State diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters

Cao, Chen; Xu, Jianyuan; Lin, Xin; Li, Xiaolong

doi:10.1049/iet-epa.2019.0564

Cited by 8 publications

(5 citation statements)

References 26 publications

(31 reference statements)

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“…The critical load and deformation pattern of winding structure can be provided by the buckling analysis. The winding structure has an incremental balance equation at steady state as shown in equation (1).…”

Section: B Deformation Analysis Of LV Windingmentioning

confidence: 99%

“…Power transformers are essential for the transfer of electrical energy, and the dependability of the power supply is directly impacted by the operational status of transformers [1]. Greater demands are placed on the dependable and stable operation of transformers as a result of the growth of power systems and the increasing capacity and voltage levels of transformers [2].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Transformer Winding Deformation and Its Locations With High Probability of Occurrence

et al. 2023

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Power transformer, as a crucial equipment for power transmission, frequently suffers winding deformation defects, owing to the impact of huge external short-circuit current in actual operation. To resist the rapid deterioration of the defects, online monitoring for winding states is imperative, depending on a thorough understanding of the deformation defects. Unfortunately, there are not much related research on the deformation form and location, as two key objects online diagnosed. In this paper, we concentrate on the analysis of winding deformation and investigation of its high-probability location to deeply understand the winding deformation. For the form of the deformation, a three-dimensional finite element model of the transformer is established to obtain the form and extent of the short-circuit impact force, and a buckling model of the low-voltage winding is designed to analyze the mechanism and form of winding deformation under the impact of huge force induced by short-circuit current. For the location of the deformation, the threedimensional simulation models are established to analyze the force characteristics of different winding structures in three locations (the conductor transposition area, the area with imbalanced amp-turns and the area with uneven circumferential distribution of force). Then we clarify the possibility of winding deformation in these areas, defined as high-probability locations. Finally, the analyzed results are found to be highly consistent with the actual winding deformation, demonstrating the correctness of the research methods and conclusions. Next stage, the results will provide effective theoretical support for online diagnosis of winding deformation and optimal design of winding structure.

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Section: B Deformation Analysis Of LV Windingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Transformer Winding Deformation and Its Locations With High Probability of Occurrence

et al. 2023

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“…From the analysis of the experimental comparison results in Figures 6-9, the following can be inferred: (1) The true value of the degradation curve at each prediction point completely lies in the 95% confidence interval of the FPCA degradation model, predict the main degradation trend of a transformer even if there is less known degradation information. (3) Because there is more known degradation information at prediction position 3, all models have good accuracy performance at this point; however, the FPCA degradation model still maintains an accuracy advantage of more than 3%.…”

Section: Accuracy Comparison Of Multiple Degradation Modelsmentioning

confidence: 99%

“…Good oil-paper insulation is a prerequisite for maintaining reliable operation of power transformers [1,2]. However, in the operation of transformers, the oil-paper insulation gradually degrades and fails [3,4].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

et al. 2022

IET Science Measure & Tech

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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.

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“…Vibration measurement is widely used in machine fault diagnosis, especially for condition monitoring and diagnosis of rotating machinery [17,18]. During the open and close process of HVCBs, many mechanical waves are generated by the interaction of internal mechanical components.…”

Section: Introductionmentioning

confidence: 99%

Fault identification of high voltage circuit breaker trip mechanism based on PSR and SVM

Ruan

Wang

Zhou³

et al. 2022

IET Generation Trans & Dist

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The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.

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State diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters

Cited by 8 publications

References 26 publications

Analysis of Transformer Winding Deformation and Its Locations With High Probability of Occurrence

Analysis of Transformer Winding Deformation and Its Locations With High Probability of Occurrence

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

Fault identification of high voltage circuit breaker trip mechanism based on PSR and SVM

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