[1]徐其丹,張新燕,李笑竹,等.混合布谷鳥算法在高壓斷路器故障診斷上的應用[J].高壓電器,2018,54(03):212-218.[doi:10.13296/j.1001-1609.hva.2018.03.031]
 XU Qidan,ZHANG Xinyan,LI Xiaozhu,et al.Application of Hybrid Cuckoo Algorithm in High Voltage Circuit Breaker Fault Diagnosis[J].High Voltage Apparatus,2018,54(03):212-218.[doi:10.13296/j.1001-1609.hva.2018.03.031]
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混合布谷鳥算法在高壓斷路器故障診斷上的應用()
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《高壓電器》[ISSN:1001-1609/CN:61-11271/TM]

卷:
第54卷
期數:
2018年03期
頁碼:
212-218
欄目:
技術討論
出版日期:
2018-03-15

文章信息/Info

Title:
Application of Hybrid Cuckoo Algorithm in High Voltage Circuit Breaker Fault Diagnosis
作者:
徐其丹張新燕李笑竹趙理威
新疆大學電氣工程學院, 烏魯木齊 830047
Author(s):
XU Qidan ZHANG Xinyan LI Xiaozhu ZHAO Liwei
College of Electrical Engineering, Xinjiang University, Urumqi 830047, China
關鍵詞:
高壓斷路器 故障診斷 支持向量機 混合布谷鳥算法
Keywords:
high voltage circuit breaker fault diagnosis support vector machine hybrid cuckoo algorithm
DOI:
10.13296/j.1001-1609.hva.2018.03.031
文獻標志碼:
A
摘要:
爲了更准確、快速地對高壓斷路器故障進行分類、診斷,提出一種基于混合布谷鳥算法優化的最小二乘支持向量機(LSSVM)的故障診斷方法。首先提取分合閘線圈的時間和電流特征量得到特征向量,再利用模擬退火算法(SA)與布谷鳥算法(CS)結合形成的混合布谷鳥算法(CS-SA),對支持向量機進行尋優,旨在得到具有最優參數支持向量機分類模型,提高診斷結果的准確性。最後,利用收集到的數據對該算法進行診斷驗證,結果表明利用混合布谷鳥算法優化後的LS-SVM得到的分類模型比常用的粒子群算法、遺傳算法、標准布谷鳥算法優化得到的模型准確率更高。
Abstract:
A diagnostic method based on least squares support vector machine(SVM) optimized by Hybrid cuckoo algorithm is proposed to accurately and quickly classification,diagnose faults in high voltage circuit breakers. First,the eigenvectors are obtained by extracting the time and current characteristic of the tripping and closing coil. Second,optimizing the support vector machine(SVM) use Hybrid cuckoo algorithm(CS-SA) which synthetized by Simulated annealing algorithm(SA)and cuckoo algorithm(CS)to get the support vector machine classification model with optimal parameter and improve the accuracy of diagnostic results. Finally,using the collected data to verify this algorithm,and,the experimentation shows that the accuracy rate of classification model obtained by LS-SVM optimized by CS-SA will be higher than optimized by particle swarm optimization,genetic algorithm or normal cuckoo algorithm.

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備注/Memo

備注/Memo:
收稿日期:2017- 09- 21; 修回日期:2017 - 11 - 24   基金項目:國家自然科學基金資助項目(51367015)。   Project Supported by the National Natural Science Foundation of China(51367015).徐其丹(1989—),男,碩士研究生,主要研究方向爲電機故障診斷。 張新燕(1965—),女,教授,博士生導師,主要從事新能源發電與故障診斷的研究工作(通信作者)。
更新日期/Last Update: 2018-02-25