The Application of Particle Swarm Optimization in Fault Location of Power Supply and Distribution System of Intelligent Buildings Is Improved

In order to solve the challenges of fault location in intelligent financial power supply and distribution system, in view of the shortcomings of the existing gray wolf algorithm, this study introduces an innovative application method for fault location in power supply and distribution system based on improved particle swarm algorithm. This new scheme uses the principles of neighborhood topology theory to accurately identify and locate key influencing factors, and accordingly carries out intelligent index classification to reduce possible interference. At the same time, by using the unique mechanism of improved particle swarm optimization, the design strategy of fault location in the distribution system is cleverly constructed. The empirical results show that the scheme shows a significant improvement compared with the traditional gray wolf algorithm in the key performance indicators such as the accuracy of the application and the processing efficiency of key factors in the fault location of the power supply and distribution system, showing its obvious strong advantages. In intelligent buildings, the application of fault location in power supply and distribution system plays a vital role, which can accurately predict and optimize the growth trend and output results of the application of fault location in intelligent financial power supply and distribution system. However, in the face of complex simulation tasks, traditional gray wolf algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of application in fault location of power supply and distribution system optimized by improved particle swarm optimization, and accurately controls the influencing parameters through the neighborhood topology theory, and uses it as a road map for index allocation, and then uses the improved particle swarm optimization 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 application of fault location in the intelligent financial power supply and distribution system, the simulation scheme based on the improved particle swarm optimization successfully overcomes the shortcomings of the traditional gray wolf algorithm and significantly improves the accuracy and operation efficiency of the simulation.