新投产风电场的短期风速预测模型建立

常规的风电场功率预测建模主要方法是将数值天气预报产生的气象要素输入基于历史scada数据建立统计模型,得到全场预报总功率。但是新投产的风电场没有历史scada数据,而风电场功率预测的准确性主要依赖于短期风速预报的精度。因此,为提高新投产风电场功率预测的准确性,短期风速预报的建立是基于数值气象预报的物理模型和统计模型相结合的方式。首先,通过数值气象模式输出风电场测风塔处轮毂高度层的气象要素；其次,通过建立神经网络模型和多元线性回归两种统计方法对模式输出数据进行修正；最后,对误差的来源进行分类分析。在江苏某风场的测试结果表明,较传统的方式,预测精度有了明显的提高,该方法能够消除数值气象预报的振幅偏差,但相位偏差仍是误差的主要来源。

Established of Short-term Wind Speed Prediction Model for New Wind Farm

The conventional wind power prediction method is primarily generated by numerical weather prediction based on historical scada build statistical model or physical model to get the total power prediction. As the new wind farm could not collect the historical scada data, and the accuracy of the wind farm power prediction relies on the accuracy of short-term wind speed prediction. Therefore, in order to improve the accuracy of short-term wind speed prediction for new wind farm. First of all, numerical weather model exported the meteorological element on turbine hub height layers; secondly, the output data are corrected by the establishment of neural network model and multiple linear regression models; finally, the sources of the error are classified and analyzed. The wind farm test in Jiangsu province results indicate that: this method can improve the accuracy of wind speed prediction significantly and eliminate the amplitude deviation of numerical weather prediction compared with traditional methods, but the phase deviation is still the main source of error.