Insulators ESDD Predicting under Various Meteorological Conditions Based on Least Squares Support Vector Machines

According to the data provided by "Optical Sensor System for the ESDD Monitoring of Transmission Equipment", an intellectual prediction model based on least squares support vector machines (LS-SVM) is built, whose input variables are temperature (T), relative humidity (H), wind velocity (WV), air pressure (P) and rainfall(R), and output variable is equal salt deposit density (ESDD). In this model, the non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are substituted by equality constraints .Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups ,and the SVM algorithm is realized by least squares method. Through Grid Search Method, the optimal parameters of LS-SVM are selected automatically, which has improved the speed and accuracy of the forecasting. The simulated results show that the predicted ESDD data are very close to the on-line measured ones. Therefore, the model presented provides a doable thought for the computerization of pollution area map of power network.