An effective principal component regression method for transformer life management based on indirect parameters

Li, Shiiun; Yang, Liuqing; Wang, Yan; Cui, Huize; Ge, Zhao; Li, Shengtao

doi:10.1109/cmd.2018.8535897

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The aging experimental results obtained using the other two oil samples (mineral oil-2 and palm oil) are used to assess this adaptive feature. The performance of the proposed FCM-LR model is compared with the performance of the principal component analysis linear regression (PCA-LR) proposed in [2]. Results of this comparison are listed in Tables 5 and 6 and are plotted in Figure 8.…”

Section: Generalization Performance Of the Proposed Methodsmentioning

confidence: 99%

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A New Technique to Estimate the Degree of Polymerization of Insulation Paper Using Multiple Aging Parameters of Transformer Oil

Abu-Siada

et al. 2019

IEEE Access

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Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.INDEX TERMS Degree of polymerization, oil aging parameters, paper insulation, power transformers.

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Section: Generalization Performance Of the Proposed Methodsmentioning

confidence: 99%

“…During the operation of the power equipment, OIP gradually degrades due to the multi-stresses it is subjected to. Since insulation condition is a key factor for reliable operation of power equipment, evaluation of insulation condition has received much attention by researchers and industry [2].…”

Section: Introductionmentioning

confidence: 99%

A New Technique to Estimate the Degree of Polymerization of Insulation Paper Using Multiple Aging Parameters of Transformer Oil

Abu-Siada

et al. 2019

IEEE Access

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“…And in order to obtain its average life data, it is necessary to perform multiple irreversible failure tests which is of low practicability. In order to adapt to the random characteristics of the degradation process better, some scholars try to estimate the remaining life of the transformer based on machine learning methods: Li et al proposed the use of principal component analysis and linear regression to estimate the degree of polymerization of insulating oil paper to assess its insulation state indirectly [14]; Forouhari et al proposed an adaptive neuro-fuzzy logic model based on the tension value between insulating oil, furfural content and moisture content to estimate the life of oil-immersed transformers [15]. The above method can adapt to the deterioration of transformer performance better.…”

Section: Introductionmentioning

confidence: 99%

Prediction method of residual life of transformer oil‐paper insulation based on Wiener random process improved by strong tracking filter

Zhao

Chang

2022

IET Generation Trans & Dist

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In order to solve the problems of oil‐paper insulation deterioration of oil‐immersed power transformers, including the instability of performance degradation quantity at the early stage, non‐linear deterioration process and uncertainty of life prediction, an improved Wiener stochastic process based on strong tracking filter (STF) is proposed to predict the remaining life of transformer oil‐paper insulation in this paper. Firstly, in order to grasp the dynamic process from healthy state to current deterioration state, a deterioration model is established based on Wiener random process. Secondly, in order to improve the accuracy, the initial values of model parameters are estimated based on expectation maximization (EM) algorithm, then with the increase of monitoring data, model parameters are updated by using STF algorithm, and the probability density function of remaining life is derived to predict the remaining life of oil‐paper insulation. Finally, the accuracy of the proposed method is verified by using furfural content as performance degradation index and accelerated thermal aging experimental data. Compared with the Bayesian–Wiener algorithm, the MSE of the proposed algorithm is reduced to 0.2138 and its MAPE is reduced more than 4%. Besides, the proposed method has the advantages of strong robustness and low uncertainty of residual life prediction.

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Aging state assessment of transformer cellulosic paper insulation using multivariate chemical indicators

et al. 2021

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An effective principal component regression method for transformer life management based on indirect parameters

Cited by 4 publications

References 6 publications

A New Technique to Estimate the Degree of Polymerization of Insulation Paper Using Multiple Aging Parameters of Transformer Oil

A New Technique to Estimate the Degree of Polymerization of Insulation Paper Using Multiple Aging Parameters of Transformer Oil

Prediction method of residual life of transformer oil‐paper insulation based on Wiener random process improved by strong tracking filter

Aging state assessment of transformer cellulosic paper insulation using multivariate chemical indicators

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