Online Detection Method for Capacitor Voltage Transformer Measurement Error Adapted to Nonstationary Random Processes

Capacitor voltage transformers (CVTs) are measurement devices widely used in high-voltage power grids, and the long-term stability of their measurement errors affects the safe operation of power systems. To address the problem of insufficient real-time periodic calibration using standard transformers, this article proposes an online detection method for CVT measurement error based on enhanced integrated principal component analysis (EIPCA). Considering that the secondary output voltage of CVTs is characterized by a nonstationary random distribution, the method combines the advantages of principal component analysis (PCA) and kernel principal component analysis (KPCA) to enhance its ability to process mixed linear and nonlinear data. It performs variational mode decomposition (VMD) of the linear residuals mapped in the cascade process to eliminate the modeling error caused by nonstationary data. Finally, the residual component characterizing the integrated error information of the CVT is separated from the primary voltage fluctuation, and the $T<^>{2}$ statistic is established as the index of measurement error detection. The experimental results show that the EIPCA-based online error detection method proposed in this article can effectively detect the 0.2-level error of the CVT, significantly improving detection performance compared with traditional PCA.