Abstract：The paper has put forward a combined modeling method of forecasting the range of wind power fluctuation based on fuzzy information granulation and least squares support vector machine. In this method,firstly,the theory of fuzzy information granulation is applied in processing the training samples. The valid information of components of each window,namely the minimum value,the general average value and the maximum value of each window,is extracted according to different demands. Secondly,with regard to each component,forecasting model is built respectively with least squares support vector machine and the model of each component is then optimized with adaptive particle swarm optimization. Finally,the range of wind power fluctuation is forecasted with optimized least squares support vector machine. Case study demonstrates that the combined forecasting model is able to track wind power variation to make a forecast of wind power fluctuation.
王贺，胡志坚，仉梦林. 基于模糊信息粒化和最小二乘支持向量机的风电功率波动范围组合预测模型[J]. 电工技术学报, 2014, 29(12): 218-224.
Wang He,Hu Zhijian,Zhang Menglin. A Combined Forecasting Model for Range of Wind Power Fluctuation Based on Fuzzy Information Granulation and Least Squares Support Vector Machine. Transactions of China Electrotechnical Society, 2014, 29(12): 218-224.
[1] 林卫星,文劲宇,艾小猛,等. 风电功率波动特性的概率分布研究[J]. 中国电机工程学报,2012,32(1): 38-46. Lin Weixing,Wen Jinyu,Ai Xiaomeng,et al. Probability density function of wind power variations [J]. Proceedings of the CSEE,2012,32(1): 38-46. [2] 崔杨,穆钢,刘玉,等. 风电功率波动的时空分布特性[J]. 电网技术,2011,35(2): 110-114. Cui Yang,Mu Gang,Liu Yu,et al. Spatiotemporal distribution characteristic of wind power fluctuation [J]. Power System Technology,2011,35(2): 110-114. [3] Sorensen P,Cutululis N A,Vigueras-Rodriguez A. et al. Power fluctuations from large wind farms[J]. IEEE Transactions on Power Systems,2007,22(3): 958-965. [4] Mary B,Goran S. Value of bulk energy storage for managing wind power fluctuations[J]. IEEE Transac- tions on Energy Conversion,2007,22(1): 197-205. [5] Ernst B,Oakleaf B,Ahlstrom M L,et al. Predicting the wind[J]. IEEE Power & Energy Magazine,2007(11): 79-89. [6] 范高锋,王伟胜,刘纯,等. 基于人工神经网络的风电功率预测[J],中国电机工程学报,2008,28(34): 118-123. Fan Gaofeng,Wang Weisheng,Liu Cun,et al. Wind power prediction based on artificial neural network[J]. Proceedings of the CSEE,2008,28(34): 118-123. [7] 袁铁江,晁勤,李义岩,等. 大规模风电并网电力系统经济调度中风电场出力的短期预测模型[J]. 中国电机工程学报,2010,30(13): 23-27. Yuan Tiejiang,Chao Qin,Li Yiyan,et al. Short-term wind power output forecasting model for economic dispatch of power system incorporating large-scale wind farm[J]. Proceedings of the CSEE,2010,30(13): 23-27. [8] 杨锡运,孙宝君,张新房,等. 基于相似数据的支持向量机短期风速预测仿真研究[J]. 中国电机工程学报,2012,32(4): 35-41. Yang Xiyun,Sun Baojun,Zhang Xinfang,et al. Short-term wind speed forecasting based on support vector machine with similar Data[J]. Proceedings of the CSEE,2012,32(4): 35-41. [9] Zadel L. A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and System,1997,90(2): 111-127. [10] Pawlak Z. Granularity of knowledge,indiscernibility and rough sets[C]. Proceedings of IEEE World Congress on Computational Intelligence,Piscataway,NJ,USA: 1998. [11] 张铃,张钹. 模糊商空间理论(模糊粒度计算方法) [J]. 软件学报,2003,14(4): 770-776. Zhang Lin,Zhang Bo. Theory of fuzzy quotient space (methods of fuzzy granular computing)[J]. Journal of Software,2003,14(4): 770-776. [12] Bargiela A,Pedrycz W. Granular computing: an introduction[M]. Dodrecht: Kluwer Academic Publi- shers,2003. [13] 曾鸣,吕春泉,田廓,等. 基于细菌群落趋药性优化的最小二乘支持向量机短期负荷预测方法[J]. 中国电机工程学报,2011,31(34): 93-99. Zeng Ming,Lü Chunquan,Tian Kuo,et al. Least squares-support vector machine load forecasting approach optimized by bacterial colony chemotaxis method[J]. Proceedings of the CSEE,2011,31(34): 93-99. [14] 尚万峰,赵升吨,申亚京. 遗传优化的最小二乘支持向量机在开关磁阻电机建模中的应用[J]. 中国电机工程学报,2009,29(4): 65-69. Sang Wanfeng,Zhao Shengdun,Shen Yajing. Applica- tion of LSSVM Optimized by genetic algorithm to modeling of switched reluctance motor[J]. Proceedings of the CSEE,2009,29(4): 65-69. [15] 张春晓,张涛. 基于最小二乘支持向量机和粒子群算法的两相流含油率软测量方法[J]. 中国电机工程学报,2010,30(2): 86-91. Zhang Chunxiao,Zhang Tao. Soft measurement method for oil holdup of two phase flow based on least squares support vector machine and particle swarm optimization[J]. Proceedings of the CSEE,2010,30(2): 86-91. [16] 田东平,赵天绪. 基于Sigmoid惯性权值的自适应粒子群优化算法[J]. 计算机应用,2008,28(12): 3058-3061. Tian Dongping,Zhao Tianxu. Adaptive particle swarm optimization algorithm based on Sigmoid inertia weight[J]. Computer Applications,2008,28(12): 3058- 3061.
2014, Vol. 29 
