基于动态柯西蜂群算法优化支持向量机的风机叶片故障诊断

Subjects: Mechanical Engineering >> Other Disciplines of Mechanical Engineering submitted time 2018-06-15 Cooperative journals: 《电气工程学报》

王宇鹏 王致杰 刘 琦 徐莉莉 王 鸿 程亚丽

Abstract： In order to improve the recognition rate of wind turbine blade fault diagnosis, the nonlinear relationship between fault diagnosis and characteristic parameters of wind turbine blade is established by using support vector machine. In the Cauchy artificial bee colony algorithm, a dynamic Cauchy factor is introduced to dynamically adjust the search step in the optimization process of the colony, to improve the perturbation ability of the colony algorithm, and to avoid the colony into the local search. A dynamic support vector machine model for dynamic Cauchy bee colony optimization is established. The model is trained and diagnosed by four conditions of a wind farm in the south. The results show that the support vector machine model can be improved based on the dynamic Cauchy artificial bee colony algorithm. Wind turbine generator fault recognition rate, with a certain engineering reference significance.

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