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A Fault-Line Detection Method in Non-Effective Grounding System based on Bidirectional Long-Short Term Memory Network

Aiming at low accuracy and difficulty in parameter setting in traditional fault line detection (FLD) methods, this paper proposes an FLD method in non-effective grounding systems based on Bidirectional Long-Short Term Memory Network (Bi-LSTM). First, when a single-phase ground fault occurs, real-time sampling data including current, power, and power factor are used as training and testing samples for the model. Then, Bi-LSTM network model is used to process time-series data to extract the deep features of the samples. Finally, an FLD model with good generalization ability is obtained. In practical applications, historical data set is used for offline training. The trained model is operated online, and the faulty line of non-effective grounding system is automatically selected. The simulation results show that the average FLD accuracy of this model reaches 88.7% without using the internal parameters of the non-effective grounding system, which has a wide range of application prospects.