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电网技术  2018, Vol. 42 Issue (8): 2373-2381    DOI: 10.13335/j.1000-3673.pst.2018.0137
  国家重点研发计划 本期目录 | 过刊浏览 | 高级检索 |
基于递归量化分析的COA-SVR短期风速混合预测模型
潘超1, 谭启德1, 蔡国伟1, 张子信2
1.东北电力大学 电气工程学院,吉林省 吉林市 132012;
2.国网辽宁省电力有限公司 经济技术研究院,辽宁省 沈阳市 110015
Hybrid Short-Term Wind Speed Prediction Model by COA-SVR Based on Recursive Quantitative Analysis
PAN Chao1, TAN Qide1, CAI Guowei1, ZHANG Zixin2
1. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin Province, China;
2. Economic and Technological Research Institute, State Grid Liaoning Electric Power Company,Shenyang 110015, Liaoning Province, China
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摘要 风电规模化并网对电力系统规划与运行的影响日益严重。针对风速波动的随机性和不确定性特点,提出了一种递归量化分析方法。根据风速序列的递归率及确定性组成的联合指标对风速系列的可预测性进行量化评价,利用联合指标优选的重构参数对风速序列进行相空间重构,综合嵌入维度与延迟时间获取预测模型的最佳输入集,构建基于杜鹃优化的支持向量回归模型对风速进行预测。结合实际风电场风速数据,通过对比不同算法的预测结果,对所提方法的准确性和有效性进行验证,同时利用假设检验评估所建预测模型的泛化能力。
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潘超
谭启德
蔡国伟
张子信
关键词:  风速预测  递归量化分析  相空间重构  支持向量回归    
Abstract: As scale of wind power integration continuously increases, its impact on safe and stable operation of power systems becomes increasingly serious. Aiming at randomness and uncertainty of wind speed fluctuation, a recursive quantitative analysis method is proposed in this paper. Firstly, predictability of wind speed series is quantitatively analyzed according to joint index combined by recurrent rate and determinism. The wind speed series is reconstructed using the parameters optimized with joint index. And the best input sets of prediction model are obtained by embedding dimension and delay time. Then the support vector regression (SVR) model optimized with cuckoo optimization algorithm (COA) is used to predict wind speed. According to actual wind speed data, accurateness and effectiveness of the hybrid prediction model are verified by comparing prediction results of different algorithms. Finally, hypothesis test is performed to evaluate generalization ability of the hybrid prediction model.
Key words:  wind speed prediction    recursive quantitative analysis    phase space reconstruction    SVR
收稿日期:  2018-01-17                     发布日期:  2018-08-08      期的出版日期:  2018-08-05
ZTFLH:  TM73  
基金资助: 国家重点研发计划项目(2016YFB0900100); 国家自然科学基金项目(51507028)
作者简介:  潘超(1981),男,博士,副教授,研究方向为新能源发电技术,E-mail:31563018@qq.com;谭启德(1993),男,硕士研究生,通信作者,研究方向为新能源并网,E-mail:neeputqd@163.com。
引用本文:    
潘超, 谭启德, 蔡国伟, 张子信. 基于递归量化分析的COA-SVR短期风速混合预测模型[J]. 电网技术, 2018, 42(8): 2373-2381.
PAN Chao, TAN Qide, CAI Guowei, ZHANG Zixin. Hybrid Short-Term Wind Speed Prediction Model by COA-SVR Based on Recursive Quantitative Analysis. POWER SYSTEM TECHNOLOGY, 2018, 42(8): 2373-2381.
链接本文:  
http://www.dwjs.com.cn/CN/10.13335/j.1000-3673.pst.2018.0137  或          http://www.dwjs.com.cn/CN/Y2018/V42/I8/2373
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