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

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基于風(fēng)電故障機(jī)組篩選的齒輪箱故障診斷研究

來源:電工電氣發(fā)布時(shí)間:2019-09-19 10:19 瀏覽次數(shù):718
基于風(fēng)電故障機(jī)組篩選的齒輪箱故障診斷研究
 
石慧1,趙巧娥2
(1 太原市康培園林綠化工程有限公司,山西 太原 030025;2 山西大學(xué) 電力工程系,山西 太原 030006)
 
    摘 要:利用改進(jìn)粒子群優(yōu)化模糊C均值聚類算法對雙饋風(fēng)力發(fā)電機(jī)組群進(jìn)行故障機(jī)組分類,并提出基于改進(jìn)粒子群優(yōu)化的模糊核聚類算法對雙饋風(fēng)力發(fā)電機(jī)組齒輪箱的已知以及未知故障進(jìn)行診斷分類。通過分析實(shí)際風(fēng)電場采集得來的齒輪箱振動數(shù)據(jù),驗(yàn)證所提方法不僅可以準(zhǔn)確快速地判斷出故障機(jī)組,而且還可以進(jìn)一步對發(fā)生的已知故障以及未知故障進(jìn)行一個很好的診斷。
    關(guān)鍵詞:雙饋風(fēng)力發(fā)電機(jī)組;模糊C 均值聚類算法;模糊核聚類算法;改進(jìn)粒子群優(yōu)化算法;故障診斷
     中圖分類號:TM614     文獻(xiàn)標(biāo)識碼:A     文章編號:1007-3175(2019)09-0007-05
 
Gearbox Fault Diagnosis Based on Wind Turbine Fault Unit Selection
 
SHI hui1, ZHAO Qiao-e2
(1 Taiyuan City Kangpei Garden Greenery Engineering Limited Company, Taiyuan 030025, China;
2 Department of Electric Power Engineering, Shanxi University, Taiyuan 030006, China)
 
    Abstract: This paper used the improved particle swarm optimization fuzzy C means clustering algorithm to classify the fault units of doubly fed wind turbines, and presented a fuzzy kernel clustering algorithm based on the improved particle swarm optimization (PSO) for the diagnosis and classification of the known and unknown faults of the gear box of the doubly fed wind turbine. By analyzing the vibration data of the gear box collected by the actual wind farm, it is proved that the proposed method can not only judge the fault unit accurately and quickly, but also further diagnose the known fault and the unknown fault.
    Key words: doubly fed induction generator (DFIG); fuzzy C means clustering algorithm; fuzzy kernel clustering algorithm; improved particle swarm optimization algorithm; fault diagnosis
 
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