基于模糊信息粒化的光伏出力区间预测
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摘要
相对于传统的光伏点预测而言,光伏区间预测可以为电网调度人员提供更加全面、有效的预测信息。鉴于此,文章提出一种基于模糊信息粒化理论的区间预测方法。针对光伏功率原始数据的强波动特性,采用集成经验模态分解方法将其分解为若干个子序列。并依据样本熵理论,将复杂度较高的子序列重组为随机分量,代表光伏输出的波动性,论文对该随机分量进行模糊化处理,从而得出其波动趋势以及波动上、下界,再分别进行预测;而复杂度相对较低的其他子序列代表光伏出力稳定分量,因此,直接对其进行确定性预测。论文采用经过纵横交叉算法改进的人工神经网络(CSO-BP)进行预测,得出最终光伏区间预测结果。
Compared with the traditional PV deterministic point forecast,PV interval prediction can provide more comprehensive and effective forecast information for grid dispatcher. Therefore,the paper proposes an interval prediction method based on fuzzy information granulation theory. In view of the fluctuating character of the photovoltaic power,the original PV data is decomposed into several sub-sequences by using the ensemble empirical model decomposition( EEMD). According to the sample entropy theory,the sub-sequences with higher complexity are reorganized into random components,which represent the volatility of PV output. This paper conducted the random components with fuzzy information granulation,which provided its fluctuating trend,fluctuating upper bound and lower bound. And the remaining sub-sequences with relatively small complexity represent the PV stabilized components,and therefore,the deterministic predictions are made directly. In this paper,the artificial neural network model( CSO-BP),which is improved by crisscross algorithm( CSO),is used to predict the PV interval prediction results.
Compared with the traditional PV deterministic point forecast,PV interval prediction can provide more comprehensive and effective forecast information for grid dispatcher. Therefore,the paper proposes an interval prediction method based on fuzzy information granulation theory. In view of the fluctuating character of the photovoltaic power,the original PV data is decomposed into several sub-sequences by using the ensemble empirical model decomposition( EEMD). According to the sample entropy theory,the sub-sequences with higher complexity are reorganized into random components,which represent the volatility of PV output. This paper conducted the random components with fuzzy information granulation,which provided its fluctuating trend,fluctuating upper bound and lower bound. And the remaining sub-sequences with relatively small complexity represent the PV stabilized components,and therefore,the deterministic predictions are made directly. In this paper,the artificial neural network model( CSO-BP),which is improved by crisscross algorithm( CSO),is used to predict the PV interval prediction results.
引文
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[12]王恺,关少卿,汪令祥,等.基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模[J].电力系统保护与控制,2015,43(2):26-32.Wang Kai,Guan Shaoqing,Wang Lingxiang,et al.A combined forecasting model for wind power predication based on fuzzy information granulation and least squares support vector machine[J].Power System Protection and Control,2015,43(2):26-32.
[13]MENG A,CHEN Y,YIN H,et al.Crisscross optimization algorithm and its application[J].Knowledge-Based Systems,2014,67:218-229.
[14]张学清,梁军,张熙,等.基于样本熵和极端学习机的超短期风电功率组合预测模型研究[J].中国电机工程学报,2013,33(25):33-40.Zhang Xueqing,Liang Jun,Zhang Xi,et al.Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J].Proceedings of the CSEE,2013,33(25):33-40.
[15]赵书强,王明雨,胡永强,等.基于不确定理论的光伏出力预测研究[J].电工技术学报,2015,30(6):213-220.Zhao Shuqiang,Wang Mingyu,Hu Yongqiang,et al.Research on the prediction of PV output based on uncertainty theory[J].Transactions of China Electro-technical Society,2015,30(6):213-220.
[16]陈伯成,梁冰,周越博,等.自组织映射神经网络(SOM)在客户分类中的一种应用[J].系统工程理论与实践,2004,3(8):8-14.
[17]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.
[18]李知艺,丁剑鹰,吴迪,等.电力负荷区间预测的集成极限学习机方法[J].华北电力大学学报,2014,41(2):78-88.
[19]杨明,范澍,韩学山,等.基于分量稀疏贝叶斯学习的风电场输出功率概率预测方法[J].电力系统自动化,2012,36(14):125-130.
[2]黄磊,舒杰,姜桂秀,等.基于多维时间序列局部支持向量回归的微网光伏发电预测[J].电力系统自动化,2014,38(5):1924.DOI:10.7500/AEPS20130710005.Huang Lei,Shu Jie,Jiang Guixiu,et al.Photovoltaic generation forecast based on multidimensional time series and local support vector regression in microgrids[J].Automation of Electric Power Systems,2014,38(5):1924.DOI:10.7500/AEPS201307 10005.
[3]陈阿莲,冯丽娜,杜春水,等.基于支持向量机的局部阴影条件下光伏阵列建模[J].电工技术学报,2011,26(3):140-147.Chen Alian,Feng Lina,Du Chunshui,et al.Modeling of photovoltaic array based on support vector machines under partial shaded conditions[J].Transactions of China Electrotechnical Society,2011,26(3):140-147.
[4]王新,孟玲玲.基于EEMD-LSSVM的超短期负荷预测[J].电力系统保护与控制,2015,43(1):61-66.Wang Xin,Meng Lingling.Ultra-short-term load forecasting based on EEMD-LSSVM[J].Power System Protection and Control,2015,43(1):61-66.
[5]罗建春,晁勤,罗洪,等.基于LVQ-GA-BP神经网络光伏电站出力短期预测[J].电力系统保护与控制,2014,42(13):89-94.Luo Jianchun,Chao Qin,Luo Hong,et al.PV short-term output forecasting based on LVQ-GA-BP neural network[J].Power System Protection and Control,2014,42(13):89-94.
[6]李练兵,张秀云,王志华,等.故障树和BAM神经网络在光伏并网故障诊断中的应用[J].电工技术学报,2015,30(2):248-254.Li Lianbing,Zhang Xiuyun,Wang Zhihua,et al.Fault diagnosis in solar photovoltaic grid-connected power system based on fault tree and BAM neural network[J].Transactions of China Electrotechnical Society,2015,30(2):248-254.
[7]ZAKARIA Z,MASATO O,TOMONOBU S,et al.Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system[J].IEEE Trans on Sustainable Energy,2014,5(2):682-690.
[8]董雷,周文萍,张沛,等.基于动态贝叶斯网络的光伏发电短期概率预测[J].中国电机工程学报,2013,33(S1):3845.Dong Lei,Zhou Wenping,Zhang Pei,et al.Short term photovoltaic output forecast based on dynamic Bayesian network theory[J].Proceedings of the CSEE,2013,33(S1):3845.
[9]罗明武,孙朝霞,刘强民,等.基于集对分析理论的太阳辐照度区间预测[J].电力科学与工程,2015,31(10):44-49.Luo Mingwu,Sun Zhaoxia,Liu Qiangmin,et al.Solar irradiance interval prediction based on set pair analysis theory[J].Electric power science and engineering,2015,31(10):44-49.
[10]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.Wang He,Hu Zhijian,Chen Zhen,et al.A hybrid model for wind power forecasting based on ensemble empirical mode decomposition networks[J].Transactions of China Electro technical Society,2013,28(9):137-144.
[11]张学清,梁军,张熙,等.基于样本熵和极端学习机的超短期风电功率组合预测模型[J].中国电机工程学报,2013,33(25):33-40.Zhang Xueqing,Liang Jun,Zhang Xi,et al.Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J].Proceedings of the CSEE,2013,33(25):33-40.
[12]王恺,关少卿,汪令祥,等.基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模[J].电力系统保护与控制,2015,43(2):26-32.Wang Kai,Guan Shaoqing,Wang Lingxiang,et al.A combined forecasting model for wind power predication based on fuzzy information granulation and least squares support vector machine[J].Power System Protection and Control,2015,43(2):26-32.
[13]MENG A,CHEN Y,YIN H,et al.Crisscross optimization algorithm and its application[J].Knowledge-Based Systems,2014,67:218-229.
[14]张学清,梁军,张熙,等.基于样本熵和极端学习机的超短期风电功率组合预测模型研究[J].中国电机工程学报,2013,33(25):33-40.Zhang Xueqing,Liang Jun,Zhang Xi,et al.Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J].Proceedings of the CSEE,2013,33(25):33-40.
[15]赵书强,王明雨,胡永强,等.基于不确定理论的光伏出力预测研究[J].电工技术学报,2015,30(6):213-220.Zhao Shuqiang,Wang Mingyu,Hu Yongqiang,et al.Research on the prediction of PV output based on uncertainty theory[J].Transactions of China Electro-technical Society,2015,30(6):213-220.
[16]陈伯成,梁冰,周越博,等.自组织映射神经网络(SOM)在客户分类中的一种应用[J].系统工程理论与实践,2004,3(8):8-14.
[17]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.
[18]李知艺,丁剑鹰,吴迪,等.电力负荷区间预测的集成极限学习机方法[J].华北电力大学学报,2014,41(2):78-88.
[19]杨明,范澍,韩学山,等.基于分量稀疏贝叶斯学习的风电场输出功率概率预测方法[J].电力系统自动化,2012,36(14):125-130.