基于风速云模型相似日的短期风电功率预测方法

风电功率预测是解决风电不确定性影响的重要基础和必要手段,高比例风电并网条件下对每个时刻点的预测精度要求都将更为严格。训练样本是影响预测精度的关键因素之一,但由于实际天气系统的复杂多样性和类属模糊性,定向选择与调度时段内风况相似的训练样本对预测精度至关重要。因此,提出了基于云模型定向选取风速相似日数据作为训练样本的短期风电功率预测方法,能够对指定时段内风速随机性和模糊性特征进行学习和建模,通过对历史数据的定向筛选和精细化利用提升预测精度。首先,以日为单位建立历史风速的云模型数据库;然后,建立云模型相似度量化指标,用于判断与待预测时段风速云模型最为相似的历史数据序列,以此为训练样本建立短期风电功率预测模型。在实际预测中,每日根据天气预报信息滚动更新训练样本和预测模型,提高预测精度。最后,选择中国北方某风电场运行数据进行实例分析,结果证明了所提方法能够提高风电功率预测精度,具有一定的工程实用价值。

Short-term Wind Power Prediction Method Based on Wind Speed Cloud Model in Similar Day

Wind power prediction is the important basis and necessary means to solve the influence of wind power uncertainty. The requirement to prediction accuracy at each time point will be stricter with high-proportion wind power integration. Training samples are one of the key factors influencing the prediction accuracy. However, because of the complex diversity and ambiguity of the actual weather system, it is very important for improving the prediction accuracy to select the training samples that are similar to the wind conditions in the scheduling period. Therefore, a method based on wind speed cloud model is proposed to directionally select training samples with similar wind speed features in a given day. This method can improve the learning ability in capturing the randomness and fuzziness of wind speed in designated time period. By screening the historical data in an oriented way, the proposed method can enhance the prediction accuracy. Firstly, a historical database for daily wind speed cloud models is created. Then, the similarity of the wind speed cloud models is quantified, and this quantification index is used to select a sequence of historical data being similar to the wind in a predicted period. The selected samples will be used as training samples for short-term wind power prediction. In the practice, the training samples and prediction models are updated according to the weather prediction information so as to improve the prediction accuracy. The results of an example of a wind farm in Northern China are demonstrated, and verify that the proposed method can improve the accuracy of short-term wind power prediction and has practical value in engineering applications.