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基于SCADA数据的风电机组关键载荷预测
引用本文:周士栋,薛扬,马晓晶,王文卓.基于SCADA数据的风电机组关键载荷预测[J].农业工程学报,2018,34(2):219-225.
作者姓名:周士栋  薛扬  马晓晶  王文卓
作者单位:中国电力科学研究院新能源与储能运行控制国家重点实验室,北京 100192,中国电力科学研究院新能源与储能运行控制国家重点实验室,北京 100192,中国电力科学研究院新能源与储能运行控制国家重点实验室,北京 100192,中国电力科学研究院新能源与储能运行控制国家重点实验室,北京 100192
基金项目:国家电网公司科技项目(NYB17201600300);国家科技支撑项目(2015BAA07B01)
摘    要:风电机组关键位置载荷预测对风电机组安全、经济运行具有重要意义。通过建立SCADA数据与载荷间的近似关系对风电机组关键位置载荷进行预测。采用BP神经网络建立SCADA数据和载荷的关系模型,利用SCADA数据与载荷间的相关性来筛选模型输入参量,采用试错法确定BP神经网络的层数与神经元数量。针对某2.5 MW风电机组的7处关键位置进行了载荷实测。研究表明,在不采用风速作为输入参量的情况下,模型的预测结果与实测结果具有良好的一致性,相对误差的均值在1.28%到15.6%之间,决定系数R2在0.951到0.882之间;与试错法选择输入参量相比,基于相关性计算的输入参量选择方法能够更高效地筛选出更多恰当的SCADA参量,从而进一步提高预测准确度。因此,基于BP神经网络建立SCADA数据与载荷的近似关系可作为风电机组关键位置载荷预测评估的有效手段。

关 键 词:风电机组  模型  神经网络  SCADA数据  参数筛选  疲劳等效载荷  载荷预测
收稿时间:2017/7/27 0:00:00
修稿时间:2017/12/4 0:00:00

Prediction of wind turbine key load based on SCADA data
Zhou Shidong,Xue Yang,Ma Xiaojing and Wang Wenzhuo.Prediction of wind turbine key load based on SCADA data[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(2):219-225.
Authors:Zhou Shidong  Xue Yang  Ma Xiaojing and Wang Wenzhuo
Institution:State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China,State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China,State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China and State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
Abstract:Abstract: The accurate prediction of wind turbine load at crucial positions is of great importance for wind turbines'' structural safety and scheme of operation and maintenance. Traditional load measurements using strain gauges are not suitable for long-term load monitoring because such measurements are labor-intensive, costly and time-consuming. Load simulation using software represented by GH bladed is not applicable to the evaluation of wind turbines'' load because of the failure to get comprehensive information of wind in real time. Therefore, it is necessary to develop an economical and feasible assessment method for wind turbine load. The prediction of wind turbine load may be achieved by establishing the relationship between SCADA data and load measurements. In view of the complicated load condition and coupling relationship among loads, BP (back propagation) neural network is used to construct the relationship between SCADA data and load indicators. The load indicator used in this study is fatigue equivalent load. The SCADA data used as input parameters are selected by calculating the correlation coefficients between SCADA data and load indicators. The numbers of hidden layers and neurons are determined by trial-and-error approach. Taking blade edgewise blending moment as the example, when the rest of parameters of neural network remain fixed, the predicted outputs by the BP neural network with 1 hidden layer and 6 neurons are the most accurate. For other BP neural network models, 1 hidden layer is used and the number of neurons is determined by trial-and-error approach. In order to prove the validity of the model, load measurements at 7 crucial locations of a 2.5 MW wind turbine are carried out. The accuracy of the relationship model is judged by comparing the predicted outputs by the model with the measured values. The coefficient of determination and the arithmetic mean value of relative errors between model outputs and measured values are used to characterize the accuracy. The arithmetic mean values of relative errors are between 1.28% and 15.6%, and the coefficients of determination are between 0.882 and 0.951, which show that the predictions are in good agreement with measurements. Therefore, establishing the approximate relationship between SCADA data and load indicators by BP neural networks can be used as an effective means to achieve the long-term monitoring and evaluation of wind turbine load at crucial locations. In the consideration of the fact that the wind farm seldom has the wind measurement mast and anemometer mounted in nacelle is often affected by the rotating blades, this study has abandoned the wind speed in the load prediction. Using BP neural network, hub rotating speed, pitch angle and active power are used to predict wind speed. The predicted values agree well with the measured value, which indicates that wind speed is not indispensable for the accurate load prediction. The study also shows that better prediction accuracy can be achieved by increasing reasonable input parameters. When there are many SCADA data that can be used as model input, the correlation coefficient approach is more efficient than the trial-and-error approach in selecting model inputs.
Keywords:wind turbines  models  neural network  SCADA data  parameter selection  fatigue equivalent load  load prediction
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