基于GAWOA优化ELM的风机变流器故障诊断  

Fault Diagnosis of Wind Turbine Converter Based on GAWOA-ELM

作　　者：许如远 马萍[1] XU Ruyuan;MA Ping(School of Electrical Engineering,Xinjiang University,Urumqi Xinjiang 830017,China)

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830017

出　　处：《新疆大学学报（自然科学版）（中英文）》2022年第3期377-384,共8页Journal of Xinjiang University(Natural Science Edition in Chinese and English)

基　　金：国家自然科学基金(52065064,51967019);天山雪松计划(2020XS03);天山青年计划(2020Q066).

摘　　要：为提高双馈异步风力发电机变流器的开路故障诊断准确率,提出一种基于全局自适应鲸鱼优化算法优化极限学习机的故障诊断方法.首先,建立双馈异步风力发电机(DFIG)并网模型,采集网侧变流器故障状态下的三相线电压信号.其次,对采集的电压信号进行快速傅里叶变换,再将三相线电压的不同谐波分量的频率幅值和直流分量重构成特征向量,为去除部分冗余特征,利用邻域保持投影对特征向量进行降维.最后,利用全局自适应鲸鱼算法优化的极限学习机(GAWOA-ELM)对变流器故障进行诊断.使用不同方法对不同信噪比下的变流器故障进行诊断分析,验证了本文所提方法的有效性和鲁棒性.In order to improve the accuracy of the open-circuit fault diagnosis of the double-fed asynchronous wind turbine converter,a fault diagnosis method based on the global adaptive whale optimization algorithm to optimize the extreme learning machine is proposed.Firstly,establish a grid-connected model of doubly-fed induction generator(DFIG),and collect the three-phase line voltage signal under the fault state of the grid-side converter.Secondly,fast Fourier transform is performed on the collected voltage signal,and then the frequency amplitude of the different harmonic components of the three-phase line voltage and the DC component are reconstructed into a feature vector.In order to remove some redundant features,use the neighborhood to maintain the projection pair,the feature vector is dimensionally reduced.Finally,an extreme learning machine optimized by the global adaptive whale optimization algorithm(GAWOA-ELM) is used to diagnose the faults of the converter.Different methods are used to diagnose and analyze converter faults under different signal-to-noise ratios,verifying the effectiveness and robustness of the method proposed in this paper.

关 键 词：双馈感应电机 变流器 故障诊断 全局自适应鲸鱼算法 极限学习机 

分 类 号：TM46[电气工程—电器]