Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于IAFSA-SVM的岸電箱斷路器故障診斷

來源:電工電氣發(fā)布時間:2019-08-16 09:16 瀏覽次數(shù):707
基于IAFSA-SVM的岸電箱斷路器故障診斷
 
楊奕飛1,焦文文1,何祖軍1,張發(fā)平2,郭江2
(1 江蘇科技大學 電子信息學院,江蘇 鎮(zhèn)江 212003;2 江蘇中智海洋工程裝備有限公司,江蘇 鎮(zhèn)江 212000)
 
    摘 要:斷路器的故障診斷對岸電系統(tǒng)的穩(wěn)定運行有重要意義。針對人工魚群算法和其他智能算法在優(yōu)化支持向量機參數(shù)時,存在易陷入局部最優(yōu)、泛化能力差等問題,通過自適應調整步長和引入全局隨機行為,提出基于改進人工魚群算法優(yōu)化支持向量機參數(shù)的故障診斷模型。將斷路器合閘線圈電流信號中的時間和電流信號作為特征量,采用改進人工魚群算法對支持向量機的參數(shù)尋優(yōu),以提升支持向量機的故障分類性能。仿真結果顯示,該算法在樣本數(shù)量小的情況下仍具有良好的分類性能,能夠準確對斷路器進行故障分類。
    關鍵詞:支持向量機;改進人工魚群算法;岸電箱;斷路器故障診斷
    中圖分類號:TM561     文獻標識碼:A     文章編號:1007-3175(2019)08-0057-05
 
Circuit Breaker Fault Diagnosis of Shore Connection Box Based on Improved
Artificial Fish Swarm Algorithm and Support Vector Machine
 
YANG Yi-fei1, JIAO Wen-wen1, HE Zu-jun1, ZHANG Fa-ping2, GUO Jiang2
(1 School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China;
2 Jiangsu Zhongzhi Marine Engineering Equipment Co., Ltd, Zhenjiang 212000, China)
 
    Abstract: The fault diagnosis of the circuit breaker is of great significance to the stable operation of the shore power system. For the artificial fish swarm algorithm and other intelligent algorithms, when optimizing the parameters of support vector machine, there were problems such as easy to fall into local optimum and poor generalization ability. By adaptively adjusting the step size and introducing global random behavior, this paper proposed an improved artificial fish swarm algorithm to optimize the fault diagnosis model of the support vector machine parameters. To improve the fault classification performance of the support vector machine, the time and current signals extracted from the current signals of the circuit breaker closing coil were used as the characteristic variables, and the improved artificial fish swarm algorithm was adopted to optimize the parameters of the support vector machine. The simulation results show that this algorithm can accurately judge the fault type of the circuit breaker with good classification performance under the conditions of small quantity of samples.
    Key words: support vector machine; improved artificial fish swarm algorithm; shore power box; circuit breaker fault diagnosis
 
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