基于SCADA和支持向量回归的风电机组状态在线评估方法

摘要

为提高风电机组并网运行的实时可靠性、优化机组维修策略、降低风力发电成本,有必要充分考虑风电机组各部件或子系统之间的相互作用和耦合关系.利用数据挖掘技术,建立了一个针对风电机组整体运行状态的在线评估模型.首先,分析了数据采集与监控(SCADA)报警系统的不足,提出了基于回归预测模型和SCADA报警系统相配合的鲁棒性更强的在线评估方案;其次,对评估方案中的回归预测模型进行了详细说明,建立了以SCADA系统的部分监测项目为输入量、以风电机组有功功率为输出量的基于支持向量回归(SVR)算法的回归预测模型.最后,利用某风电场的实测数据对所提出的在线评估模型进行了验证,结果证明了此方法的可行性.%In order to improve the real-time reliability of grid-connected wind turbines,optimize the maintenance strategy,and reduce the cost of wind power generation,it is necessary to consider the interaction and coupling between components or subsystems of a wind turbine.An online assessment model for the operation conditions of the whole wind turbine is established by data mining technology.Firstly,after analyzing the shortcomings of the supervisory control and data acquisition (SCADA) warning system of wind turbines,a more robust on-line assessment scheme is proposed based on the cooperation of a regression prediction model and the SCADA warning system.Secondly,the regression prediction model is described in detail that the support vector regression (SVR) algorithm is adopted.The inputs of SVR are part of the monitoring projects of the SCADA system,and the output of SVR is the active power of the wind turbine.Finally,measurement results of a wind farm are used to verify the proposed model.