Forecasting the ESDD of Insulator Based on Least Squares Support Vector Machine

The equal salt deposit density (ESDD) is the source of defining pollution classes and mapping pollution areas. The surface leakage current (LC) and environment factors are detected in the online monitoring system. Investigation shows that the LC is affected not only by the contamination of insulator surface but also by the environment factors including temperature, humidity and so on. The nonlinear relationship between the LC and the various factors is complicated. Based on laboratory simulation experiments and field data, the LC R.M.S., the pulse peak value of the LC, the amplitude and times of the pulses of the LC, temperature and humidity of environment are chosen as five input variables, the ESDD is chosen as one output variable, the intelligent prediction model using least squares support vector machine (LS-SVM)is built. The feasibility of the method is proved by tests in the laboratory and the field.