A fault diagnosis system for power grid based on multi-source information fusion

With the increasing scale of the power grid, there exists more and more equipment in it. Thus, the probability of accidents due to the fault of a certain equipment is getting higher. Therefore, it is significant to detect and diagnose the abnormal equipment timely and effectively to keep power grid safety and steady. In this paper, we propose a fault diagnosis system to address this critical problem. Specifically, the system uses the Supervisory Control and Data Acquisition (SCADA) module to collect the switch quantity information, and conducts single fault diagnosis based on Bayesian network. In addition, it also adapts the fault recorder to obtain the electricity quantity information, and performs multiple fault diagnosis based on D-S evidence theory and fuzzy C-Means (FCM) algorithm. Ultimately, the results demonstrate that the proposed diagnosis system has high accuracy and practicability.