Investigation on Power System Dynamic Monitoring System Using Artificial Neural Networks

The power system is one of the indispensable infrastructure in modern society, and its stable operation is crucial for the normal operation of society. Therefore, how to monitor the operation status of the power system and detect abnormal situations in a timely manner is a challenge that power system managers and engineers have been striving to solve. This article introduced the application of artificial neural networks in power systems and analyzed relevant algorithms. The model mainly consisted of two parts, namely neurons and decision support vector machines. This article studied a progressive method based on the interaction of factors such as different neural structures, power distribution characteristics, and fault state classifiers. A set of nonlinear systems for describing complex problems was established by using hybrid learning method. This article used fuzzy reasoning theory to analyze the comprehensive diagnosis technology of intelligent knowledge architecture and processing capabilities, and designed a system for power system load monitoring. This article also conducted simulation tests on the performance of the system, and the test results showed that the system’s fault finding time remained within 3 to 5 seconds.