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Fault location method for overhead line-cable hybrid line based on the LSTM network

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DOI: 10.23977/jeeem.2022.050101 | Downloads: 14 | Views: 160


GAO Shang 1, ZHAO Laijun 1, ZOU Wenlei 1, SUN Kang 1


1 School of Electrical Engineering and Automation, Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Henan Polytechnic University., Jiaozuo 454003

Corresponding Author

ZHAO Laijun


The intelligent algorithm has attracted broad attention in recent research of fault location method for the overhead line-cable hybrid line. To aim at the problems of high computational complexity and poor fault tolerance in existing hybrid line intelligent fault location algorithms, a new fault location method based on Long Short-term Memory (LSTM) network is proposed. Firstly, a 220kV hybrid line is built to collect line-mode voltage signals on the bus side of the line under different fault types. Secondly, discrete wavelet transform is used to decompose the line-mode voltage signal to extract fault features, and the data is preprocessed to obtain a sample set. Finally, the LSTM network performs adaptive learning on the input and output samples to obtain the LSTM fault location model. PSCAD/Matlab simulation results show that the fault location algorithm is simple to implement and has high fault tolerance. It is not affected by the transition resistance and the initial phase angle of the fault. It meets the requirements of engineering practice that the positioning accuracy is within 200 meters.


Hybrid line; long short-term memory network; fault location; intelligent algorithm


GAO Shang, ZHAO Laijun, ZOU Wenlei, SUN Kang, Fault location method for overhead line-cable hybrid line based on the LSTM network. Journal of Electrotechnology, Electrical Engineering and Management (2022) Vol. 5: 1-8. DOI:


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