Abstract:Judging and forecasting types of strong convective weather causing velocity mutation are important to wind power ramping events.By considering the actual wind farm operation of regional power grid,power system operation mode,and the starting speed and capacity of hot standby,the definition of the wind power ramping and decision criteria of ramping weather scenario are determined.Based on this definition,the introduction of the support vector domain description method for wind power ramping scenario classification is used to establish an initializaed extremum model.Through appropriate significant parameters and preliminary classification results,the wind power ramping scenario classification model is established.Then according to the meteorological meaning,typical ramping weather types and related characteristics parameter thresholds are obtained.As a result,the case study shows that the establishment of the wind power ramping scenario classification model and the settled classification thresholds of the significant parameters can provide a good guidance for judging and forecasting the wind power ramping weather types in the target area.
熊一,査晓明,秦亮,欧阳庭辉,夏添. 风电功率爬坡气象场景分类模型及阈值整定研究[J]. 电工技术学报, 2016, 31(19): 155-162.
Xiong Yi ,Zha Xiaoming,Qin Liang ,Ouyang Tinghui ,Xia Tian. Study on Wind Power Ramping Weather Scenario Classification Model and Threshold Setting. Transactions of China Electrotechnical Society, 2016, 31(19): 155-162.
[1] 杨正瓴,冯勇,熊定方,等.基于季风特性改进风电功率预测的研究展望[J].智能电网,2015,3(1):1-7. Yang Zhengling,Feng Yong,Xiong Dingfang,et al.Research prospects of improvement in wind power forecasting based on characteristics of monsoons[J].Smart Grid,2015,3(1):1-7. [2] Freedman J,Markus M,Penc R.Analysis of west texas wind plant ramp-up and ramp-down events[R].AWS Truewind LLC,Albany,N.Y.,2008 [3] Ghelli A,Primo C.On the use of the extreme dependency score to investigate the performance of an NWP model for rare events[J].Meteorological Applications,2009,16(4):537-544. [4] Cutler N,Kay M,Outhred H,et al.High-risk scenarios for wind power forecasting in australia[C]//Proceedings of the European Wind Energy Conference & Exhibition (EWEC),Milan,Italy,2007. [5] Vladimir N.Vapnik.统计学习理论的本质[M].张学工,译.北京:清华大学出版社,2000. [6] Cortes C,Vapnik V.Support-vector networks[J].Machine Learning,1995,20(3):274-288. [7] Burges C J C.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998,2(2):123-132. [8] Tax D M J,Duin R P W.Support vector domain description[J].Pattern Recognition Letters,1999,20(11-13):1191-1199. [9] Tax D M J,Duin R P W.Support vector domain description[J].Machine Learning,2004,54(1):45-66. [10]高新波.模糊聚类分析及其应用[M].西安:西安电子科技大学出版社,2004. [11]何清.模糊聚类分析理论与应用研究进展[J].模糊系统与数学,1998(2):12-17. He Qing.Fuzzy cluster analysis theory and application research progress[J].Fuzzy Systems and Mathematics,1998 (2):12-17. [12]熊一,査晓明,秦亮,等.基于强对流天气判别的风功率爬坡预报方法研究[J].中国电机工程学报,2016,36(10):2690-2698. Xiong Yi, Zha Xiaoming,Qin Liang,et al.Research on wind power ramping prediction based on the strong convective weather discriminant method[J].Proceedings of the CSEE,2016,36(10):2690-2698. [13]罗旭,马珂.美国得克萨斯州电力可靠性委员会在风电调度运行管理方面的经验和启示[J].电网技术,2011,35(10):21-30. Luo Xu,Ma Ke.Experience and enlightenment in operations of wind generation in ERCOT grid[J].Power System Technology,2011,35(10):21-30. [14]赵秀英,吴宝俊.风暴强度指数SSI[J].气象,2000,26(5):55-56. Zhao Xiuying,Wu Baojun.Storm severity index(SSI) [J].Meteorological Monthly,2000,26(5):55-56. [15]高守亭,孙淑清.应用里查逊数判别中尺度波动的不稳定[J].大气科学,1986,10(2):171-182. Gao Shouting,Sun Shuqing.Determining the instability of mesoscale perturbation swith richardson number[J].Scientia Atmospherica Sinica,1986,10(2):171-182. [16]Johns R H,Doswell C A I.Severe local storms forecasting[J].Weather and Forecasting,1992,7(4):588-612. [17]Halverson J B,Rickenbach T,Roy B,et al.Environmental characteristics of convective systems during TRMM-LBA[J].Monthly Weather Review,2002,130 (6):12-18. [18]刘玉玲.对流参数在强对流天气潜势预测中的作用[J].气象科技,2003,31(3):24-29. Liu Yuling.The role of convective parameters in strong convective weather potential prediction[J].Journal of Meteorological Science and Technology,2003,31(3):24-29. [19]Bezdek J C,Keller J,Krishnapuram R,et al.Fuzzy models and algorithms for pattern recognition and image processing[M]//Dubois D,Prade H.The Handbooks of Fuzzy Sets Series.New York:Springer,1999:146-157. [20]Krishnapuram R,Keller J M.A possibilistic approach to clustering[J].IEEE Transactions on Fuzzy Systems,1993(1):98-110. [21]Pal N R,Pal K,Bezdek J C.A mixed c-means clustering model[C]//Proceedings of the 6th IEEE International Conference on Fuzzy Systems,Barcelona,1997(1):11-21. [22]Wu K L,Yang M S.Alternative c-means clustering algorithms[J].Pattern Recognition,2002,35(10):64-68. [23]Le K.Fuzzy relation compositions and pattern recognition[J].Journal of Information Science,1996,89(1):74-87. [24]戚永志,刘玉田.风光储联合系统输出功率滚动优化与实时控制[J]电工技术学报,2014,29(8):265-273. Qi Yongzhi,Liu Yutian.Output power rolling optimization and real-time control in wind-photovoltaic-storage hybrid system[J].Transactions of China Electrotechnical Society,2014,29(8):265-273. [25]刘方,杨秀,时珊珊,等.基于序列运算的微网经济优化调度[J]电工技术学报,2015,30(10):227-237. Liu Fang,Yang Xiu,Shi Shanshan,et al.Economic operation of micro-grid based on sequence operation[J].Transactions of China Electrotechnical Society,2015,30(10):227-237. [26]修春波,任晓,李艳晴,等.基于卡尔曼滤波的风速序列短期预测方法[J]电工技术学报,2014,29(2):253-259. Xiu Chunbo,Ren Xiao,Li Yanqing,et al.Short-term prediction method of wind speed series based on Kalman filtering fusion[J].Transactions of China Electrotechnical Society,2014,29(2):253-259.