计及转折性天气过程识别与检验的短期风电功率预测  

Short-Term Wind Power Prediction Considering Identification and Testing of Transitional Weather Processes

作　　者：王勃 冯双磊 刘晓琳 王钊 WANG Bo;FENG Shuanglei;LIU Xiaolin;WANG Zhao(State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems,China Electric Power Research Institute,Beijing 100912,China;Electric Power Meteorology State Grid Corporation Joint Laboratory,Beijing 100192,China)

机构地区：[1]新能源与储能运行控制国家重点实验室,中国电力科学研究院有限公司,北京100192 [2]电力气象国家电网有限公司联合实验室,北京100192

出　　处：《南方电网技术》2023年第12期52-62,共11页Southern Power System Technology

基　　金：中国电力科学研究院有限公司长线攻关项目(人工智能与物理机理相结合的新一代数值预报模式研究)(NY83-22-004)。

摘　　要：为提升数值天气预报(numerical weather prediction,NWP)对于短期风电功率预测的指导意义,计及转折性天气过程对功率预测的影响,提出了一种考虑转折性天气过程识别与检验的短期风电功率预测方法。对时间序列的NWP间隔15 min的样本采用基于门控循环单元(gated recurrent unit,GRU)分类器进行转折性天气过程的识别,基于识别结果,采用基于面向对象的诊断检验方法(method for object-based on diagnostic evaluation,MODE)对转折性天气过程的风速序列进行检验,挖掘NWP预报规律性。根据待预测时段的天气过程识别结果匹配天气过程,选用不同模型进行短期风电功率预测。将所提方法应用于中国吉林某风电场进行算例验证。结果表明转折性天气过程识别方法具有较高的识别准确率。各类天气过程条件下RMSE值平均降低2.77%,MAE值平均下降了2.46%,证明了该方法的有效性。In order to enhance the significance of numerical weather prediction(NWP)for short-term wind power prediction and take into account the influence of transitional weather processes on power prediction,a short-term wind power prediction method consider⁃ing identification and testing of transitional weather processes is proposed.The samples with NWP interval 15 min of the time series are identified using a gated recurrent unit(GRU)based classifier for transitional weather processes.Based on the identification results,the wind speed series of transitional weather processes are tested by method for object-based on diagnostic evaluation(MODE),and the NWP forecasting regularity is explored.Based on the results of weather process identification for the time period to be predicted,matching weather processes,different models are selected for short-term wind power prediction.The proposed method is applied to a wind farm in Jilin,China,for arithmetic validation.The results show that the transitional weather process identification method has a high identification accuracy.The average reductions of RMSE value by 2.77%and MAE value by 2.46%for all types of weather process conditions prove the effectiveness of the method.

关 键 词：转折性天气过程识别 MODE空间检验 NWP预报规律性挖掘 短期风电功率预测 

分 类 号：TM614[电气工程—电力系统及自动化]