基于正余弦麻雀搜索与峭度解卷积的发电机故障诊断  

Fault Diagnosis of Generator Based on SCSSA-MCKD

作　　者：杨禄铭 池献龙 施德华 YANG Luming;CHI Xianlong;SHI Dehua(Windey Energy Technology Group Co.,Ltd.,Hangzhou 310000,China)

机构地区：[1]运达能源科技集团股份有限公司,杭州310000

出　　处：《微特电机》2025年第4期39-42,共4页Small & Special Electrical Machines

摘　　要：提出了一种基于结合正余弦麻雀搜索算法和最大相关峭度解卷积的风电机组发电机故障诊断方法。该方法通过正余弦麻雀搜索算法优化最大相关峭度解卷积算法的参数,有效提取故障信号中的微弱特征。实验结果表明,该方法能够在复杂噪声环境下准确识别发电机的早期故障特征,为风电机组的故障预警和维护提供了有力的技术支持。This article proposed a fault diagnosis method for wind turbine generators based on the sine-cosine and Cauchy mutation sparrow search algorithm(SCSSA),and maximum correlation kurtosis deconvolution(MCKD).This method optimized the parameters of the MCKD algorithm through SCSSA,effectively extracting weak features from fault signals.The experimental results showed that this method can accurately identify the early fault characteristics of generators in complex noise environments,providing strong technical support for fault warning and maintenance of wind turbines.

关 键 词：发电机 故障诊断 正余弦 麻雀搜索算法 最大相关峭度解卷积 风电机组 

分 类 号：TM315[电气工程—电机]