基于COA-SVM变压器铁芯松动识别模型  

Based on the COA-SVM Transformer Core Loosening Identification Model

作　　者：熊威 龚康 张鑫[2] 蒲秋平 马辉[2] XIONG Wei;GONG Kang;ZHANG Xin;PU Qiuping;MA Hui(State Grid Yichang Power Supply Company,Yichang 443002,China;College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)

机构地区：[1]国网宜昌供电公司,湖北宜昌443002 [2]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《电工技术》2025年第5期141-144,共4页Electric Engineering

摘　　要：为了提高变压器铁芯松动识别准确度,提出黑猩猩算法(COA)优化支持向量机(SVM)变压器铁芯松动识别模型,先提取空载实验中变压器铁芯不同松动情况下的声纹,通过声信号提取梅尔倒谱系数(MFCC)并输入所提出的变压器铁芯松动识别模型,并把最终结果与遗传算法(GA)、粒子群算法(PSO)进行对比,结果验证了该模型在变压器铁芯松动识别上具有较高可行性。In order to improve the accuracy of transformer core loosening identification,this paper proposes the chimpanzee algorithm(COA)to optimize the support vector machine(SVM)transformer core loosening identification model,ex-tracts the acoustic patterns of transformer cores with different loosening conditions in no-load experiments,and extracts the mayer factor of cancellation coefficients(MFCC)through the acoustic signals and inputs it into the transformer core loosening identification model used in this paper,and the final results are compared with those of genetic algorithm(GA)and particle swarm algorithm(PSO).The result verifies that the model in this paper has high feasibility in transformer core loosening recognition.

关 键 词：变压器 铁芯松动 黑猩猩算法 支持向量机 

分 类 号：TM711[电气工程—电力系统及自动化]