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A transformer winding deformation detection method based on the analysis of leakage inductance changes
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Abstract
The detection of transformer winding deformation caused by short-circuit current is of great significance to the realization of condition based maintenance. Considering the influence of environment and measurement errors, an online deformation detection method is proposed based on the analysis of leakage inductance changes. First, the operation expressions are derived on the basis of the equivalent circuit and the leakage inductance parameters are identified by the partial least squares regression algorithm. Second, the amount of the leakage inductance samples in a detection time window is determined using the Monte Carlo simulation thought, and then the samples in the confidence interval are obtained. Last, a criteria is built by the mean value changes of the leakage inductance samples and the winding deformation is detected. The online detection method considers the random fluctuation characteristics of the leakage inductance samples, adjust the threshold value automatically, and can quantify the change range to assess the severity. Based on the field data, the distribution of the leakage inductance samples is analyzed to obey the normal function approximately. Three deformation experiments are done by different sub-winding connections and the detection results verify the effectiveness of the proposed method
Walter de Gruyter GmbH
Title: A transformer winding deformation detection method based on the analysis of leakage inductance changes
Description:
Abstract
The detection of transformer winding deformation caused by short-circuit current is of great significance to the realization of condition based maintenance.
Considering the influence of environment and measurement errors, an online deformation detection method is proposed based on the analysis of leakage inductance changes.
First, the operation expressions are derived on the basis of the equivalent circuit and the leakage inductance parameters are identified by the partial least squares regression algorithm.
Second, the amount of the leakage inductance samples in a detection time window is determined using the Monte Carlo simulation thought, and then the samples in the confidence interval are obtained.
Last, a criteria is built by the mean value changes of the leakage inductance samples and the winding deformation is detected.
The online detection method considers the random fluctuation characteristics of the leakage inductance samples, adjust the threshold value automatically, and can quantify the change range to assess the severity.
Based on the field data, the distribution of the leakage inductance samples is analyzed to obey the normal function approximately.
Three deformation experiments are done by different sub-winding connections and the detection results verify the effectiveness of the proposed method.
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