基于机群划分方法的风电场理论发电功率计算研究
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摘要
科学统计风电理论功率对评价其受阻情况至关重要,因此提出一种基于机群划分方法的风电场理论发电功率计算模型:首先,考虑到单机机头风速的测量偏差,通过多步k聚类算法剔除了风速数据中的异常点,完成原始数据清洗;其次,针对风速序列多重共线性特点易造成拟合失真的问题,利用方差膨胀系数进行共线性检验,并以此将风电场划分为线性强相关机群和线性弱相关机群;最后,分别利用风速中位数和弱相关风速序列建立了两个机群的理论功率神经网络拟合模型。实际算例表明:所提出的模型在多次随机测试后,风电场理论功率平均绝对偏差不超过一台单机的额定容量,相关系数接近0.98,电量相对偏差仅为0.47%,均优于其他常用方法。
The research on theoretical power of wind farm is significant to evaluate curtailment of renewable energy. Therefore, we propose a new model to calculate theoretical power of wind farm called wind turbine grouping method, Firstly, to avoid measurement deviation, the abnormal data are eliminated by multi-step k-clustering algorithm. Secondly, the wind turbines are divided into two groups by calculating the variance inflation factor of wind speed, namely, strong correlative wind turbine group and weak correlative wind turbine group, and the multiple mutual linear problem of the wind speed then gets a better solution. Finally, two artificial neural nets are built respectively for two wind turbine groups, one is dependent on median wind speed of the group and the other is dependent on all the wind speed series of the group. A simulation is performed based on the actual output of a certain wind farm in the northwest of China.The test results show that the new model can make a superior performance compared with other traditional methods. The average absolute deviation of theoretical power calculated by the new model is less than the rated capacity of a single wind turbine, the correlation coefficient is close to 0.98, and the average power error is 0.47%.
The research on theoretical power of wind farm is significant to evaluate curtailment of renewable energy. Therefore, we propose a new model to calculate theoretical power of wind farm called wind turbine grouping method, Firstly, to avoid measurement deviation, the abnormal data are eliminated by multi-step k-clustering algorithm. Secondly, the wind turbines are divided into two groups by calculating the variance inflation factor of wind speed, namely, strong correlative wind turbine group and weak correlative wind turbine group, and the multiple mutual linear problem of the wind speed then gets a better solution. Finally, two artificial neural nets are built respectively for two wind turbine groups, one is dependent on median wind speed of the group and the other is dependent on all the wind speed series of the group. A simulation is performed based on the actual output of a certain wind farm in the northwest of China.The test results show that the new model can make a superior performance compared with other traditional methods. The average absolute deviation of theoretical power calculated by the new model is less than the rated capacity of a single wind turbine, the correlation coefficient is close to 0.98, and the average power error is 0.47%.
引文
[1]风电场理论可发电量与弃风电量评估导则:NB/T31055-2014[S].北京:中国电力出版社,2016.Guide on wind farm theoretical energy production and wind energy curtailment evaluation:NB/T31055-2014[S].Beijing,China:China Electric Power Press,2016.
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[15]满敬銮,杨薇.基于多重共线性的处理方法[J].数学理论于应用,2010,30(2):105-109.MAN Jingluan,YANG Wei.Based on multiple collinearity processing method[J].Mathematical Theory and Applications,2010,30(2):105-109.
[16]刘国旗.多重共线性产生的原因及其诊断处理[J].合肥工业大学学报,2001,24(4):607-610.LIU Guoqi.Cause of multi collinearity and its diagnosis and treatment[J].Journal of Hefei University of Technology,2001,24(4):607-610.
[17]张亚超,刘开培,秦亮,等.计及柔性资源的含风电场电力系统多目标动态协调调度[J].高电压技术,2017,43(4):1186-1193.ZHANG Yachao,LIU Kaipei,QIN Liang,et al.Multi-objective dynamic coordination dispatching for power systems with wind farms considering flexible resources[J].High Voltage Engineering,2017,43(4):1186-1193.
[18]王建学,张耀,万筱钟,等.面向电网运行的新能源处理特性指标体系研究-风电出力特性指标体系[J].电网与清洁能源,2016,32(2):42-51.WANG Jianxue,ZHANG Yao,WAN Xiaozhong,et al.An operation-oriented evaluation index system for renewable power output characteristics:wind power output characteristics[J].Power System and Clean Energy,2016,32(2):42-51.
[2]国家电力调度控制中心.风电受阻电量计算办法[R].北京:国家电网公司,2012.State Electric Power Dispatching and Control Centre.Calculation method of wind power curtailment[R].Beijing,China:State Grid Corporation of China,2012.
[3]国家能源局.2016年风电并网运行情况[EB/OL].(2017-01-26).[2017-06-20].http://www.nea.gov.cn/2017-01/26/c_136014615.htm.National Energy Board.Operation of wind power in 2016[EB/OL].(2017-01-26).[2017-06-20].http://www.nea.gov.cn/2017-01/26/c_136-014615.htm.
[4]ROGERS J,FINK S,PORTER K.Examples of wind energy curtailment practices[R].Maryland,USA:National Renewable Energy Laboratory,2010.
[5]International Electrotechnical Commission.Wind turbines part 12-2:power performance of electricity producing wind turbines based on nacelle anemometry:IEC 61400-12-2[S],2013.
[6]国家电力监管委员会.风电场弃风电量计算办法(试行)[R].北京:国家能源局,2012.State Electricity Regulatory Commission.Trial calculation method of wind power curtailment[R].Beijing,China:National Energy Administration,2012.
[7]陈颖,丁宇宇,周海,等.基于风资源实时监测数据的弃风电量评估方法:中国,201110075847.2[P].2011-09-14.CHEN Ying,DING Yuyu,ZHOU Hai,et al.A method of wind energy curtailment evaluation based on real-time monitoring data of wind resources:China,201110075847.2[P].2011-09-14.
[8]姜文玲,冯双磊,孙勇,等.基于机舱风速数据的风电场弃风电量计算方法研究[J].电网技术,2014,38(3):647-652.JIANG Wenling,FENG Shuanglei,SUN Yong,et al.Study on energy loss calculation during wind power curtailment based on wind speed measured by turbine nacelle anemometers[J].Power System Technology,2014,38(3):647-652.
[9]风力发电机组功率特性试验:GB/T18451.2-2012[S].北京:中国标准出版社,2012.Power performance measurements of electricity producing wind turbines:GB/T18451.2-2012[S].Beijing,China:China Standard Press,2012.
[10]钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):1047-1060.QIAN Zheng,PEI Yan,CAO Lixiao,et al.Review of wind power forecasting method[J].High Voltage Engineering,2016,42(4):1047-1060.
[11]苏盛,高大兵,杨洪明,等.极值风速地域性演化及对风力发电机组安全性的影响[J].高电压技术,2017,43(7):2378-2385.SU Sheng,GAO Dabing,YANG Hongming,et al.Spatial variation in wind extremes under the context of climate change and its impact on safety of wind turbines[J].High Voltage Engineering,2017,43(7):2378-2385.
[12]Jiawei Han,Micheline Kamber.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2007.Jiawei Han,Micheline Kamber.Data mining[M].FAN Ming,MENGXiaofeng,translated.Beijing,China:China Machine Press,2007.
[13]严英杰,盛弋皞,刘亚东,等.基于滑动窗口和聚类算法的变压器状态异常检测[J].高电压技术,2016,42(12):4020-4025.YAN Yingjie,SHENG Yihao,LIU Yadong,et al.Anomalous state detection of power transformer based on algorithm sliding windows and clustering[J].High Voltage Engineering,2016,42(12):4020-4025.
[14]胡军,尹立群,李振,等.基于大数据挖掘技术的输变电设备故障诊断方法[J].高电压技术,2017,43(11):3690-3697.HU Jun,YIN Liqun,LI Zhen,et al.Fault diagnosis method of transmission and transformation equipment based on big data mining technology[J].High Voltage Engineering,2017,43(11):3690-3697.
[15]满敬銮,杨薇.基于多重共线性的处理方法[J].数学理论于应用,2010,30(2):105-109.MAN Jingluan,YANG Wei.Based on multiple collinearity processing method[J].Mathematical Theory and Applications,2010,30(2):105-109.
[16]刘国旗.多重共线性产生的原因及其诊断处理[J].合肥工业大学学报,2001,24(4):607-610.LIU Guoqi.Cause of multi collinearity and its diagnosis and treatment[J].Journal of Hefei University of Technology,2001,24(4):607-610.
[17]张亚超,刘开培,秦亮,等.计及柔性资源的含风电场电力系统多目标动态协调调度[J].高电压技术,2017,43(4):1186-1193.ZHANG Yachao,LIU Kaipei,QIN Liang,et al.Multi-objective dynamic coordination dispatching for power systems with wind farms considering flexible resources[J].High Voltage Engineering,2017,43(4):1186-1193.
[18]王建学,张耀,万筱钟,等.面向电网运行的新能源处理特性指标体系研究-风电出力特性指标体系[J].电网与清洁能源,2016,32(2):42-51.WANG Jianxue,ZHANG Yao,WAN Xiaozhong,et al.An operation-oriented evaluation index system for renewable power output characteristics:wind power output characteristics[J].Power System and Clean Energy,2016,32(2):42-51.