摘要
风电集群的整体功率预测对区域风电的优化调度具有重要意义,现有集群预测方法并未考虑集群内各风电场数值天气预报(numerical weather prediction, NWP)信息在时间序列上的差异性波动,并按此进行集群的合理划分。为此,提出了基于天气过程动态划分的风电集群短期功率预测方法。首先将96 h时间尺度的待预测样本均分成16份等时长的子样本;然后对每份子样本分别进行集群的聚类与划分;再依据划分结果构建各子样本所含子集群的训练集;最后通过双向长短期记忆(bidirectional long short-term memory,BLSTM)人工神经网络对各子集群进行功率预测。算例结果表明,所提方法在4 h超短期预测、24 h日前预测、96 h短期预测中相较统计升尺度法可分别提高1.69%、0.77%和0.59%的精度。论文研究可为风电集群划分和短期功率预测提供参考。
The wind power prediction for wind farm clusters is important for optimal scheduling of regional wind farms. The existing cluster prediction algorithms and cluster division methods do not consider the differential fluctuations among stations’ numerical weather prediction(NWP) along time scale, and the cluster isn’t divided reasonably. Consequently, we put forward a short-term wind power prediction method based on weather-process-dynamic wind farm cluster division. First, the 96-hour-time-scale prediction sample is equally divided into 16 sub-samples, followed by separate cluster division judging for each sub-sample. Then, the training set is organized for each sub-sample’s sub-cluster according to cluster division result. Finally, the bidirectional long-short term memory(BLSTM) neural network is used for power prediction for each sub-cluster. The results show that,using the proposed method, the prediction accuracy can increase by 1.69%, 0.77%, and 0.59% compared to using the statistical upscaling method in 4 h-urtra-short-term, 24 h-day-ahead, and 96 h-short-term wind power prediction, respectively. The research can provide a reference for the topic of wind farm cluster division and short-term power prediction.
作者
杨子民
彭小圣
郎建勋
王洪雨
王勃
刘纯
YANG Zimin;PENG Xiaosheng;LANG Jianxun;WANG Hongyu;WANG Bo;LIU Chun(State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;State Key Laboratory of Operation and Control of Renenable Energy&Storage Systems,China Electric Power Research Institute,Beijing 100192,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2021年第4期1195-1203,共9页
High Voltage Engineering
基金
国家重点研发计划(促进可再生能源消纳的风电/光伏发电功率预测技术及应用)(2018YFB0904200)。
关键词
风电场
功率预测
集群划分
聚类分析
深度学习
神经网络
wind farm
power prediction
cluster division
cluster analysis
deep learning
neural network
