Prediction model of insulator contamination degree based on adaptive mutation particle swarm optimisation and general regression neural network

Abstract

The contaminants accumulated on the surface of transmission line insulator mainly come from the suspended particles in the air. Therefore, it is necessary to consider meteorological factors and environmental factors in the prediction of insulator contamination degree. In view of the advantages of generalised regression neural network (GRNN) in the aspects of fault tolerance and robustness, this study uses it to predict equivalent salt deposit density (ESDD). Furthermore, the adaptive mutation particle swarm optimisation and GRNN prediction model is proposed in this study. According to adaptive algorithm and mutation algorithm, the inertia weight and acceleration factor of particles are dynamically adjusted to achieve the purpose of searching global optimal smoothing factor. The optimisation method can effectively avoid the premature convergence of particle swarm optimisation (PSO) and solve the drawback that PSO is easy to fall into the local optimal value. The results show that the prediction model proposed in this study can effectively predict the insulators ESDD, and the prediction error is less than the GRNN and PSO–GRNN models. The research can provide guidance for the development of a more scientific and rational maintenance plan to achieve effective control of the contaminants of the line.