An online detection method for capacitor voltage transformer with excessive measurement error based on multi-source heterogeneous data fusion.

Uncalibrated capacitive voltage transformers (CVTs) may significantly degrade measurement accuracy, because of the undetected excessive measurement error (ME). In this article, an online detection method is proposed which combines multi-source heterogeneous data composed of CVT measurements, acceptance test errors, and error limits. By measuring the same voltage with multiple CVTs, the monitoring statistics are generated and the statistic thresholds for the excessive ME detection are set according to the acceptance test errors and the error limits. To further ensure accuracy, the monitoring statistics and acceptance test errors for the CVTs surpassing the thresholds are used to estimate the ME. This estimation is then compared with the error limits as a cross-check to the detection result. Simulation shows that the difference between the ME estimated from the proposed method, and the actual ME is less than 0.01 % and the faulty CVT recognition accuracy exceeds 99%. • Detect the CVT with abnormal measurement in real-time without power outages. • The regulation for determining abnormal CVT is its error surpass the limits. • Can apply to all voltage transformers with different accuracy levels. • Experiments verify this method by Monte Carlo method. [ABSTRACT FROM AUTHOR]