Self-Detecting the Measurement Error of Electronic Voltage Transformer Based on Principal Component Analysis-Wavelet Packet Decomposition

作者：Binbin Li, Zhu Zhang, Lijian Ding · 发表于：IEEE Transactions on Instrumentation and Measurement · 年份：2021 · DOI：10.1109/tim.2021.3124065 · 被引用次数：17 · 研究领域：Fault Detection and Control Systems、Power Transformer Diagnostics and Insulation、Machine Fault Diagnosis Techniques

The poor long-term stability of measurement error is the main problem in the operation of an electronic voltage transformer (EVT). Prevalent methods of error detection involve calibrating it regularly with a standard voltage transformer, but this is not conducive to the timely detection of the degradation in the error of the EVT. In light of this, this paper proposes a method to self-detect error based on principal component-wavelet packet decomposition. A standard of comparison for error detection is first established based on the characteristics of three-phase symmetrical operation. Principal component analysis is then applied to the measurement data of the three-phase EVTs, and their error is mapped to the online process monitoring of squared prediction error (SPE) statistics. Furthermore, the SPE statistics are decomposed by a three-level wavelet packet to eliminate the influence of the time-varying characteristics of node imbalance on the results of detection. The results of experiments show that the proposed method can detect the measurement error with an accuracy of 0.2 class without a standard.