Detection of secondary arc extinction and auto-reclosing in compensated AC transmission lines based on machine learning

Detecting and recognizing the secondary arc extinction is considered as a backbone of the single pole auto reclosing schemes. This paper proposes a multi-channel convolutional network to detect the secondary arc extinction during a long AC power transmission line arc fault. In contrast to the conventional methods, the proposed Machin learning-based method can automatically learn the features from the raw data given as input. Multivariate time series data of phase voltage, current and neutral reactor signals were used as the input to the proposed model. The features are extracted in the convolution layer instead of using hand-crafted feature extraction like most of the existing research. The softmax classifier is used to detect fault and secondary arc extinction. Matlab Simulink is used to simulate the test system and collect the dataset to evaluate the proposed neural network model. To verify the generalizability of the proposed architecture datasets were collected at various locations and different phase lines. The proposed algorithm was able to detect the secondary arc extinction with 98.26% accuracy and able to give signal for auto-recloser logic with the maximum window length of three cycles. The result shows that the proposed neural network architecture is accurate and robust to detect the secondary arc extinction further can be used as a signal for a single pole adaptive auto recloser scheme.