Intelligent Diagnosis System of Networked Substation Equipment Based on Data Mining Algorithm

Abstract

The power industry is an important foundation for the development of the national economy, and a strong and reliable power supply is the basic guarantee for social stability. This research mainly discusses the intelligent diagnosis system of networked substation equipment based on data mining algorithm. First, through various experiments and monitoring methods, the original database of characteristic indicators is obtained, and then the original database is screened, repaired, and quantitatively converted into a form that is convenient for computer processing, providing a data basis for mining status information. Fusion of multi-dimensional information, classification of status levels according to certain standards or models, and further refinement functions such as fault location and division of responsibilities can be realized according to needs. Based on the results of diagnosis and evaluation, considering the actual operation mode of the system and the local human and material resources, a multi-objective optimization function considering effectiveness and safety is established. By solving the objective function, the test items that need to be arranged for a specific transformer can be obtained. Decision-making optimization such as the video frequency of the tour. The highest diagnosis rate under low temperature and overheating state was 93.28%. This research will help improve the reliability of power supply for substation equipment.