为确保发电场正常供应电力,设计短时强降雨天气风电场发电功率预测模型,提升发电功率预测效果。通过欧式距离与角度原则扩充短时强降雨天气小样本;利用改进深度可分离卷积算法,在正常天气样本内,提取气象-功率时空特征,并输入长短期记忆网络内,建立正常天气风电场发电功率基准值预测模型,得到发电功率基准值;在Transformer算法内,输入扩充样本,建立短时强降雨天气下发电功率损失值预测模型;利用基于注意力机制的Sequence to Sequence网络,结合扩样本,构造发电功率损失时间点预判模型,结合损失值预测模型,得到最终发电功率损失值;利用基准值减去损失值,得到短时强降雨天气下风电场发电功率预测结果。实验证明：该模型可有效扩充短时强降雨天气小样本;该方法可精准预判发电功率损失时间点,得到发电功率损失值,完成发电功率预测;不同风速下,该模型发电功率预测的关键失误指数与偏移程度均较低,即发电功率预测精度较高。
Abstract
To ensure the normal power supply of the power plant, a short-term heavy rainfall weather wind farm power generation prediction model is designed to improve the power generation prediction effect. Expanding small samples of short-term heavy rainfall weather through the principles of Euclidean distance and angle; using the improved depth separable convolution algorithm, the spatio-temporal characteristics of weather power are extracted from the normal weather samples, and input into the long-term and short-term memory network to establish the prediction model of normal weather wind farm power generation reference value, and obtain the power generation reference value; in the Transformer algorithm, input an expanded sample to establish a prediction model for power generation loss value under short-term heavy rainfall weather; using a Sequence to Sequence network based on attention mechanism, combined with expanding samples, a prediction model for the time point of power generation loss is constructed. Combined with a loss value prediction model, the final power generation loss value is obtained; subtract the loss value from the reference value to obtain the prediction results of wind farm power generation under short-term heavy rainfall weather. The experiment proves that the model can effectively expand the small samples of short-term heavy rainfall weather; this method can accurately predict the time point of power generation loss, obtain the value of power generation loss, and complete power generation prediction; under different wind speeds, the key error index and deviation degree of the model's power generation prediction are relatively low, indicating a higher accuracy of power generation prediction.
关键词
短时强降雨 /
风电场 /
发电功率 /
预测模型 /
可分离卷积 /
注意力机制
Key words
short term heavy rainfall /
wind farms /
power generation /
prediction model /
separable convolution /
attention mechanism
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基金
广西电网公司生产技改项目（046000GS62210006）
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