Prediction of Pollution Flashover Voltage of Insulators Based on Genetic Algorithm
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In order to explore the prediction method of insulator flashover voltage, this paper uses genetic algorithm and BP neural network to optimize the initial weight and threshold of BP neural network by using the global search ability of genetic algorithm, and established a prediction model for salt density, gray density and pollution flash voltage. In this paper, the flashover voltage of FXBW-10/70 composite insulator under different salt density and gray density is obtained by artificial contamination test. Combine with the simulation results show that compared with the traditional BP neural network prediction model, the BP neural network optimized by genetic algorithm can speed up the convergence speed of the network and improve the prediction accuracy of the insulator flashover voltage. According to the prediction method, the external insulation state of the insulator can be evaluated by monitoring the contamination state of the insulator, which has certain guiding significance for maintaining the safe and stable operation of the power system.
Genetic algorithm, BP neural network, Pollution flash voltage, Predictive model