Abnormal Event Monitoring and Alarm Optimization of Power System Based on Improved Genetic Algorithm

In order to solve the challenges of abnormal event monitoring and alarm optimization in power system, in view of the shortcomings of the existing improved particle swarm algorithm, an innovative abnormal event monitoring and alarm optimization method based on improved genetic algorithm is proposed. This new approach leverages the principles of best practice for group foraging to precisely identify and locate key influencing factors, and accordingly intelligently classify indicators to reduce possible interference. At the same time, using the unique mechanism of improved genetic algorithm, this scheme cleverly constructs a design strategy for systematic abnormal event monitoring. The empirical results show that the proposed scheme shows a significant improvement comprised with the traditional improved particle swarm optimization in key performance indicators such as the accuracy of abnormal event monitoring and alarm optimization, and the processing efficiency of key factors, showing its obvious strong advantages. In the power system, abnormal event monitoring and alarm optimization plays a vital role, which can accurately predict and optimize the growth trend and output results of abnormal event monitoring and alarm optimization in the power system. However, in the face of complex simulation tasks, traditional improved particle swarm algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of abnormal event monitoring and alarm optimization with improved genetic algorithm optimization, and accurately controls the influencing parameters through the optimal theory of group foraging, and uses it as a road map for index allocation, and then uses the improved genetic algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the process of abnormal event monitoring and alarm optimization of power system, the simulation scheme based on the improved genetic algorithm successfully overcomes the shortcomings of the traditional improved particle swarm optimization and significantly improves the accuracy and operation efficiency of the simulation.