Research on Data-Driven Self-Diagnosis for Measurement Errors in Capacitor Voltage Transformers

Capacitor voltage transformers (CVTs) are widely used in high-voltage power systems. Their main problem is poor stability in measurement errors. The existing approach of utilizing standard voltage transformers for regular offline error calibration is insufficient in real time and cannot meet the requirements of intelligent substations for online operations monitoring of key equipment. Therein, this article proposes a data-driven self-diagnosis method based on metering errors of operational CVTs. This method synchronously collects the secondary signals of three-phase CVT outputs. First, under the rigid constraints of three-phase symmetrical power system operations, the residual subspace parameters that characterize the measurement errors are extracted using principal component analysis (PCA). Then, bilateral threshold detection is combined with a multidimensional comprehensive assessment to determine the boundary conditions of the abnormal overall error state of the three-phase CVT and its deterioration direction. Finally, the coded multidimensional detection results are input into the learning vector quantized (LVQ) classifier to rapidly diagnose CVT measurement error deterioration. The experimental results show that the proposed method effectively identifies error fluctuations at 0.02% with a detection accuracy for the CVT amplitude error overshoot reaching 0.1%. The method can accurately locate the faulty phases and direction of error deterioration in real time, realizing the demand for self-diagnosing for CVT metrological errors at the 0.2-level.