Predictions for monthly tripping quantity of circuit breaker in electric distribution network based on weighted Markov chain

Tripping quantity of circuit breaker in 10 kV rural electric distribution network has characteristic of randomness, and it is inclined to be influenced greatly by various factors such as external environments, power load, power equipment, safety consciousness of power consumer in rural area. A method of prediction based on weighted Markov chain is applied to predicting the tripping quantity of the next mouth in this paper. After transfer of history data series, the rate of tripping quantity of circuit breaker monthly is selected as observed series for predicting. The mean-variance classification model is used to confirm state space of observed series and the self-coefficients of observed series are calculated as weight value by normalization processing. The tripping quantity of circuit breaker in the next month is predicted by the method of weighted Markov chain. Result shows that the method is feasible and has upper applied value in practice.