Improving protection reliability of series-compensated transmission lines by a fault detection method through an ML-based model

This article addresses the distance protection challenges associated with the series-compensated transmission lines and the impact of fault resistance by employing a machine-learning model. In the proposed model, stacked layers of bidirectional long short-term memory (Bi-LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics-robust and improve the correlation interpretation between the features for the Bi-LSTM model, the 3-phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power-swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series-compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power-swing conditions of the power system.