基于多场景敏感气象因子优选及小样本学习与扩充的转折性天气日前风电功率预测  

Transitional-weather-considered Day-ahead Wind Power Forecasting Based on Multi-scene Sensitive Meteorological Factor Optimization and Few-shot Learning

作　　者：刘雅婷 杨明[1] 于一潇 李梦林 王勃 LIU Yating;YANG Ming;YU Yixiao;LI Menglin;WANG Bo(School of Electrical Engineering,Shandong University,Jinan 250061,China;China Electric Power Research Institute,Beijing 100192,China)

机构地区：[1]山东大学电气工程学院,济南250061 [2]中国电力科学研究院有限公司,北京100192

出　　处：《高电压技术》2023年第7期2972-2982,共11页High Voltage Engineering

基　　金：国家电网有限公司总部管理科技项目资助(面向转折性天气的风电功率预测预警技术研究)(4000-202155063A-0-0-00)。

摘　　要：随着风电渗透率的不断提高,如何对风电出力进行精准可靠预测是电力系统调度部门所面临的巨大挑战。当前,中国已具备较为成熟的风电功率预测解决方案,但其在转折性天气时段仍会出现极端预测偏差。同时,转折性天气数据集相对于常规功率预测数据集而言属于小样本,如何在小样本数据集下实现准确建模是精度提升的关键。针对上述问题,提出一种基于多场景敏感气象因子优选及小样本学习与扩充的转折性天气日前风电功率预测方法,该方法通过优选与构造多重场景转折性天气过程下的气象敏感特征,利用时间序列生成对抗网络对多场景气象敏感特征小样本集进行扩充,并采用长短期记忆神经网络对扩充后的敏感气象因子与风电观测出力序列之间的非线性关系进行建模。采用吉林某风电场数据进行算例验证,结果表明所提模型能够在一定程度上提高包含转折性天气的日前风电功率预测精度。With the continuous improvement of wind power penetration,how to accurately and reliably forecast wind power output is a great challenge for power system dispatching departments.At present,China has relatively mature schemes for wind power forecasting solutions,whereas extreme forecasting deviations still appear in the transitional weather period.At the same time,the transitional weather dataset is a small sample compared with the conventional prediction dataset.How to achieve accurate modeling under the small sample dataset is the key to improve the accuracy.To solve these problems,this paper proposes a transitional-weather-considered day-ahead wind power forecasting based on multi-scene sensitive meteorological factor optimization and few-shot learning.The method optimizes the meteorological sensitive features under the weather transitional process of multiple scenarios and expands the small sample set of multi-scene sensitive meteorological features by using time-series generative adversarial network.The nonlinear mapping relationship between the expanded sensitive meteorological factors and the output of wind power is modeled by using the long-short term memory neural network.The data of a wind farm in Jilin Province are used for example verification,and the results illustrate that the proposed model can partly improve the prediction accuracy of transitional-weather-considered day-ahead wind power.

关 键 词：转折性天气 敏感气象因子 小样本学习 生成对抗网络 长短期记忆网络 日前风电功率预测 

分 类 号：TM614[电气工程—电力系统及自动化]