Temperature prediction of disconnecting switch based on fuzzy rough set and neural network
Yiwen Xiao, Jiang‐Wen Xiao, Lei Wang · 2017
Because of the high demand for the safety of the electrical equipment, the fault of the electrical equipment is mostly the abnormal temperature. In order to solve the problem of abnormal temperature, a new method based on fuzzy rough set and neural network is proposed. First of all, the fuzzy rough set is used to reduce the factors which affect the temperature of the switch. Then cluster the sample data. Neural network is used to get the prediction model of each cluster. And according to the current value matching model to predict the temperature. The actual data of a substation in Hubei power grid is taken as an example. The results show that the method has a good effect on the temperature prediction of the disconnector.