Identification and Calibration Method of Deviation of Main Transformer Online Monitoring Date Groups

This is the authors' abstract. We don't add key points for this paper.

Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively.