We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
基于多特征提取和麻雀搜索算法优化 XGBoost 的 变压器绕组松动诊断方法.
- Authors
马宏忠; 肖雨松; 颜锦; 孙永腾
- Abstract
In order to solve the problem of overlap and insufficient anti-interference ability under different load conditions in diagnosing transformer winding looseness using a single feature quantity, a vibration signal diagnosis method for transformer winding looseness based on kernel principal component analysis (KPCA) and extreme gradient boosting ( XGBoost) optimized by improved sparrow search algorithm (SSA) was proposed. Firstly, feature quantities in vibration signals were extracted from three dimensions: time domain, frequency domain, and entropy; Then, the feature quantity was dimensionally reduced through grid search optimized KPCA; Finally, a fault diagnosis model based on XGBoost was constructed and sparrow search algorithm was used to optimize the parameters for achieving accurate identification of transformer winding looseness faults under different currents. The experimental verification was conducted on a 110 kV transformer. The diagnosis results show that the extracted feature quantities can accurately reflect the fault characteristics, have stronger anti-interference ability, and the diagnostic accuracy rate of the diagnostic model is 99. 00% . Compared with other diagnostic algorithms, the accuracy and stability are higher, and have good recognition effects under different load conditions.
- Subjects
PRINCIPAL components analysis; FAULT diagnosis; SEARCH algorithms; DIAGNOSIS methods; ENTROPY
- Publication
Electric Machines & Control / Dianji Yu Kongzhi Xuebao, 2024, Vol 28, Issue 6, p87
- ISSN
1007-449X
- Publication type
Article
- DOI
10.15938/j.emc.2024.06.009