| 引用本文: | 周晓华,冯雨辰,陈 磊,等.改进秃鹰搜索算法优化SVM的变压器故障诊断研究[J].电力系统保护与控制,2023,51(8):118-126. |
| ZHOU Xiaohua,FENG Yuchen,CHEN Lei,et al.Transformer fault diagnosis based on SVM optimized by the improved bald eagle search algorithm[J].Power System Protection and Control,2023,51(8):118-126 |
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| 摘要: |
| 支持向量机(support vector machine, SVM)用于变压器故障诊断时，其核函数参数g和c的最优值难以根据人工经验选取，故障诊断准确率较低；而秃鹰搜索算法(bald eagle search, BES)存在易陷入局部最优和收敛精度低的缺陷。针对以上问题，提出一种改进秃鹰搜索算法(Ct-GBES)优化SVM参数g和c的变压器故障诊断模型。采用tent混沌映射、自适应t-分布及动态选择、黄金正弦算法对BES的3个阶段进行改进和优化，以提高算法的收敛速度和搜索能力。通过与原始BES、布谷鸟算法(cuckoo search, CS)和萤火虫算法(firefly algorithm, FA)的寻优对比测试，验证了Ct-GBES算法的优越性。将Ct-GBES-SVM模型与SVM、FA-SVM、CS-SVM模型进行故障诊断实验对比，并与BES-SVM模型进行稳定性实验对比。结果表明，所提模型准确率更高、稳定性更好、运行时间更短，其故障诊断效果更好。 |
| 关键词: 变压器故障诊断 秃鹰搜索算法 混沌映射 自适应t-分布 黄金正弦算法 支持向量机 |
| DOI：10.19783/j.cnki.pspc.221236 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(61563006)；广西自然科学基金重点项目资助( 2020GXNSFDA238011)；广东省基础与应用基础研究基金项目资助(2021B1515420003) |
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| Transformer fault diagnosis based on SVM optimized by the improved bald eagle search algorithm |
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ZHOU Xiaohua1, FENG Yuchen1, CHEN Lei2, LUO Wenguang3, LIU Shengyong1
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1. School of Automation, Guangxi University of Science and Technology, Liuzhou 545616, China; 2. Guangxi Liuzhou
Special Transformer Co., Ltd., Liuzhou 545006, China; 3. School of Computer Science and Technology,
Guangxi University of Science and Technology, Liuzhou 545006, China
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| Abstract: |
| When a support vector machine (SVM) is applied to transformer fault diagnosis, the optimum values of its kernel function parameters g and c are difficult to select from manual experience, and the accuracy of fault diagnosis is low. The bald eagle search (BES) algorithm has the defects of easily falling into a local optimum and low convergence accuracy. Given these problems, a transformer fault diagnosis model based on an improved bald eagle search algorithm (Ct-GBES) is proposed to optimize the parameters g and c of the SVM. To improve the convergence speed and search ability of the algorithm, the three stages of the BES algorithm are improved and optimized using a tent chaotic map, adaptive t-distribution and dynamic selection, and the golden sine algorithm. The superiority of the Ct-GBES algorithm is verified by comparing it with the optimization tests of the original BES, the cuckoo algorithm (CS) and the firefly algorithm (FA). The Ct-GBES-SVM model is compared with the SVM, FA-SVM, CS-SVM models in fault diagnosis experiments, and is compared with the BES-SVM model in stability experiments. The results show that the proposed model has higher accuracy, better stability, shorter running time and offers better fault diagnosis. |
| Key words: transformer fault diagnosis bald eagle search algorithm chaotic map adaptive t-distribution golden sine algorithm support vector machine |
