Pattern Recognition and Algorithm Improvement for Error Analysis in Voltage Transformer Low Voltage Testers

Abstract

To ensure the fairness and accuracy of electric energy measurement, it is necessary to carry out on-site calibration of voltage transformers in operation according to the cycle, and due to a large amount of measurement workload and many dangerous factors, a low-voltage tester is used at this stage to carry out the measurement work. This paper explores the basic method of low-voltage testing, sorting out the steps for the use of low-voltage testers in voltage transformers and proposing a resonance-based CVT analysis method to assist in the voltage transformer measurement operation of low-voltage measuring instruments. When performing the calculation of the error on the low-voltage side, the low-voltage extrapolation method is used to extrapolate the measurement error on the high-voltage side of the voltage transformer, and on this basis, a neural network architecture is proposed to suggest an improvement strategy for the existing algorithm. Using the method proposed in this paper, the voltage transformer low voltage test error analysis, the method proposed in this paper is consistent with the error trend measured by the overall error test method, and the maximum value of the error deviation is 0.0436%, which is not more than 0.05%, and is smaller than the error limit of the test article. At the same time, in different modes of voltage transformer error characterization, with the ambient temperature from -35 ° C to +22 ° C in the process of change, the ratio difference of voltage transformer from 0.183% change to 0.152%, phase difference from 4.18′ change to 4.68′, the whole process, the ratio, phase difference is not exceeded, the error measurement effect is good.

Keywords

• voltage transformer
• resonant cvt analysis
• low voltage test
• neural network
• error analysis
• 68t05