Explainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis

--Novel AI-based arc fault diagnosis models have demonstrated outstanding performance in terms of classification accuracy. However, an inherent problem is whether these models can actually be trusted to find arc faults. In this light, this work proposes a soft evaluation indicator that explains the outputs of arc fault diagnosis models, by defining the the correct explanation of arc faults and leveraging Explainable Artificial Intelligence and real arc fault experiments. Meanwhile, a lightweight balanced neural network is proposed to guarantee competitive accuracy and soft feature extraction score. In our experiments, several traditional machine learning methods and deep learning methods across two arc fault datasets with different sample times and noise levels are utilized to test the effectiveness of the soft evaluation indicator . Through this approach, the arc fault diagnosis models are easy to understand and trust, allowing practitioners to make informed and trustworthy decisions. ITH the deepening of the electrification of buildings and transportation, arc faults have become an essential problem in power systems, since they can ignite surrounding materials, leading to fires that often go undetected [1] and posing serious threats to people and property [2]. Meanwhile, the arc faults will reduce the current of the circuit, which causes the conventional over-current and leakage current protection devices to fail to detect the fault [3]. Therefore, many recent studies have designed many arc fault detection or classification methods to warn of the occurrence of arc faults in advance and avoid the tragedy of fire.