基于小波变换和改进萤火虫算法优化LSSVM的短期风速预测
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摘要
准确预测风速对风电规模化并网至关重要。为提高短期风速预测精度,提出一种基于小波分解和改进的萤火虫算法优化最小二乘支持向量机超参数的风速预测模型。首先利用小波变换将风速时序分解为近似序列和细节序列,然后对各序列分别利用一种新颖的混沌萤火虫算法优化LSSVM进行预测,最后将各序列预测值叠加得到最终风速预测值。在两种时间尺度的实测数据上进行仿真计算。结果表明,该算法较交叉验证的LSSVM,IPSO-LSSVM,WD-DE-LSSVM及BP神经网络等多种经典算法预测精度更高,表明了该算法的有效性和优越性。
Accurately predicting wind speed is of key importance for large scale wind power connecting to the grid. To improve the short-term wind speed forecasting accuracy, a least squares support vector machine wind speed prediction model based on wavelet decomposition and improved firefly algorithm is proposed. Firstly, the actual wind speed series is decomposed and reconstructed to approximate series and detail series, then the series are separately predicted by LSSVM optimized by chaotic firefly algorithm, at last the separate prediction series are superposed as the ultimate prediction wind speed. To verify the proposed model, two different time scale actual wind speed data are applied to simulation. The results show that the proposed model has higher prediction accuracy than classical model like CV-LSSVM, IPSO-LSSVM, WD-DE-LSSVM and BP neural networks, showing its validity and superiority.
Accurately predicting wind speed is of key importance for large scale wind power connecting to the grid. To improve the short-term wind speed forecasting accuracy, a least squares support vector machine wind speed prediction model based on wavelet decomposition and improved firefly algorithm is proposed. Firstly, the actual wind speed series is decomposed and reconstructed to approximate series and detail series, then the series are separately predicted by LSSVM optimized by chaotic firefly algorithm, at last the separate prediction series are superposed as the ultimate prediction wind speed. To verify the proposed model, two different time scale actual wind speed data are applied to simulation. The results show that the proposed model has higher prediction accuracy than classical model like CV-LSSVM, IPSO-LSSVM, WD-DE-LSSVM and BP neural networks, showing its validity and superiority.
引文
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[18]MALLAT S G.A theory for multi-resolution signal decomposition:the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
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[20]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):93-99,11.ZENG Ming,LüChunquan,TIAN Guo,et al.Least squares-support vector machine load forecasting approach optimized by bacterial colony chemo taxis method[J].Proceedings of the CSEE,2011,31(34):93-99,11.
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[22]栗然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法[J].电力系统保护与控制,2010,38(21):146-151.LI Ran,CHEN Qian,XU Hongrui.Wind speed forecasting method based on LS-SVM considering the related factors[J].Power System Protection and Control,2010,38(21):146-151.
[23]YANG Xinshe.Nature-inspired matheuristic algorithms[M].UK:Lunvier Press,2008.
[24]LUKASIK S,ZAK S.Firefly algorithm for continuous constrained optimization tasks[C]//ICCCC 2009:97-100.
[25]程声烽,程小华,杨露.基于改进粒子群算法的小波神经网络在变压器故障诊断中的应用[J].电力系统保护与控制,2014,42(19):37-42.CHENG Shengfeng,CHENG Xiaohua,YANG Lu.Application of wavelet neural network with improved particle swarm optimization algorithm in power transformer fault diagnosis[J].Power System Protection and Control,2014,42(19):37-42.
[26]程泽,董梦男,杨添剀,等.基于自适应混沌粒子群算法的光伏电池模型参数辨识[J].电工技术学报,2014,29(9):245-252.CHENG Ze,DONG Mengnan,YANG Tiankai,et al.Extraction of solar cell model parameters based on selfadaptive chaos particle swarm optimization algorithm[J].Transactions of China Electrotechnical Society,2014,29(9):245-252.
[27]易谷,王清蓉.基于CPSO-RLS的电力系统谐波估计融合算法[J].电力系统保护与控制,2014,42(12):25-29.YI Gu,WANG Qingrong.Fusion algorithm of harmonic estimation based on CPSO-RLS for power system[J].Power System Protection and Control,2014,42(12):25-29.
[2]王娟娟,赵闻蕾,王兴强,等.基于Johnson分布直接转换法的风速预测[J].电力自动化设备,2014,34(6):20-24.WANG Juanjuan,ZHAO Wenlei,WANG Xingqiang,et al.Wind speed prediction based on Johnson direct transformation[J].Electric Power Automation Equipment,2014,34(6):20-24.
[3]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.LUO Wen,WANG Lina.Short-term wind speed forecasting for wind farm[J].Transactions of China Electrotechnical Society,2011,26(7):68-74.
[4]史宇伟,潘学萍.计及历史气象数据的短期风速预测[J].电力自动化设备,2014,34(10):75-80.SHI Yuwei,PAN Xueping.Short-term wind speed forecasting considering historical meteorological data[J].Electric Power Automation Equipment,2014,34(10):75-80.
[5]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.DING Ming,ZHANG Lijun,WU Yichun.Wind speed forecast model for wind farms based on time series analysis[J].Electric Power Automation Equipment,2005,25(8):32-34.
[6]李文良,卫志农,孙国强,等.基于改进空间相关法和径向基神经网络的风电场短期风速分时预测模型[J].电力自动化设备,2009,29(6):89-92.LI Wenliang,WEI Zhinong,SUN Guoqiang,et al.Multi-interval wind speed forecast model based on improved spatial correlation and RBF neural network[J].Electric Power Automation Equipment,2009,29(6):89-92.
[7]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.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.
[8]肖迁,李文华,李志刚,等.基于改进的小波-BP神经网络的风速和风电功率预测[J].电力系统保护与控制,2014,42(15):80-86.XIAO Qian,LI Wenhua,LI Zhigang,et al.Wind speed and power prediction based on improved wavelet-BP neural network[J].Power System Protection and Control,2014,42(15):80-86.
[9]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.
[10]杜颖,卢继平,李青,等.基于最小二乘支持向量机的风电场短期风速预测[J].电网技术,2008,32(15):62-66.DU Ying,LU Jiping,LI Qing,et al.Short-term wind speed forecasting of wind farm based on least square-support vector machine[J].Power System Technology,2008,32(15):62-66.
[11]曾杰,张华.基于最小二乘支持向量机的风速预测模型[J].电网技术,2009,33(18):144-147.ZENG Jie,ZHANG Hua.A wind speed forecasting model based on least squares support vector machine[J].Power System Technology,2009,33(18):144-147.
[12]杨洪,古世甫,崔明东,等.基于遗传优化的最小二乘支持向量机风电场风速短期预测[J].电力系统保护与控制,2011,35(11):44-48.YANG Hong,GU Shifu,CUI Mingdong,et al.Forecast of short-term wind speed in wind farms based on GA optimized LS-SVM[J].Power System Protection and Control,2011,35(11):44-48.
[13]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.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.
[14]王贺,胡志坚,张翌晖,等.基于IPSO-LSSVM的风电功率短期预测研究[J].电力系统保护与控制,2012,40(24):107-112.WANG He,HU Zhijian,ZHANG Yihui,et al.Short-term prediction of wind power based on IPSO-LSSVM[J].Power System Protection and Control,2012,40(24):107-112.
[15]王贺,胡志坚,张翌晖,等.基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245.WANG He,HU Zhijian,ZHANG Yihui,et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J].Transactions of China Electrotechnical Society,2014,29(4):237-245.
[16]彭春华,刘刚,孙惠娟.基于小波分解和微分进化支持向量机的风电场风速预测[J].电力自动化设备,2012,32(1):9-13.PENG Chunhua,LIU Gang,SUN Huijuan.Wind speed forecasting based on wavelet decomposition and differential evolution-support vector machine for wind farms[J].Electric Power Automation Equipment,2012,32(1):9-13.
[17]田中大,李树江,王艳红,等.基于小波变换的风电场短期风速组合预测[J].电工技术学报,2015,30(9):112-120.TIAN Zhongda,LI Shujiang,WANG Yanhong,et al.Short-term wind speed combined prediction for wind farms based on wavelet transfer[J].Transactions of China Electro technical Society,2015,30(9):112-120.
[18]MALLAT S G.A theory for multi-resolution signal decomposition:the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[19]唐杰明,刘俊勇,杨可,等.基于灰色模型和最小二乘支持向量机的电力短期负荷组合预测[J].电网技术,2009,33(3):63-68.TANG Jieming,LIU Junyong,YANG Ke,et al.Short-term load combination forecasting by grey model and least square support vector machine[J].Power System Technology,2009,33(3):63-68.
[20]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):93-99,11.ZENG Ming,LüChunquan,TIAN Guo,et al.Least squares-support vector machine load forecasting approach optimized by bacterial colony chemo taxis method[J].Proceedings of the CSEE,2011,31(34):93-99,11.
[21]傅美平,马红伟,毛建容.基于相似日和最小二乘支持向量机的光伏发电短期预测[J].电力系统保护与控制,2012,40(16):65-69.FU Meiping,MA Hongwei,MAO Jianrong.Short-term photovoltaic power forecasting based on similar days and least square support vector machine[J].Power System Protection and Control,2012,40(16):65-69.
[22]栗然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法[J].电力系统保护与控制,2010,38(21):146-151.LI Ran,CHEN Qian,XU Hongrui.Wind speed forecasting method based on LS-SVM considering the related factors[J].Power System Protection and Control,2010,38(21):146-151.
[23]YANG Xinshe.Nature-inspired matheuristic algorithms[M].UK:Lunvier Press,2008.
[24]LUKASIK S,ZAK S.Firefly algorithm for continuous constrained optimization tasks[C]//ICCCC 2009:97-100.
[25]程声烽,程小华,杨露.基于改进粒子群算法的小波神经网络在变压器故障诊断中的应用[J].电力系统保护与控制,2014,42(19):37-42.CHENG Shengfeng,CHENG Xiaohua,YANG Lu.Application of wavelet neural network with improved particle swarm optimization algorithm in power transformer fault diagnosis[J].Power System Protection and Control,2014,42(19):37-42.
[26]程泽,董梦男,杨添剀,等.基于自适应混沌粒子群算法的光伏电池模型参数辨识[J].电工技术学报,2014,29(9):245-252.CHENG Ze,DONG Mengnan,YANG Tiankai,et al.Extraction of solar cell model parameters based on selfadaptive chaos particle swarm optimization algorithm[J].Transactions of China Electrotechnical Society,2014,29(9):245-252.
[27]易谷,王清蓉.基于CPSO-RLS的电力系统谐波估计融合算法[J].电力系统保护与控制,2014,42(12):25-29.YI Gu,WANG Qingrong.Fusion algorithm of harmonic estimation based on CPSO-RLS for power system[J].Power System Protection and Control,2014,42(12):25-29.