排灌机械工程学报2026，Vol.44Issue(4)：397-404,8.DOI:10.3969/j.issn.1674－8530.24.0172
基于FCSE结合BGWO－LSSVM的风电机组故障诊断
Wind turbine fault diagnosis based on FCSE combined with BGWO－LSSVM
摘要
Abstract
With increasing global attention on the development of wind energy resources,ensuring the safe and stable operation of wind turbines has become crucial for the efficient utilization.However,ex-tracting information from complex vibration signals units for fault feature characterization faces many challenges.For this reason,a feature extraction method based on the fractional order idea was proposed to improve the feature extraction capability of wind turbine unit signals under different states.Conside-ring the fractional-order characteristics of signals,the extraction effect of complex signal features was improved by introducing the fractional-order cosine similarity entropy(FCSE)method.Meanwhile,the multi-strategy improved grey wolf optimization algorithm(BGWO)was combined to find the optimal parameter combinations of least squares support vector machine(LSSVM)for efficient feature classifi-cation.Noise with a signal-to-noise ratio of 3 dB was added to the original signal,and FCSE was com-pared and analyzed with two traditional feature entropy to evaluate its anti-noise performance.The si-mulation results show that the feature extraction ability of FCSE on a given dataset is significantly better than that of the other two methods,while the classification accuracy of the proposed fault diagnosis sys-tem on the laboratory dataset and actual vibration signals reaches 100.0%and 97.50%,respectively,proving its validity and reliability in practical applications.关键词
风电机组/改进的灰狼优化算法/分数阶余弦相似熵/最小二乘支持向量机/故障诊断Key words
wind turbine unit/improved grey wolf optimization algorithm/fractional cosine similarity entropy/least squares support vector machine/fault diagnosis分类
信息技术与安全科学引用本文复制引用
王昕,庄加利,李法社,刘飞,宋颂伟..基于FCSE结合BGWO－LSSVM的风电机组故障诊断[J].排灌机械工程学报,2026,44(4):397-404,8.基金项目
广西2021年市场化并网多能互补一体化项目(2110－450000－04－01－384989) （2110－450000－04－01－384989）
