Optimization of support vector machine for transformer core and winding looseness fault diagnosis based on improved grey wolf algorithm

Abstract

To diagnose transformer core and winding looseness faults more timely and effectively, a complementary ensemble empirical mode decomposition (CEEMD) and improved grey wolf optimization-based support vector machine (IGWO-SVM) method for transformer core and winding looseness fault diagnosis are proposed. Firstly, the vibration signal is decomposed into multiple intrinsic mode functions (IMFs) through CEEMD. Secondly, the energy entropy of different IMFs is calculated, and the energy entropy of different states is formed into a feature dataset. Then, the improved GWO-optimized SVM model is used to classify and recognize the feature dataset. Finally, we establish an experimental platform for experimental verification. The results show that the proposed method can accurately and effectively diagnose transformer core and winding looseness faults, and has a high diagnostic accuracy, which is at least 3.5% higher than the existing optimal diagnostic models. The proposed method provides a theoretical reference for the development of transformer fault diagnosis strategies.