Multilayer fuzzy detection of substation operation risk based on PSO optimised BP neural network
Jining Li · 2024
Due to the complexity and uncertainty of substation operation environment, as well as the problem of insufficient sample data or insufficient diversity that may exist in practical applications. Therefore, this paper proposes a multilayer fuzzy detection of substation operation risk based on PSO optimisation BP neural network. The main advantage of this approach is that it combines the powerful nonlinear mapping ability of BP neural network and the global search ability of PSO algorithm, thus improving the performance of the detection system. Firstly, the operational data from substations are collected for normalisation or standardisation to suit the training needs of the neural network. Second, the structure of the BP neural network and the parameters of the PSO algorithm are designed to find the optimal neural network parameters by iteratively updating the speed and position of the particles. Finally, the optimised BP neural network is used for training and the network parameters are adjusted to improve the prediction accuracy. The experimental results show that this technique optimises the parameters of the BP neural network through the PSO algorithm, which helps the BP neural network to better adapt to the complexity and uncertainty of the operating environment of the substation, so as to more accurately detect and assess the operational risks.