文章摘要
叶林,陈小雨,靳晶新,等.考虑风功率密度和风向特征的风能资源MCP评估方法[J].电力系统自动化,2019,43(3):24-32. DOI: 10.7500/AEPS20180710007.
YE Lin,CHEN Xiaoyu,JIN Jingxin, et al.Measure-Correlate-Predict Assessment Method of Wind Energy Resource Considering Wind Power Density and Wind Direction[J].Automation of Electric Power Systems,2019,43(3):24-32. DOI: 10.7500/AEPS20180710007.
考虑风功率密度和风向特征的风能资源MCP评估方法
Measure-Correlate-Predict Assessment Method of Wind Energy Resource Considering Wind Power Density and Wind Direction
DOI:10.7500/AEPS20180710007
关键词: 风电场  区域风能资源评估  风力发电  测量-关联-预测  支持向量回归机  风功率密度
KeyWords: wind farm  regional wind energy resource assessment  wind power generation  measure-correlate-predict (MCP)  support vector regression (SVR)  wind power density
上网日期:2018-12-19
基金项目:国家自然科学基金资助项目(51677188)
作者单位E-mail
叶林 中国农业大学信息与电气工程学院, 北京市 100083 yelin@cau.edu.cn 
陈小雨 中国农业大学信息与电气工程学院, 北京市 100083  
靳晶新 中国农业大学信息与电气工程学院, 北京市 100083  
李镓辰 中国农业大学信息与电气工程学院, 北京市 100083  
滕景竹 国网北京市电力公司电力科学研究院, 北京市 100075  
摘要:
      在区域风电场风能资源评估方法中,传统的测量-关联-预测(MCP)方法未充分使用参考站的观测数据建立参考站和目标站之间风功率密度的映射关系,导致目标站长期风能资源的预测精度不高。在传统MCP组合方法的基础上,综合考虑参考站风功率密度和风向的特征组合,利用支持向量回归机(SVR)理论,建立2种不同的MCP模型,并将传统的考虑参考站风速、风向特征组合的MCP模型作为对比模型来验证所提出模型的有效性和准确性。实例研究表明,考虑参考站的风功率密度和风向特征输入的MCP模型对目标站风功率密度预测决定系数高于0.9,预测精度和适用性要明显优于传统的MCP模型,因而,该模型可用于评估区域风电场风能资源分布状况。
Abstract:
      In the wind resources assessment methods of regional wind farms, the observation data of reference stations is not sufficiently utilized by traditional measure-correlate-predict (MCP) methods to establish the mapping relationship between the wind power density of reference stations and the target station. Therefore, the forecasting accuracy of the long-term wind energy resources at the target station is not high. Based on the traditional MCP combination theory, two different MCP models are established with support vector regression (SVR). In addition, the traditional MCP models considering the characteristics of wind speed and direction are taken as the benchmarks to verify the effectiveness and accuracy of the proposed models. Results show that the determination coefficient of the proposed MCP method for wind power density prediction of the target station is higher than 0.9, and the forecasting accuracy and feasibility are significantly improved in comparison with those of traditional MCP models. Therefore, the proposed model can be applied to assess the wind energy resources of regional wind farms.
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