摘要
海上风电机组齿轮箱运行状态的有效监测和及时预警对海上风机运维工作具有重要意义。为此,提出一种基于门控循环单元(Gated Recurrent Unit,GRU)和注意力机制的海上风电机组齿轮箱状态监测方法。在训练阶段,通过注意力机制自动提取海上风电SCADA数据集输入参量与目标建模参量间的关联关系,同时采用GRU网络提取数据间的时序依赖关系,进而建立风电机组齿轮箱的正常行为模型。在测试阶段,采用指数加权移动平均值(Exponentially Weighted Moving-Average,EWMA)控制图对目标建模参量实际值和模型预测值间的输出残差进行监控,实现海上风机齿轮箱运行状态的实时监测和预警。最后基于东海大桥海上风电场真实数据对所提方法的有效性和优越性进行了验证。结果表明:所提方法对故障和正常运行条件下的海上风电机组齿轮箱状态均可进行有效监测,且相比现有陆上风机状态监测方法具有更高的精度和可解释性,并能更早地揭示故障趋势。
The effective monitoring and early warning of an offshore Wind Turbine(WT)gearbox operating state is of great significance to its operation and maintenance.Therefore,a Condition Monitoring(CM)method for an offshore WT gearbox based on Gated Recurrent Unit(GRU)and attention mechanism is proposed.In the training phase,the attention mechanism is introduced to automatically extract the correlation between the input features and the target modeling feature of an offshore WT Supervisory Control and Data Acquisition(SCADA)dataset.The GRU network is used to effectively extract the temporal relationships between the data,thereby establishing the normal behavior model of the WT gearbox.In the test phase,an Exponentially Weighted Moving-Average(EWMA)control chart is applied to monitor the output residuals between the predicted value and the true value of the target modeling feature,so as to realize real-time monitoring and early warning of the operating status of the gearbox.Finally,the validity and superiority of the proposed method are verified based on real data from the Donghai Bridge offshore wind farm.The results show that the proposed method can effectively monitor the gearbox state under both fault and normal operating conditions,and has higher accuracy and interpretability than the existing onshore monitoring method,and can reveal early failure trends earlier.
作者
苏向敬
山衍浩
周汶鑫
符杨
SU Xiangjing;SHAN Yanhao;ZHOU Wenxin;FU Yang(College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《电力系统保护与控制》
CSCD
北大核心
2021年第24期141-149,共9页
Power System Protection and Control
基金
国家自然科学基金面上项目资助(61873159)
上海市科委项目资助(18020500700)。
