A comprehensive real-time state evaluation strategy for distribution equipment based on MI-PSO-BP algorithm

This paper studies a comprehensive state assessment strategy for distribution equipment with the aim to enhance the reliability and real-time performance of the state evaluation. In an implementation framework of two-stage evaluation, a qualitative assessment method is employed to carry out the initial state evaluation, in order to provide sufficient training data samples for the real-time evaluation in the second stage. To improve the accuracy and timelessness of the distribution equipment state evaluation, we use a mutual information-based technology to refine the training data set and apply particle swarm optimization approach to optimize the parameters of the neural network-based prediction model that is crucial to the real-time evaluation. Numerical simulation results are provided to demonstrate the performance of the developed strategy.