Reliability Prediction of the Distribution Network Based on Wavelet Neural Network with Quantum Particle Swarm Optimization Algorithm

Abstract

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks.