Stability of Electronic Circuits Based on Complex Neural Network Theory

Abstract

Abstract In recent years, with the widespread application of power electronic equipment in various fields of the national economy, the problem of insufficient stability of power electronic equipment has brought a greater negative impact on the application and promotion of new technologies and the development of the national economy. Therefore, to carry out the stability research of power electronic circuits and find suitable methods to reduce the failure rate of power electronic circuits is of great significance for improving the stability of power electronic circuits and promoting the development of power electronic technology and social progress. The purpose of this article is to study the stability of electronic circuits based on the theory of complex neural networks. In this paper, in order to realize the effective use of power grid electricity, during the low period of power consumption, the energy storage battery is operated in the charging state to store the excess energy in the grid; during the peak period of power consumption, the energy storage battery is operated in the discharged state to the load side of the grid powered by. This paper adds a multi-agent system to the power system and uses the linear time-invariant consistency protocol of the multi-agent system to obtain the net active power of the grid. Then, according to the size of the net active power and the working state of the energy storage battery, it is determined whether switch the working status of the energy storage battery. By calculating the changes in the network efficiency value of the circuit weighted network model when the failure rate of each component in the power electronic circuit changes, find the weak points in the power electronic circuit, and prove that the component’s stability to the power electronic circuit is not only related to the failure of the component itself the rate is related to the position of the component in the circuit. Experimental studies have shown that the resistance of current or control signal transmission becomes smaller, especially when the component D1 or D4 is short-circuited, the circuit network efficiency value increases by about 15%.