Design of Electronic Voltage Transformer Error Pattern Recognition and Classification Algorithm Based on Data Mining

Deqian Kou, Yan Su


With the development of smart grids, electronic voltage transformer (EVT) has gradually entered the stage of large-scale applications. Accurately identifying errors in electronic voltage transformers is crucial for the stability of power systems. Strengthening the measurement accuracy of EVT is of great significance for the operation of power systems and measurement and protection devices. However, due to the limitations of traditional verification methods, there
are still challenges. To better improve the accuracy of transformer identification, a data-driven method for enhancing transformer error evaluation and prediction
was developed. Based on the low accuracy of traditional EVT error verification and the difficulty of monitoring, data mining technology is proposed for EVT error analysis and evaluation. Recursive principal component analysis is used to separate errors from EVT measurement data, and feature statistics are used to monitor its operating status. Then, regression analysis under support vector machines is added to predict errors for active error correction and better evaluation of its status. The evaluation of the transformer monitoring dataset shows that the classification accuracy of error detection of the proposed method exceeds 93%, and the deviation between the predicted error value and the actual error value is less than 0.05%. Compared with methods such as artificial neural networks and ARMA, the average error rate has been reduced by more than 18%. The accuracy and average accuracy of the algorithm proposed in the study exceeded 80%, with values of 96.23% and 85.12%, respectively. The average error of the ratio difference feature of the EVT is only 0.023, and the average error of the angle difference is less than 0.01, which is much smaller than the algorithm used for comparison. The application response time is less than 0.1 s and the evaluation threshold can better identify data anomalies, with high application accuracy. This method can effectively provide real-time evaluation tools for the operational status of electronic voltage transformers and more accurately and proactively identify transformer errors from conventional data. This study provides an important data-driven solution for improving power grid reliability.



data mining, electronic mutual sensor, PCA, SVM, Q-statistic, ratio difference characteristic

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