A novel power system reliability predicting model based on PCA and RVM (Q473929)

Summary: The power system reliability is an important index to evaluate the ability of power supply. According to the characteristics of the practical grid operation, this paper trains and sets up power grid reliability predicting model, based on relevance vector machine, taking the load supplying capacity of power grid and natural calamities as input variables, and the outage time of power grid failure affecting the reliability of the power supply as output variables. In the modeling process, through principal component analysis of the training sample set of relevance vector machine, the input factor number of sample is improved, the input number of network is reduced, the network structure is simplified, and the predicting accuracy is increased. Simulation results are provided to verify the effectiveness of the proposed algorithm, which show that it provides a new way for power system reliability predicting.