Article ID Journal Published Year Pages File Type
6860673 International Journal of Electrical Power & Energy Systems 2013 10 Pages PDF
Abstract
In this paper, two new schemes are proposed for insulation failure detection in power transformer windings. In the first step, a real high frequency HV transformer winding is modeled based on the detailed model. Thereafter, a simulator is obtained for the insulation failure and is embedded in different locations of the winding in static and dynamic forms. In the first proposed scheme, the obtained ground current signals are used for feature selection based on the cross-correlation technique. Afterwards, a four-layered multiplier perceptron Artificial Neural Network (ANN) is trained using these features for fault detection. The accuracy rate of the ANN network was 84.33% for different faults. In the second scheme, k-Nearest Neighbors (k-NNs) is used as a regression method. The accuracy rate of this method reaches to 80.4%. Clearly, the first proposed scheme is able to detect the faults more accurately. Furthermore, in the first scheme, the accuracy rate was higher for the shunt faults in comparison to the series ones while the second scheme is more successful in detection of series failures.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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