微型电脑应用2026，Vol.42Issue(2)：52-56,5.
基于改进递归神经网络算法的风电机组故障诊断研究
Research on Fault Diagnosis of Wind Turbine Based on Improved Recurrent Neural Network Algorithm
摘要
Abstract
The fault diagnosis of wind turbine based on classical recurrent neural network uses single value data,lacking the consideration of system uncertainty,which affects the accuracy of diagnosis.Therefore,an improved recurrent neural network algorithm based wind turbine fault diagnosis scheme is proposed.By introducing hierarchical k-means clustering and interval value data technology,the sensitivity of the fault diagnosis algorithm to system parameter changes and external uncertain inter-ference is reduced,so that it can maintain satisfactory robustness and diagnosis accuracy in a long operation process.The sys-tem model of the wind turbine is analyzed,and the hierarchical k-means clustering and interval value data technology are intro-duced to improve the classical recurrent neural network algorithm.The unit fault data of an offshore wind power generation ar-ea in Guangdong is used for verification.The proposed wind turbine fault diagnosis program can achieve a diagnostic accuracy of greater than 98％.It has higher diagnostic accuracy than classical recurrent neural network algorithm and other neural net-work algorithm of the same type.关键词
风电机组/故障诊断/改进递归神经网络/分层k-means聚类/区间值数据技术/诊断准确率Key words
wind turbine/fault diagnosis/improved recurrent neural network/hierarchical k-means clustering/interval value data technology/diagnostic accuracy分类
信息技术与安全科学引用本文复制引用
颜云,曾东..基于改进递归神经网络算法的风电机组故障诊断研究[J].微型电脑应用,2026,42(2):52-56,5.基金项目
广东省科技厅科技创新战略专项(2023A0505050789) （2023A0505050789）
