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

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.

GCN-Based Short-Circuit Current Calculation Method for Active Distribution Networks

“…Certain scholars have introduced a calculation approach founded on machine learning techniques to address the tradeoff between speed and accuracy in current distribution calculations within ADNs [16][17][18]. The ML method tackles the accuracy issue in electric current calculations by learning the mapping between electrical characteristics and shortcircuit currents.…”