An Explainable and Robust Method for Fault Classification and Location on Transmission Lines

Machine learning-based approaches for fault diagnosis on transmission lines have attracted increasing attention in recent years, yet concerns over their robustness and explainability hamper their practical applications. Moreover, separate algorithms are normally used to solve the fault classification and location tasks. In this work, we focus on these tasks and propose an integrated model based on the convolutional neural network, whose improved performance over separate models is demonstrated by numerical results. Besides, explainability of the proposed model is demonstrated and analyzed. Specifically, the class activation maps and attention maps illustrate that the proposed structure can reduce the degradation of the model performance due to data pollution, enhancing the credibility of the proposed method in practical applications. Moreover, the proposed model outperforms existing machine learning-based models in terms of accuracy and robustness. Such a structure has been proven effective on field data and can be applied to many other tasks of fault diagnosis.

Automated summary

Machine learning-based integrated model for fault diagnosis on transmission lines is proposed in this study, demonstrating improved performance over separate models. The explainability and robustness of the proposed model are analyzed using class activation maps and attention maps. The proposed model outperforms existing machine learning-based models in terms of accuracy and robustness, making it applicable to various fault diagnosis tasks.