Application of ISMA-SVM method in monitoring of gas content in insulating oil

YANG Zhao; LIU Chong; YANG Jialei; ZHANG Hao; KOU Lin

doi:10.19666/j.rlfd.202208175

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Application of ISMA-SVM method in monitoring of gas content in insulating oil

Thermal Power Generation Vol. 52, Issue 1, Pages: 165-169(2023)

更新时间：2025-09-28

- Application of ISMA-SVM method in monitoring of gas content in insulating oil
- 作者机构：
  
  西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202208175
  CLC： TM40;TP18
- Published：2023
- 稿件说明：
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YANG Zhao, LIU Chong, YANG Jialei, et al. Application of ISMA-SVM method in monitoring of gas content in insulating oil[J]. Thermal Power Generation, 2023, 52(1): 165-169.
DOI：

YANG Zhao, LIU Chong, YANG Jialei, et al. Application of ISMA-SVM method in monitoring of gas content in insulating oil[J]. Thermal Power Generation, 2023, 52(1): 165-169. DOI： 10.19666/j.rlfd.202208175.

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。

Abstract

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.

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