A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine. Issue 1 (1st February 2022)

• Record Type: Journal Article
• Authors: Wu, Yuhan; Sun, Xianbo; Zhang, Yi; Zhong, Xianjing; Cheng, Lei
• Is Part Of: Journal of physics. Volume 2203:Issue 1(2022)
• Publication Date: 2022-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2203/1/012067
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: An improved Squirrel Search Algorithm (ISSA) is proposed to overcome the shortcoming of Support Vector Machine (SVM) parameters is difficult to select. Firstly, an adaptive probability formula is used to balance the exploration and foraging ability of SSA, so as to improve the optimization performance of SSA. In order to test the optimization performance of ISSA, two test functions are used. And results show that ISSA has better optimization performance than SSA. Finally, based on ISSA-SVM and SVM, and 3-fold cross-Validation is used, the fault diagnosis of DGA data is carried out. The diagnostic results show that ISSA-SVM has the highest diagnosis performance.