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基于集合经验模式分解和遗传-高斯过程回归的短期风速概率预测

, PP. 138-147

Keywords: 集合经验模式分解,高斯过程回归,遗传算法,风速,概率预测

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Abstract:

短期风速概率预测对实现大规模风电并网具有重要意义。当前风速预测方法大多为点预测,无法描述风能的随机性。提出了一种基于集合经验模式分解(EEMD)和遗传-高斯过程回归(GA-GPR)的组合概率预测方法,首先对筛选和归一化后的风速时间序列进行集合经验模式分解,然后对各分量分别建立高斯过程回归模型,并引入遗传算法代替共轭梯度法,改进协方差函数的超参数寻优过程。最后叠加子序列预测结果得到风速概率预测结果,并与分位点回归法进行比较。仿真结果表明,该方法能够有效提高概率预测准确度,并为类似工程提供借鉴。

References

[1]  雷亚洲.与风电并网相关的研究课题[J].电力系统自动化, 2003, 27(8):84-89.
[2]  Lei Yazhou.Studies on wind farm integration into power system[J].Automation of Electric Power Systems, 2003, 27(8):84-89.
[3]  袁小明.大规模风电并网问题基本框架[J].电力科学与技术学报, 2012, 27(1):16-18.
[4]  Yuan Xiaoming.The basic framework of large-scale wind power integration problems[J].Journal of Electric Power Science and Technology, 2012, 27(1):16-18.
[5]  Kariniotakis G, Waldl I H P, Marti I, et al.Next generation forecasting tools for the optimal management of wind generation[C].Probabilistic Methods Applied top ower Systems, Stockholm, Sweden, 2006:1-6.
[6]  Negnevitsky M, Potter C W.Innovative short-term wind generation prediction techniques[C].Power Engineering Society General Meeting, Montreal, Canada, 2006:60-65.
[7]  Alexiadis M C, Dokopoulos P S, Sahsamanoglou H S.Short term forecasting of wind speed and related electrical power[J].Solar Energy, 1998, 63(1):61-68.
[8]  丁明, 张立军, 吴义纯.基于时间序列分析的风电场风速预测模型[J].电力自动化设备, 2005, 25(8):32-34.
[9]  Ding Ming, Zhang Lijun, Wu Yichun.Wind speed forecasting model based on time series analysis[J].Electric Power Automation Equipment, 2005, 25(8):32-34.
[10]  武小梅, 白银明, 文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制, 2011, 39(15):80-83.
[11]  Wu Xiaomei, Bai Yinming, Wen Fushuan.Short-term wind power forecast based on the Radial Basis Function neural network[J].Power System Protection and Control, 2011, 39(15):80-83.
[12]  Kariniotakis G N, Stavrakakis G S, Nogaret E F.Wind power forecasting using advanced neural networks models[J].IEEE Transactions on Energy Conversion, 1996, 11(4):762-767.
[13]  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.
[14]  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.
[15]  Bossanyi E A.Short-term wind speed using Kalman filters[J].Wind Engineering, 1985, 9(1):1-7.
[16]  罗文, 王莉娜.风场短期风速预测研究[J].电工技术学报, 2011, 26(7):68-74.
[17]  Luo Wen, Wang Lina.Short-term wind speed forecasting for wind farm[J].Transactions of China Electrotechnical Society, 2011, 26(7):68-74.
[18]  孙斌, 姚海涛.基于PSO优化LSSVM的短期风速预测[J].电力系统保护与控制, 2012, 40(5):85-89.
[19]  Sun Bin, Yao Haitao.The short-term wind speed forecast analysis based on the PSO-LSSVM predict model[J].Power System Protection and Control, 2012, 40(5):85-89.
[20]  王松岩, 于继来.风速与风电功率的联合条件概率预测方法[J].中国电机工程学报, 2011, 31(7):7-15.
[21]  Wang Songyan, Yu Jilai.Joint conditions probability forecast method for wind speed and wind power[J].Proceedings of the CSEE, 2011, 31(7):7-15.
[22]  孙元章, 吴俊, 李国杰, 等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报, 2009, 29(4):41-47.
[23]  Sun Yuanzhang, Wu Jun, Li Guojie, et al.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE, 2009, 29(4):41-47.
[24]  Barthelmie R J, Murray F, Pryor S C.The economic benefit of short-term forecasting for wind energy in the UK electricity market[J].Energy Policy, 2008, 36(5):1687-1696.
[25]  Sloughter J M, Gneiting T, Raftery A E.Probabilistic wind vector forecasting using ensembles and Bayesian model averaging[J].Monthly Weather Review, 2013, 141(6):2107-2119.
[26]  颜拥, 文福拴, 杨首晖, 等.考虑风电出力波动性的发电调度[J].电力系统自动化, 2010, 34(6):79-88.
[27]  Yan Yong, Wen Fushuan, Yang Shouhui, et al.Generation scheduling with fluctuating wind power[J].Automation of Electric Power Systems, 2010, 34(6):79-88.
[28]  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.
[29]  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.
[30]  Bremnes J B.Probabilistic wind power forecasts using local quantile regression[J].Wind Energy, 2004, 7(1):47-54.
[31]  Rasmussen C E, Williams C K I.Gaussian processes for machine learning[M].Massachusetts:The MIT Press, 2006.
[32]  Williams C K I.Prediction with Gaussian processes:From linear regression to linear prediction and beyond[R].Birmingham:Aston University, 1997.
[33]  孙斌, 姚海涛, 刘婷.基于高斯过程回归的短期风速预测[J].中国电机工程学报, 2012, 32(29):104-109.
[34]  Sun Bin, Yao Haitao, Liu Ting.Short-term wind speed forecasting based on Gaussian process regression model[J].Proceedings of the CSEE, 2012, 32(29):104-109.
[35]  王贺, 胡志坚, 陈珍, 等.基于集合经验模态分解和小波神经网络的短期风功率组合预测[J].电工技术学报, 2013, 28(9):137-144.
[36]  Wang He, Hu Zhijian, Chen Zhen, et al.A hybrid model for wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networks[J].Transactions of China Electrotechnical Society, 2013, 28(9):137-144.
[37]  Guindon S, Gascuel O.A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood[J].Systematic Biology, 2003, 52(5):696-704.
[38]  Wu Zhaohua, Huang N E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Process of the Royal Society of London Series A, 2003, 460(2046):1597-1611.
[39]  Wu Zhaohua, Huang N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis, 2009, 1(1):1-41.
[40]  Goldberg D E.Genetic algorithm in search, optimization and machine learning[M].New Jersey:Addison-Wesley, 1989.
[41]  杨世杰.动态测试数据中坏点处理的一种新方法——绝对均值法及应用研究[J].中国测试技术, 2006, 32(1):47-49, 82.
[42]  Yang Shijie.A new method of removing singular points in dynamic testing data—absolute mean value method and its application study[J].China Measurement & Testing Technology, 2006, 32(1):47-49, 82.
[43]  Pinson P, Kariniotakis G.Conditional prediction intervals of wind power generation[J].IEEE Transactions on Power System, 2010, 25(4):1845-1856.
[44]  Sideratos G, Hatziargyriou N D.Probabilistic wind power forecasting using radial basis function neural networks[J].IEEE Transactions on Power System, 2012, 27(4):1788-1796.
[45]  Pinson P, Nielsen H, Moller J, et al.Nonparametric probabilistic forecasts of wind power:required properties and evaluation[J].Wind Energy, 2007, 10(6):497-516.

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