Dynamic risk assessment of thermal fault in distribution station by online analysis of cloud model

Power distribution station plays a vital role in the entire power system. How to assess the risk level of an unattended distribution station is an important topic. This study proposes a dynamic risk assessment method of thermal fault based on the cloud model, order relationship analysis, and entropy weight method. Three indexes: temperature, temperature rise, and interphase temperature difference are selected to make a reasonable judgment of equipment thermal fault. The order relationship analysis method and entropy weight method are utilized to calculate the weight of the indexes, and then the calculated data is sent to the cloud model to generate a qualitative concept of the risk level. The numerical test shows that the proposed method can accurately reflect the risk level of the equipment. The cloud model can convert the quantitative value of equipment to a qualitative concept. Moreover, the cloud model can deal with abnormal condition when the data acquisition contains faulty data, avoiding the fault alarm.