Power transformer oil–paper insulation degradation modelling and prediction method based on functional principal component analysis

Abstract

This study is for the case where the available data of power transformer oil-paper insulation is limited to a small amount furfural data, to solve the problems in oil-paper insulation degradation modelling, such as few samples available, unknown function form of the degradation process, differences of individual transformers among degradation processes, and commonality of degradation trends. A power transformer oil-paper insulation degradation modelling and prediction method based on functional principal component analysis (FPCA) is proposed. First, discrete furfural data of oil-paper insulation degradation are converted into continuous functional data, and the common degradation information of transformers is extracted based on functional time warping technology. Second, the principal components of insulation degradation are extracted based on FPCA method, and the difference of degradation information of individual transformers is obtained by analysing the differential of principal component scores. Subsequently, power transformer oil-paper insulation degradation model is constructed, and finally, the degradation model is updated based on Bayesian theory and the oil-paper insulation degradation is predicted. The example results show that compared with traditional transformer oil-paper insulation degradation modelling method, the proposed method has obvious superiority in model accuracy.

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

“…Unfortunately, during the operational lifespan of transformers, the insulation of the windings undergoes gradual degradation due to various stresses [2]. Evaluating the degradation of transformer winding insulation serves the purpose of assessing the current state of insulation degradation, constructing degradation indicators, predicting the trend of insulation degradation, and providing valuable reference for formulating effective transformer maintenance plans [3].…”

A Smart Grid Overvoltage Identification System Associated with Partial Discharge Signal and Dielectric Loss Detection

“…Currently, online monitoring is the primary means of operation and maintenance for substation units [ 1 , 2 ]. Monitoring the health status of the power system online mainly involves monitoring the overvoltage of the main network and the insulation strength of capacitive equipment [ 3 , 4 ].…”