Abstract
Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to con-struct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combina-tion ESDD forecasting model is higher than that of any single model and that of traditional linear combina-tion forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.
Highlights
IntroductionSurfaces of insulators will be polluted. After being wetted, the polluted layers will deduce insulators’ insulation capability which often invites pollution flashover
Exposed to dirty environment, surfaces of insulators will be polluted
To improve Equal Salt Deposit Density (ESDD) forecasting accuracy, a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed
Summary
Surfaces of insulators will be polluted. After being wetted, the polluted layers will deduce insulators’ insulation capability which often invites pollution flashover. In recent years [1,2], there have happened several large area pollution flashovers in some locals or the whole country, which resulted in large area power blackout. Among all the causes resulting in pollution flashover, the lag and the inaccuracy of pollution distribution map of power network is a primary one. Equal Salt Deposit Density (ESDD) is the equivalent amount of NaCl that would yield the same conductivity at complete dilution [3], which is the dominant factor to classify contamination severity and draw pollution distribution map. The model provides a new doable way to boost the precision of pollution distribution map of power network
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