Detecting measurement error drifts of a capacitor voltage transformer on-line and its field application

Zhang, Chuanji; Li, Hongbin; Yang, Jian; Chen, Mianzhou; Jiao, Yang

doi:10.1088/1361-6501/ab199a

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…It has a significant impact on the measurement accuracy of CVT. Literature [17] proposes an identification method for CVT metrology error prediction by connecting the detection to the same phase on a data-driven basis. The authors effectively improved the sensitivity of the metrology error by performing a principal component analysis of the phase error.…”

Section: Introductionmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

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Section: Introductionmentioning

confidence: 99%

Voltage transformer metering error state prediction method based on GA-BP algorithm

Gao,

Zhao,

Jiang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…The capacitor voltage transformer (CVT) has the virtue of high insulation strength and is widely used in high-voltage power systems [1,2]. Compared with electromagnetic voltage transformers, CVT is more complex in structure and has added capacitor voltage divider and compensation reactor, because of which its measurement accuracy is affected by more factors [3][4][5][6][7][8]. The measurement error of the CVT affects the fair trade of electricity and the safe and economical operation of the power system [9].…”

Section: Introductionmentioning

confidence: 99%

Online detection of measurement error in three-phase CVT based on equivariant adaptive source separation

Zhang

Zhang²,

Fan³

et al. 2022

Meas. Sci. Technol.

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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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“…The capacitor voltage transformers (CVTs) have already been the most widely used voltage transformer in the high voltage (HV) and ultra-high voltage (UHV) power system [1][2][3] . The internal structure of CVTs is much more complicated compared with the conventional electromagnetic potential transformers (PTs), which leads to worse long-term stability and susceptibility to external electromagnetic interference 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Internal insulation condition identification for high-voltage capacitor voltage transformers based on possibilistic fuzzy clustering

Zhan

Chen

et al. 2020

Review of Scientific Instruments

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The internal insulation condition of capacitor voltage transformers (CVTs) is a key influence factor to its measurement performance and safe operation, but the internal insulation would be aged along with long-time operation and degraded influenced by environmental factors, once the insulation degradation grows, serious damages and even explosion may happen to CVTs, so it is necessary to monitor the internal insulation condition of CVTs, and the fault type and fault degree need to be identified. In this paper, a data-driven internal insulation condition identification method for CVTs is proposed. Both the amplitude and phase of the output voltage of CVTs are collected, then recognition models based on the combination of the output voltages and distribution topology of CVTs in substations are built, a possibilistic fuzzy clustering method is used to monitor the internal insulation condition of CVTs, different type and different degree of insulation faults could be identified effectively. Finally, the proposed method is verified by several cases, not only the preset typical faults in the method can be identified effectively, but also the faults beyond the preset faults could be diagnosed.

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Detecting measurement error drifts of a capacitor voltage transformer on-line and its field application

Cited by 8 publications

References 21 publications

Voltage transformer metering error state prediction method based on GA-BP algorithm

Voltage transformer metering error state prediction method based on GA-BP algorithm

Online detection of measurement error in three-phase CVT based on equivariant adaptive source separation

Internal insulation condition identification for high-voltage capacitor voltage transformers based on possibilistic fuzzy clustering

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