A Method Based on CNN–BiLSTM–Attention for Wind Farm Line Fault Distance Prediction

Ming Zhang, Qingzhong Gao (), Baoliang Liu, Chen Zhang and Guangkai Zhou
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Ming Zhang: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Qingzhong Gao: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Baoliang Liu: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Chen Zhang: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China
Guangkai Zhou: School of Electric Power, Shenyang Institute of Engineering, Shenyang 110136, China

Energies, 2025, vol. 18, issue 14, 1-18

Abstract: In view of the complex operating environments of wind farms and the characteristics of multi-branch mixed collector lines, in order to improve the accuracy of single-phase grounding fault location, the convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (attention) were combined to construct a single-phase grounding fault location strategy for the CNN–BiLSTM–attention hybrid model. Using a zero-sequence current as the fault information identification method, through the deep fusion of the CNN–BiLSTM–attention hybrid model, the single-phase grounding faults in the collector lines of the wind farm can be located. The simulation modeling was carried out using the MATLAB R2022b software, and the effectiveness of the hybrid model in the single-phase grounding fault location of multi-branch mixed collector lines was studied and verified. The research results show that, compared with the random forest algorithm, decision tree algorithm, CNN, and LSTM neural network, the proposed method significantly improved the location accuracy and is more suitable for the fault distance measurement requirements of collector lines in the complex environments of wind farms. The research conclusions provide technical support and a reference for the actual operation and maintenance of wind farms.

Keywords: wind farm; fault location; CNN–BiLSTM–attention; collector lines; zero-sequence current (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: 2025
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