keywords: distribution system; inverter-interfaced distributed generator (IIDG); short-circuit current prediction; feature analysis; XGBoost method distribution system; inverter-interfaced distributed generator (IIDG); short-circuit current prediction; feature analysis; XGBoost method
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Zheng, X.; Wang, H.; Jiang, K.; He, B. A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks. Energies 2019, 12, 3793.

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