Short-Term Prediction Method of Transformer Error Based on Intelligent Algorithm

Abstract

The error test station is an important part in the automatic verification assembly line of current transformer, and its accuracy and stability of working state are related to the evaluation of the accuracy level of the current transformer being verified. This paper aims to study the short-term prediction method of transformer error based on intelligent algorithm. In this paper, the risk early-warning method based on error verification test data is used for period verification. Aiming at the characteristics of limited, nonlinear and uncertain samples in risk early-warning problems, fuzzy support vector regression based on correlation analysis and other intelligent algorithms are used for analysis. The error test data are sampled according to time. In the prediction of this kind of data, the correlation between the prediction results and the recent data is strong, but the correlation between the prediction results and the early data is weak. Therefore, the weights from small to large are assigned to the sample data according to the distance of sampling time, which is used to determine the penalty factor of fuzzy support vector regression. According to the interaction of multi-dimensional features of samples, the correlation between each feature is analyzed, and the factors with strong correlation with the parameters to be predicted are screened out. On this basis, a prediction model is established by using fuzzy support vector regression, and the parameters of support vector regression are optimized by adaptive genetic algorithm to achieve the best prediction effect. The example analysis shows that this prediction model has better performance than other prediction models in this paper. Finally, the prediction results are combined with the risk assessment strategy to realize the risk warning of current transformer error test station. Based on the research of intelligent algorithm, the risk early warning system is implemented in this paper. The trial operation results show that the system can give the changing trend of the equipment state on the basis of the period check, which provides technical support for the early maintenance of the error test station of current transformer.