Optimization of LSSVM for Transformer Fault Diagnosis Based on KPCA and Seagull Algorithm

DOI: https://doi.org/10.62517/jiem.202403404

Author(s)

Xiaopeng Zhang*, Yuan Chai

Affiliation(s)

Inner Mongolia Power (Group) Co., Ltd, Hohhot Power Supply Branch, Hohhot, Inner Mongolia, China *Corresponding Author.

Abstract

Regarding the issue of redundant transformer features affecting feature extraction and fault detection. This article proposes a transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and Seagull Algorithm optimized Least Squares Support Vector Machine (LSSVM). Firstly, the transformer fault data is preprocessed using KPCA to reduce the correlation between features and remove redundant feature components, in order to improve the accuracy of the final fault diagnosis. Secondly, in response to the problem of the influence of LSSVM parameter settings on the fault classification of the model, it is proposed to use the seagull algorithm to optimize and determine the parameters of the LSSVM model. Finally, the LSSVM model optimized by the seagull algorithm is used for final fault diagnosis, and the experimental results are compared with other models. The fault diagnosis accuracy of the proposed method in this paper is 96.33%, which is higher than several other comparison methods, verifying the effectiveness of the proposed method.

Keywords

KPCA; Seagull Algorithm; LSSVM; Transformer; Fault Diagnosis

References

[1] Li Y, Ma L. Fault Diagnosis of Power Transformer Based on Improved Particle Swarm Optimization OS-ELM. Archives of Electrical Engineering, 2019. DOI: 10.24425/AEE.2019.125987. [2] Yan Y. Research on Transformer Condition Prediction Based on Gas Prediction and Fault Diagnosis. Energies, 2024, 17. DOI: 10.3390/en17164082. [3] Jiajie Z, Changjun A. Study on Fault Diagnosis of Power Transformer Based on Integration of Multiple Artificial Intelligence Technologies. Electronic Test, 2018. [4] Sun L, Xu M, Ren H, et al. Multi-point Grounding Fault Diagnosis and Temperature Field Coupling Analysis of Oil-immersed Transformer Core Based on Finite Element Simulation. Case Studies in Thermal Engineering, 2024, 55. DOI: 10.1016/j.csite.2024.104108. [5] Liu G, Gao L, Yu L, et al. Research on Transformer Fault Diagnosis Based on Voiceprint Signal. IOP Publishing Ltd, 2024. DOI: 10.1088/1742-6596/2774/1/012052. [6] Sahri Z B, Yusof R B. Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset. Journal of Computer & Communications, 2018, 02(9):22-31. DOI: 10.4236/jcc.2014.29004. [7] Zou D, Li Z, Quan H, et al. Transformer Fault Classification for Diagnosis Based on DGA and Deep Belief Network. Energy Reports, 2023, 9:250-256. DOI: 10.1016/j.egyr.2023.09.183. [8] Gong R K, Ma L, Zhao Y J, et al. Fault Diagnosis for Power Transformer Based on Quantum Neural Network Information Fusion. Power System Protection and Control, 2011, 39(23):79-84+88. DOI: 10.1016/B978-0-444-53599-3.10005-8. [9] Cheng J, Feng Z, Xiong Y. Transformer Fault Diagnosis Based on an Improved Sine Cosine Algorithm and BP Neural Network. Recent Advances in Electrical & Electronic Engineering, 2022. [10] Sun H, Fang J, Wang Z, et al. Fault Diagnosis Model of DGA for Power Transformer Based on FCM and SVM. Proceedings of SPIE - The International Society for Optical Engineering, 2008, 7127:71271N-71271N-7. DOI: 10.1117/12.806361. [11] Gan-Yun L, Hao-Zhong C, Li-Xin D, et al. Fault Diagnosis of Power Transformer Based on Multi-La yer SVM Classifier. Proceedings of Electric Power System and Automation, 2005. [12] Ding S, Cheng Z, Wu Q, et al. Transformer Fault Diagnosis Model Based on Discrete Hopfield Neural Network. International Conference on Applications and Techniques in Cyber Security and Intelligence, 2019. DOI: 10.1007/978-3-319-98776-7_152. [13] Lu W, Shi C, Fu H, et al. Research on Transformer Fault Diagnosis Based on ISOMAP and IChOA‐LSSVM.IET Electric Power Applications, 2023. DOI: 10.1049/elp2.12302. [14] Dai C, Huang K, Hu K, et al. Fault Diagnosis Approach of Traction Transformers in High-speed Railway Combining Kernel Principal Component Analysis with Random Forest. Iet Electrical Systems in Transportation, 2016, 6(3):202-206. DOI: 10.1049/iet-est.2015.0018. [15] Wang J, Shan Y, Fu H. Fault Diagnosis of Oil-immersed Transformer Based on Improved Seagull Optimization Algorithm to Optimize Wavelet Neural Network. 2022 9th International Forum on Electrical Engineering and Automation (IFEEA), 2022:1156-1162. DOI: 10.1109/IFEEA57288.2022.10038193.