【摘要】 为提高风电机组发电效率，增加经济收益，实现风电机组运行状态的在线监测，提出一种基于麻雀搜索算法优化高斯过程回归(SSA-GPR)模型的风电机组状态监测新方法。首先对数据采集与监视控制(SCADA)系统采集到的数据进行预处理分析，利用相关性分析完成模型的输入量选择；然后利用机组正常运行状态下的参数建立常态回归模型，实时计算重构误差，通过实时监测功率残差值是否超过动态故障阈值来判断机组状态。实例结果表明，所提方法的预测误差更小，并可以提前120 min实现机组异常运行状态预警。更多还原

【Abstract】 In order to improve the power generation efficiency and economic benefits of wind turbines, the online monitoring of the operating status of wind turbines is particularly important. A new method for monitoring the status of wind turbines based on sparrow search algorithm optimized Gaussian process(SSA-GPR) model is proposed. Firstly, the data collected from data collection and monitoring is preprocessed and analyzed. The correlation analysis is used to select the input of the model. A normal regression model using the parameters of the unit under normal operating conditions is established to calculate the reconstruction error in real-time. The unit status is determined by monitoring whether the predicted power residual exceeds the dynamic fault threshold in real-time. Through examples, it is shown that the proposed SSA-GPR model smaller prediction error and can achieve abnormal operation status warning of the unit 120 minutes in advance.更多还原