Online detection of measurement error in three-phase CVT based on equivariant adaptive source separation. (1st February 2023)

• Record Type: Journal Article
• Authors: Zhang, Mengjuan; Zhang, Zhu; Fan, Min; Ding, Lijian
• Is Part Of: Measurement science & technology. Volume 34:Number 2(2023)
• Publication Date: 2023-02-01
• Subjects: ICA -- CVT -- equivariant adaptation -- measurement error -- online detection; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aca114
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: As a key device for signal acquisition in power systems, the 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 equivariant adaptive separation via independence (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 independent component analysis (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%.