摘要
对于变压器绝缘油含气(DGA)故障诊断识别准确率低问题,利用反向学习策略对黏菌算法(SMA)改进形成ISMA算法,提升全局寻优能力,并优化支持向量机(SVM),建立ISMA-SVM优化故障诊断模型,用样本集进行学习训练。将诊断识别结果与灰狼算法GWO-SVM和粒子群算法PSO-SVM优化模型进行对比,ISMA-SVM故障诊断识别准确率为93.3%,相比GWO-SVM、PSO-SVM分别提高了6.66百分点、10.66百分点。
To solve the problem of low recognition accuracy of transformer insulation oil gas fault diagnosis, the slime mold algorithm(SMA) is improved by the reverse learning strategy to form the improved slime mold algorithm(ISMA), thus to improve the global optimization ability and optimize the support vector machine(SVM).An ISMA-SVM optimized fault diagnosis model is established, and the sample set is used for learning and training.The diagnosis and recoginition results are compared with that of the greywolf algorithm(GWO-SVM) and the particle swarm optimization(PSO-SVM), it shows that the accuracy of the ISMA-SVM fault diagnosis and recognition is 93.3%, which is 6.66 and 10.66 percentage points higher than that of the GWO-SVM and PSO-SVM,respectively.
作者
杨昭
刘冲
杨嘉蕾
张灏
寇林
YANG Zhao;LIU Chong;YANG Jialei;ZHANG Hao;KOU Lin(Xi’an Thermal Power Research Institute Co.,Ltd.,Xi’an 710054,China)
出处
《热力发电》
CAS
CSCD
北大核心
2023年第1期165-169,共5页
Thermal Power Generation
基金
中国华能集团有限公司总部科技项目(HNKJ22-H36)。
关键词
绝缘油
含气故障
黏菌算法
支持向量机
诊断识别
insulating oil
gas fault
slime mold algorithm
support vector machine
diagnostic identification
