Self-detection method for measurement error of capacitor voltage transformer considering conversion error

Abstract

The measurement error of capacitor voltage transformers (CVTs) has poor stability under the complex environment of substations. Conventionally, error detection is performed by regularly comparing the output of standard transformers, which lacks real-time performance. Moreover, CVTs are prone to operating in an out-of-tolerance state. Thus, this study first analyzes the basic principle of the CVT measurement error self-detection method based on principal component analysis under the constraints of three-phase symmetrical operating characteristics of a power system. Then, the impact of the signal conversion error of analog-to-digital conversion equipment on the self-detection results of CVT measurement errors in engineering applications is analyzed, and a multi-layer wavelet analysis signal denoising method is proposed to enhance the self-detection ability of CVT measurement errors. Finally, simulations show that the proposed method can identify an error change of 0.1%, meeting the self-detection requirements for the measurement error at 0.2 class.