Application of improved support vector machine model in fault diagnosis and prediction of power transformers

Abstract

Power transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K-means algorithm into the model and constructs a high-precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.