林优;杨明;韩学山;安滨;

• 1:电网智能化调度与控制教育部重点实验室(山东大学)
• 2:国网山东省电力公司检修公司

摘要(Abstract)：

提出了一种基于稀疏贝叶斯分类与Dempster-Shafer(D-S)证据理论的短期风电功率概率分布非参数估计方法,预测时间尺度为48 h。该方法首先通过支持向量机(support vector machine,SVM)对风电功率进行点预测;进而将SVM预测误差的范围离散为多个区间,通过建立稀疏贝叶斯分类器对SVM预测误差落入各预定区间的概率进行估计。然后应用D-S证据理论对所有区间对应的概率估计结果进行整合,得到SVM预测误差的整体概率分布。最后叠加误差分布与SVM预测的风电功率值,得到风电功率的概率分布结果。该方法基于稀疏贝叶斯架构构建,具有高稀疏性,确保了模型的泛化能力与计算速度。该方法还系统地计及了风电场输出功率必须满足在[0,GN](GN为风电场装机容量)内取值的边界约束,使预测结果更加符合实际。以某74 MW的风电场为例对上述方法进行了验证,结果表明了该方法的有效性。

关键词(KeyWords)： 风电功率概率预测;非参数估计;支持向量机;稀疏贝叶斯分类;D-S证据理论

Abstract:

Keywords:

基金项目(Foundation): 国家重点基础研究发展计划项目(973项目)(2013CB228205);; 国家自然科学基金项目(51007047,51477091)~~

作者(Author): 林优;杨明;韩学山;安滨;

Email:

DOI: 10.13335/j.1000-3673.pst.2016.04.020

参考文献(References)：

• [1]Han D,Ma J,Xue A,et al.The uncertainty and its influence of wind generated power on power system transient stability under different penetration[C]//Proceedings of 2014 IEEE International Conference on Power System Technology(POWERCON).Chengdu,China:IEEE,2014:675-680.
• [2]谷兴凯,范高锋,王晓蓉,等.风电功率预测技术综述[J].电网技术,2007,31(S2):335-338.Gu Xingkai,Fan Gaofeng,Wang Xiaorong,et al.Summarization of wind power prediction technology[J].Power System Technology,2007,31(S2):335-338(in Chinese).
• [3]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.Ye Lin,Liu Peng.Combined model based on EMD-SVM for short-term wind power prediction[J].Proceedings of the CSEE,2011,31(31):102-108(in Chinese).
• [4]Zhang N,Kang C,Xia Q,et al.Modeling conditional forecast error for wind power in generation scheduling[J].IEEE Transactions on Power Systems,2014,29(3):1316-1324.
• [5]王松岩,李碧君,于继来,等.风速与风电功率预测误差概率分布的时变特性分析[J].电网技术,2013,37(4):967-973.Wang Songyan,Li Bijun,Yu Jilai.Analysis on time-varying characteristics of probability error in forecast of wind speed and wind power[J].Power System Technology,2013,37(4):967-973(in Chinese).
• [6]周松林,茆美琴,苏建徽.风电功率短期预测及非参数区间估计[J].中国电机工程学报,2011,31(25):10-16.Zhou Songlin,Mao Meiqin,Su Jianhui.Short-term forecasting of wind power and non-parametric confidence interval estimation[J].Proceedings of the CSEE,2011,31(25):10-16(in Chinese).
• [7]Yang M,Fan S,Lee W J.Probabilistic short-term wind power forecast using componential sparse Bayesian learning[J].IEEE Transactions on Industry Application,2013,49(6):2783-2792.
• [8]Yang M,Zhu S,Liu M,et al.One parametric approach for short-term JPDF forecast of wind generation[J].IEEE Transactions on Industry Application,2014,50(4):2837-2843.
• [9]Kou P,Liang D,Gao F,et al.Probabilistic wind power forecasting with online model selection and warped Gaussian process[J].Energy Conversion and Management,2014(84):649-663.
• [10]Bludszuweit H,Domínguez-Navarro J A,Llombart A.Statistical analysis of wind power forecast error[J].IEEE Transactions on Power Systems,2008,23(3):983-991.
• [11]Zhang Z S,Sun Y Z,Gao D W,et al.A versatile probability distribution model for wind power forecast errors and its application in economic dispatch[J].IEEE Transactions on Power Systems,2013,28(3):3114-3125.
• [12]王铮,刘纯,冯双磊,等.基于非参数回归的风电场理论功率计算方法[J].电网技术,2015,39(8):2148-2153.Wang Zheng,Liu Chun,Feng Shuanglei,et al.The wind farm theoretical power calculation method research based on non-parameter regression[J].Power System Technology,2015,39(8):2148-2153(in Chinese).
• [13]Pinson P.Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions[J].Journal of the Royal Statistical Society:Series C(Applied Statistics),2012,61(4):555-576.
• [14]Haque A U,Nehrir M H,Mandal P.A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting[J].IEEE Transactions on Power Systems,2014,29(99):1-10.
• [15]李智,韩学山,杨明,等.基于分位点回归的风电功率波动区间分析[J].电力系统自动化,2011,35(3):83-87.Li Zhi,Han Xueshan,Yang Ming,et al.Wind power fluctuation interval analysis based on quantile regression[J].Automation of Electric Power Systems,2011,35(3):83-87(in Chinese).
• [16]Bessa R J,Miranda V,Botterud A,et al.Time adaptive conditional kernel density estimation for wind power forecasting[J].IEEE Transactions on Sustainable Energy,2012,3(4):660-669.
• [17]Pinson P,George K.Conditional prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2010,25(4):1845-1856.
• [18]Zhu S M,Yang M,Han X S.Short-term generation forecast of wind farm using SVM-GARCH approach[C]//Proceedings of 2012 IEEE Power System Technology Conference(POWERCON).Auckland,New Zealand:IEEE,2012:1-6.
• [19]Tipping M E.Sparse Bayesian learning and the relevance vector machine[J].The Journal of Machine Learning Research,2001,1(3):211-244.
• [20]Sentz K,Ferson S.Combination of evidence in Dempster-Shafer theory[M].Albuquerque,New Mexico:Sandia National Laboratories,2002:16-26.
• [21]Madsen H,Pinson P,Kariniotakis G,et al.Standardizing the performance evaluation of short-term wind power prediction models[J].Wind Engineering,2005,29(6):475-489.
• [22]Pinson P.Estimation of the uncertainty in wind power forecasting[D].Paris:Ecole des Mines de Paris,2006.
• [23]Bremnes J B.Probabilistic wind power forecasts using local quantile regression[J].Wind Energy,2004,7(1):47-54.
• [24]Gneiting T,Balabdaoui F,Raftery A E.Probabilistic forecasts,calibration and sharpness[J].Royal Statistical Society:Series B(Statistical Methodology),2007,69(2):243-268.