Prediction Method for Short-term Wind Power Based on Wind Farm Clusters
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Prediction Method for Short-term Wind Power Based on Wind Farm Clusters
Vol. 44, Issue 4, Pages: 1254-1260(2018)
作者机构:
1. 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室
2. 国网新疆电力有限公司
3. 国网黑龙江省电力有限公司
4. 国网山西省电力有限公司
作者简介:
基金信息:
DOI:
CLC:
Published Online:10 May 2018,
Published:2018
稿件说明:
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WANG Bo, LIU Chun, FENG Shuanglei, et al. Prediction Method for Short-term Wind Power Based on Wind Farm Clusters[J]. 2018, 44(4): 1254-1260.
DOI:
WANG Bo, LIU Chun, FENG Shuanglei, et al. Prediction Method for Short-term Wind Power Based on Wind Farm Clusters[J]. 2018, 44(4): 1254-1260.DOI:
Prediction Method for Short-term Wind Power Based on Wind Farm Clusters
摘要
风电功率预测的快速全覆盖对区域风电的优化调度意义重大
现有预测算法均无法解决这一问题。为此
采用模糊聚类分析实现对风电场出力特性的有效识别和风电场集群的合理划分
将主成分分析用于区域空间特征气象参数的提取
建立了基于集群划分的区域风电功率预测技术框架。算例结果表明
单个集群的预测精度略低于传统逐风电场建模的预测精度;随着预测范围的扩大
区域集群预测与传统预测方法的精度相当
但建模量明显减少。基于集群划分的短期风电功率预测方法可在保证预测精度的同时
显著提升建模效率。
Abstract
The fast-full coverage of regional wind farms of wind power prediction is very important for optimal scheduling of wind farms;the existing prediction algorithms are unable to solve this problem.Consequently
we adopted fuzzy clustering analyses to achieve an effective recognition of the wind power characteristics and a reasonable division of wind farm clusters.The principal component analysis was used to extract the region weather characteristic parameters
and a framework of wind power prediction method was established based on clusters.The results show that the prediction accuracy of a single cluster is slightly lower than the traditional method which models for each wind farm
with the expansion of the forecast range
the prediction accuracy will reach the level of the traditional method.The prediction method based on wind farm clusters can ensure the prediction accuracy
at the same time
achieve a significant efficiency improvement compared to the traditional method.
Uncertainty Evaluation of Wind Power Prediction Based on Monte-Carlo Method
Short-term Wind Power Prediction Based on Dynamic Cluster Division and BLSTM Deep Learning Method
考虑主轴疲劳载荷的虚拟同步双馈风电场分布式频率响应策略
Generalized Phase Compensation for PSS Design in Power Systems with DFIG Wind Turbines
Storage Capacity Optimization for Wind Farm Considering the Impact of Battery Lifetime and Control Strategy
Related Author
WANG Bo
LIU Chun
ZHANG Jun
FENG Shuanglei
LI Yingyi
GUO Feng
WANG Hongyu
LANG Jianxun
Related Institution
State Grid Zhejiang Electric Power Company
China Electric Power Research Institute
State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology
School of Electrical Engineering and Automation, Harbin Institute of Technology
School of Electrical Engineering, Shenyang University of Technology