首页|基于SSA-GPR模型的风电机组运行状态监测

基于SSA-GPR模型的风电机组运行状态监测

扫码查看
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.
Wind Turbine Operation Status Monitoring Based on SSA-GPR Model
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.

SCADA datasparrow search algorithm(SSA)Gaussian process regression(GPR)status monitoringwind turbine

张杰、任康、马天、王伟璐、邢作霞、韩广明

展开 >

沈阳工业大学 电气工程学院,辽宁 沈阳 110870

中国大唐集团新能源股份有限公司,北京 100000

辽宁省风力发电技术重点实验室,辽宁 沈阳 110870

SCADA数据 麻雀搜索算法 高斯过程回归 状态监测 风电机组

辽宁省兴辽英才计划

XLYC2008005

2024

电器与能效管理技术
上海电器科学研究所(集团)有限公司

电器与能效管理技术

影响因子:0.394
ISSN:2095-8188
年,卷(期):2024.(4)
  • 21