Research on partial discharge pattern recognition of transformer based on KPCA and JS-SVM algorithm

Abstract Transformers are essential for the safe operation of power grids, and their failures can lead to significant risks. To improve the accuracy of partial discharge (PD) diagnosis in transformer bushings, this paper proposes a PD pattern recognition algorithm combining Kernel Principal Component Analysis (KPCA) and Artificial Jellyfish Search (JS)-optimized Support Vector Machine (SVM). The proposed algorithm offers the advantages of efficient dimensionality reduction, high classification accuracy, and fast optimization. KPCA is first used to reduce the dimensionality of high-frequency signals, improving data processing efficiency and lowering computational complexity. The JS algorithm is then employed to optimize SVM parameters, significantly enhancing classification and recognition performance. Experimental results show that the proposed algorithm achieves a higher recognition rate in PD defect diagnosis, with overall recognition rates improved by 3.74% and 2.5% compared to traditional SVM and BP neural network algorithms. This study provides a novel approach for early defect diagnosis in transformers and holds significant engineering application value.