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.
DESIGN OF POWER GENERATION PREDICTION MODEL FOR WIND FARMS DURING SHORT-TERM HEAVY RAINFALL WEATHER
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.
short term heavy rainfallwind farmspower generationprediction modelseparable convolutionattention mechanism
NETL
NSTL
万方数据
