基于FISSA-DBN模型的风电机组运行状态监测

Wind Turbine Operation Status Monitoring Based on FISSA-DBN Model

为提高风电机组运行效率,降低风电场运营成本,对风电机组运行状态监测显得尤为重要,提出一种基于数据采集与监控(supervisory control and data acquisition,简称SCADA)系统和萤火虫改进麻雀搜索算法优化深度置信网络(firefly improved sparrow search algorithm optimized deep belief network,简称FISSA-DBN)的风电机组状态监测新方法。首先,对SCADA数据进行预处理分析,并利用专家系统和皮尔逊相关系数分析,相关分析选取输入参数和输出参数;其次,利用预处理数据集建立基于FISSA-DBN的风电机组运行状态监测新模型,根据模型预测值和实际输出值之间的重构值误差,以及指数加权移动平均阈值(exponentially weighted moving average,简称EWMA)判断是否有异常;最后,以华东某风电场实际数据为例进行实例验证。结果表明,所提出方法的预警时间比实际记录时间最早可提前4 d多。同时,将所提出方法与其他方法进行对比,结果表明该方法预警时间提前,模型预测误差更小。

Monitoring the operation status of wind turbines is important for improving wind turbine operation efficiency and lowering wind farm operating costs. Thus, a new method of wind turbine condition monitoring is proposed, which is based on supervisory control and data acquisition(SCADA) and a firefly improved sparrow search algorithm optimized deep belief network(FISSA-DBN). The SCADA data is first preprocessed and analyzed, and the input and output parameters are chosen using an expert system and Pearson’s correlation analysis. Second, using the preprocessed data set, a new wind turbine operation monitoring model based on FISSA-DBN is established. The reconstructed value error between the predicted value and the actual output value of the model and the exponential weighted moving average(EWMA) are used to determine if there is an abnormality. Finally, actual data from a wind farm in Central China are used to validate the results. Verification results show that the early warning time of the proposed method can be more than four days earlier than the actual recording time. When compared to other methods, the proposed method provides earlier warning sand has a lower prediction error.