Research on the Forecasting Methd for the Surface of Insulator Based on Particle Swarm Optimization and ANN
Evaluating the degree of insulator filthy and prevent insulators pollution flashover is the important tast of electric power system. The tests are carried out in the entire insulation climate chamber, especially in saturated moisture conditions, and the effect of the equivalent salt deposit density(ESDD), the nonsoluble deposit density(NSDD) and the relative humidity(RH) on the LC are analyzed. Results show that they have the similar nonlinear function. It can be categorized as filthy degrees. In order to predict the insulator filthy, the Particle Swarm Optimization Back Propagation(PSO-BP) of the artificial neural network(ANN) is employed to build the prediction model for ESDD and NSDD. At the same time using the data simulation is carried out for the prediction model. It shows that the optimized PSO-BP model has achieved good predicting performance.
Non-soluble deposit density(NSDD) Equivalent salt deposit density(ESDD) Artificial neural network(ANN) Particle swarm optimization(PSO)
Hongliang Liu Yongqiang Wang Yilei Zhao
North China Electric Power University, Baoding, Hebei Province, China Hebei Electrical Power Researc North China Electric Power University, Baoding, Hebei Province, China North China Electric Power University, Baoding, Hebei Province, China Baotou Power Supply Bureau, Ba
国际会议
哈尔滨
英文
52-56
2012-05-19（万方平台首次上网日期，不代表论文的发表时间）