LSTM Classifier Based Fault Detection and Classification Scheme for 1-Open Conductor Faults in Six-Phase Transmission Line

As the demand for electrical energy grows by the day, bulk power transmission is the primary concern for power system planners. Six-phase transmission lines are capable of carrying 1.732 times more power than three-phase double-circuit transmission lines with the prevailing right of way. However, due to the large number of faults, identification of faults is a critical task in the six-phase system. In this context, this work proposes a straightforward and practical solution to the complex fault classification problem. The use of Long Short Term Memory (LSTM) for 1-Open conductor problems resulted in a high degree of accuracy in fault identification and classification. The efficacy of the current classifier has been tested for various 1-Open conductor faults with a wide range of fault attributes. The validation results of LSTM based fault classifier shows that the method can detect and identify the phase involved in the fault. In addition, the method is robust to changes in the fault attributes.