Intelligent Early Warning Method for Substations Based on Digital Twin

With the increasing demand for emergency response speed and early warning efficiency in substations, traditional safety early warning methods can no longer meet the requirements of modern power systems. Therefore, this paper proposes an intelligent early warning method for substations based on digital twin technology, which achieves dual early warning for substation faults. First, preliminary intelligent early warning judgments are made on parameters based on thresholds and trends. Then, by employing a combination weighting method to assign weights to each parameter and performing weighted fuzzy C-means clustering on the normalized data of each substation, multiple normal operating modes for each substation are obtained. Based on the identified normal operating modes, the operating state of the substation is comprehensively assessed according to the severity of the abnormality, thereby enabling comprehensive early warning. The results of the case study demonstrate that the proposed method can effectively identify substation faults.