As a key device for signal acquisition in power systems, capacitor voltage transformer (CVT) has been widely used in high-voltage power grids with a capacity of 110 kV and above. However, the long-term stability of error in its measurements is poor and requires effective detection work. The conventional method of regular offline detection has the problems of power failure and operation difficulty. An online method for identifying errors in measurements of the CVT based on equivariant adaptive source separation is proposed in this paper. Under normal operation, the secondary output information of three-phase CVT is linearly correlated, and EASI is used to analyze the correlation of data matrix. By constructing the main space and residual space, the primary voltage fluctuation is separated from the metering error. According to the separated signal characterizing the measurement error information, the standard quantity is established to realize the online detection of measurement error of three-phase CVT. In view of the non-stationary time-varying characteristics of power system operation, the data model is continuously updated according to successively acquired, which effectively improves the accuracy of the state assessment of the three-phase CVT. The simulation and experimental results show that the proposed method outperforms the ICA, which can well meet the requirements of 0.2-level accuracy and have an evaluable change in amplitude that is better than 0.05%.
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