Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification.

• Development of a new approach for transmission line faults detection and classification based on the convolutional neural network combined with wavelet transform which makes the network to efficiently train and extract the relevant features from the three phase faulty data. • Integration of the self attention mechanism with proposed CNN to identify the particular type of fault more accurately that enables the model to perform the precise classification. • Variation of input signals like voltage signal, current signal, and combined with voltage and current signal are employed at various sampling frequency to verify the performance of proposed model. • Noises are considered to prepare the input data to confirm the robustness of proposed SAT-CNN model. • Performance comparison between proposed and some other state-of-the-art model is done to confirm the high performance of proposed SAT-CNN model. This paper introduces a novel self-attention convolutional neural network (SAT-CNN) model for detection and classification (FDC) of transmission line faults. The transmission lines continuously experience the number of shunt faults and its effect in the practical system rises the instability, line restoration cost and damages the load. Therefore, a robust and precise model is needed to detect and classify the faults for the rapid restoration of faulty phases. In this paper, we propose a SAT-CNN framework with time series imaging based feature extraction model for FDC of a transmission line. To ensure the noise immunity performance, the discrete wavelet transform (DWT) has been used to denoise the faulty voltage and current signals. The effectiveness of the proposed SAT-CNN framework is tested by varying the input signals namely voltage, current, and combined voltage and current signal, under the various sampling frequencies. The robustness of the proposed model is verified by adding the noises to the input data. Results show that the proposed model is capable to perform precise classification and detection of transmission line faults with high accuracy. A comparison between the proposed and other state-of-the-art FDC model is also studied to show the superiority of the proposed SAT-CNN model. [ABSTRACT FROM AUTHOR]