A method for determining the abnormal state of current transformer accuracy based on statistical relative eigenvalue

Current transformer plays a key role in power system, and the abnormality determination of its accuracy state is very important to ensure the stable operation of power grid. This paper presents a method to determine the accuracy state anomaly of current transformer based on statistical relative eigenvalue. The method firstly collects the necessary operation data by monitoring the output current and voltage parameters of CT in real time. Then, statistical methods are used to analyze these data, especially focusing on the distribution characteristics and coefficient of variation of the data, so as to extract the core characteristic value of the current transformer. These statistical indicators help to identify normal and abnormal patterns in CT operation. The key is that the method defines a series of relative eigenvalues, which are calculated by comparing the statistical characteristics of the real-time data with the historical normal operation data of the current transformer. For example, by calculating the difference ratio between the current experimental data and the historical data mean, we can determine whether CT is in an abnormal state. Experimental findings indicate that our proposed technique, which is grounded in statistical relative eigenvalue analysis, outperforms traditional rule-based decision-making approaches in terms of accuracy and responsiveness. It not only accurately and promptly identifies abnormal conditions in current transformers but also offers a more dependable foundation for ensuring the stability of the power grid. Therefore, this method has important application value in the future health monitoring and fault warning of power system.