Power grid accident inversion and risk identification based on adaptive fuzzy inference algorithm

Abstract

Based on power grid accident inversion and risk identification, a self-adaptive fuzzy inference algorithm has been designed. Using spatial reconstruction method to extract feature information from multi-channel risk data of power grid accidents, introducing the concept of risk accident hazard assessment value, and using it as a variable in the risk data perception model, a basic inversion model based on the selection and transformation of power grid accident risk variables is established. Fuzzy logic units are combined with neural networks to propose an adaptive neuro-fuzzy inference algorithm. Applying adaptive fuzzy reasoning algorithm to the integrated design of power grid accident inversion and risk identification, achieving risk identification. The experimental results show that the experimental data accuracy of this design method is the highest, reaching 0.9907, with an accuracy of 0.9336, a recall rate of 0.9214, a recognition time of 2.84 seconds, and a significant advantage in processing speed.