Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm

Aiming at the problem that after the distribution automation coverage, the massive signal data in the zone I system is not effectively used, and the switchgear in the distribution network ring network cabinet has frequent transient flashover, grounding and other conditions before failure, which cannot be found in time and lead to tripping, this paper proposes a defect perception model of distribution network based on big data analysis and waveform matching algorithm. Through utilizing the data acquisition capability of the distribution terminal unit (DTU) of the distribution line, the protection settings are reasonably set to collect fault flashover information without affecting FA functions, and analyze the signal waveform characteristics to extract fault waveform characteristics for equipment defect identification; The distribution network defect perception model is established based on the waveform matching algorithm, the identification of fault waveform is trained and learned, and the analytic hierarchy process (AHP) algorithm is used to quantify the risk assessment. The feasibility of the design system is proved through the analysis of the example of the distribution network defect perception system, which guides the on-site targeted PD detection, finds and solves the equipment hidden dangers in the evolution process.