Research on Power Transformer Test Plan based on Big Data Processing Algorithm

Abstract

There are many power transformer parameters, and their changes are closely related to factors such as grid operation and meteorological environment. It is urgent to use effective big data technology to mine and analyze a large amount of relevant data and extract information to improve the timeliness and accuracy of power transformer experiments. First, based on a large number of fault and defect samples, the corresponding relationship between the key performance of the power transformer and the state quantity is mined through the confidence of the association rule, and then the time series of the state quantity of the power transformer is characterized by big data through the big data processing algorithm. The characteristic root spectrum distribution and circle rate of big data containing time series models are studied, the history and current state information of state quantities are analyzed, and the key performance experiments and abnormal detection of power transformers are realized. Taking a 500kV substation as an example, the transformer load, online monitoring and environmental meteorological data fusion constitute the key performance big data, and the big data processing algorithm is used to compare the spectral properties of the historical and current time period matrices to realize the key performance experiment of the transformer And anomaly detection. The research results show that the big data processing algorithm is effective for analyzing the operating status of power transformers, and provides a new way of thinking for the application of big data technology in power transformer status experiments.