The Fault Diagnosis Method Based On Adaptive Optimization Spiking Neural Membrance Sysytem In Electric Power System

With the rapid development of China's economy and society,the scale of power network and the degree of interconnection between networks are increasing,and higher requirements are put forward for the safe and stable operation of power systems.Power system fault diagnosis based on optimization technology is one of the power system fault diagnosis methods.It can not only diagnose the faults under the protection and circuit breaker action information,but also diagnose the faults under the protection and circuit breaker action information.Compared with other fault diagnosis methods,it has two advantages.First,the power system fault diagnosis method based on optimization technology is one of the most used methods in actual power grid fault diagnosis.Secondly,the power system fault diagnosis method based on optimization technology has strict mathematical logic and easy to implement programming.This paper will continue to explore the research of power system fault diagnosis based on optimization technology.The specific work of this paper is as follows:1.The evaluation function analysis of optimization method of the fault diagnosis in power system.Firstly,the basic framework of power system fault diagnosis based on optimization method is introduced.The basic idea of solving power system fault diagnosis problem based on optimization method is analyzed.The basic steps of fault diagnosis based on optimization method are summarized.Secondly,the five main evaluation functions are compared.The advantages and disadvantages of the 28 component power network are taken as an example.Eight typical faults are analyzed and verified based on five different evaluation functions.2.An adaptive optimization of the spike neural P system for power system fault diagnosis is proposed.Firstly,the optimization of the spike neural P system model is analyzed,and the advantages and disadvantages of the optimization of the spike neural P system are pointed out.Secondly,there is a deficiency in optimization of the spike neural P system,that is,the fixed learning rate and the regular learning probability are out of bounds,and an adaptive optimization of the spike neural P system is proposed.Taking the knapsack problem as an example,the convergence and diversity of the adaptive optimization of the spike neural P system are analyzed and applied to the IEEE39 node power network fault diagnosis.3.Design and implementation of power system fault diagnosis system based on adaptive optimization of the spike neural P system.Firstly,it analyzes its functional requirements,including obtaining SCADA information,identifying suspicious fault areas,establishing evaluation functions,searching for evaluation function minimum values and determining fault components.Secondly,designing a framework for adaptive optimization of the spike neural P system fault diagnosis system,mainly It consists of four modules: diagnostic system input module,suspected power outage area discriminating module,evaluation function building module,optimization module and diagnostic system output module.Finally,adaptive optimization of the spike neural P system is realized based on MATLAB Graphical User Interfaces(GUI).The power system fault diagnosis system,and tested in the power system 28 component local grid to verify its feasibility.