Error Evaluation Method of Capacitive Voltage Transformer Based on Improved Principal Component Analysis

Abstract

The capacitive voltage transformer is an important data source for electricity trading and is easily influenced by environmental factors during operation, resulting in decreased metering accuracy. Because the voltage data does not satisfy the Gaussian distribution, the accuracy of the traditional principal component analysis method is poor when evaluating the error of capacitive voltage transformers. This paper proposes an improved principal component analysis method based on the local outlier factor. First, principal component analysis is utilized to separate the primary voltage fluctuation and error change. Then, a new statistic is established by using the local outlier factor instead of the traditional statistic as an evaluation standard, which reduces the missed diagnosis and misdiagnosis caused by the data distribution characteristics. The experimental results show that the improved method is better than the traditional method in terms of the detection rate of transformer anomaly detection, can effectively detect the abnormal change of transformer error and is more suitable for capacitive voltage transformers in operation.

Error state prediction method of current transformer based on improved VMD-BP neural network

“…Current transformer is widely used in power system. Due to the change of the actual application environment, its measurement error will be generated, which is mainly related to the temperature, electric field and load of the environment [4][5][6] . The above literature mainly studies the analysis of influencing factors based on the mathematical model of the transformer, but the data of influencing factors in actual operation is difficult to obtain, and the analyzed data is very different from the actual data.…”