风电外送通道极限传输能力的自适应向量机估计
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摘要
风电随机性和间歇性导致基于典型方式计算的通道极限输电能力(TTC)有效性降低。提出一种TTC的自适应向量机估计方法,通过风电与负荷场景聚类形成代表性场景,采用重复潮流-二分法计算各场景下含暂稳约束的断面TTC值,经过最大信息系数与基于非参互信息的无监督特征筛选后,利用基于网格搜索-遗传算法寻优的自适应支持向量机对TTC进行回归估计。算例验证表明,该方法具备较强的数据拟合能力和非线性泛化能力,在线计算结果精确,能够实现TTC快速在线估计。
Total transfer capability of a transmission corridor technically changes fast with operation conditions. The conventional worst scenario-based methods hardly compute TTC efficiently that cannot meet online analysis requirement for wind power integration. In this paper, a SVM regression technique was presented to enable estimating TTC online. First, temporal wind power and load were clustered to determine representative scenario, which were used to generate samples by using repeated power flow with transient stability constraints. Second, through maximal information coefficient verification and unsupervised feature selection based on nonparametric mutual information, the most effective attributes were selected. Finally, SVM based on genetic algorithm-grid search was applied to establish regressed fitting model for TTC. Two cases were studied to validate the presented technique. The results indicate that the approach is able to fast and accurately estimate TTC of wind power exporting power systems with powerful fitting and generalization.
Total transfer capability of a transmission corridor technically changes fast with operation conditions. The conventional worst scenario-based methods hardly compute TTC efficiently that cannot meet online analysis requirement for wind power integration. In this paper, a SVM regression technique was presented to enable estimating TTC online. First, temporal wind power and load were clustered to determine representative scenario, which were used to generate samples by using repeated power flow with transient stability constraints. Second, through maximal information coefficient verification and unsupervised feature selection based on nonparametric mutual information, the most effective attributes were selected. Finally, SVM based on genetic algorithm-grid search was applied to establish regressed fitting model for TTC. Two cases were studied to validate the presented technique. The results indicate that the approach is able to fast and accurately estimate TTC of wind power exporting power systems with powerful fitting and generalization.
引文
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[21]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.
[22]Adankon M M,Cheriet M.Support vector machine[J].Computer Science,2002,1(4):1-28.
[23]王振树,李林川,牛丽.基于贝叶斯证据框架的支持向量机负荷建模[J].电工技术学报,2009,24(8):127-134.Wang Zhenshu,Li Linchuan,Niu Li.Load modeling based on support vector machine based on bayesian evidence framework[J].Transactions of China Electrotechnical Society,2009,24(8):127-134.
[24]Kundur P.Power system stability and control[M].New York:Mc Graw-Hill Professional,2005.
[2]朱星阳,张建华,刘文霞,等.风电并网引起电网电压波动的评价方法及应用[J].电工技术学报,2013,28(5):88-98.Zhu Xingyang,Zhang Jianhua,Liu Wenxia,et al.A evaluation methodology and its application of voltage fluctuation in power network caused by interconnected wind power[J].Transactions of China Electrotechnical Society,2013,28(5):88-98.
[3]Hamoud G.Assessment of available transfer capability of transmission systems[J].IEEE Transactions on Power Systems,2000,15(1):27-32.
[4]Min L,Abur A.Total transfer capability computation for multi-area power systems[J].IEEE Transactions on Power Systems,2006,21(3):1141-1147.
[5]蒋维勇,孙宏斌,张伯明,等.电力系统精细规则的研究[J].中国电机工程学报,2009,29(4):1-7.Jiang Weiyong,Sun Hongbin,Zhang Boming,et al.Fine operational rule of power system[J].Proceedings of the CSEE,2009,29(4):1-7.
[6]Ramezani M,Falaghi H,Singh C.A deterministic approach for probabilistic TTC evaluation of power systems including wind farm based on data clustering[J].IEEE Transactions on Sustainable Energy,2013,4(3):643-651.
[7]刘若凡,刘俊勇,刘友波,等.风电接入下断面极限传输功率运行规则的场景聚类提取方法[J].电力自动化设备,2014,34(10):69-74.Liu Ruofan,Liu Junyong,Liu Youbo,et al.Scenario clustering method for extracting TTC operating rules of transmission corridor with wind power[J].Electric Power Automation Equipment,2014,34(10):69-74.
[8]刘新东,方科,陈焕远,等.利用合理弃风提高大规模风电消纳能力的理论研究[J].电力系统保护与控制,2012,40(6):35-39.Liu Xindong,Fang Ke,Chen Huanyuan,et al.Research on rational wind power casting theory for large-scale wind power integration improvement[J].Power System Protection and Control,2012,40(6):35-39.
[9]Ramezani M,Singh C,Haghifam M.Role of clustering in the probabilistic evaluation of TTC in power systems including wind power generation[J].IEEE Transactions on Power Systems,2009,24(2):849-858.
[10]Wagstaff K,Cardie C,Rogers S,et al.Constrained K-means clustering with background knowledge[C]//Eighteenth International Conference on Machine Learning,Williamstoun,MA,USA,2001:577-584.
[11]张斌,庄池杰,胡军,等.结合降维技术的电力负荷曲线集成聚类算法[J].中国电机工程学报,2015,35(15):3741-3749.Zhang Bin,Zhuang Chijie,Hu Jun,et al.Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J].Proceedings of the CSEE,2015,35(15):3741-3749.
[12]Yan O,Singh C.Improvement of total transfer capability using TCSC and SVC[C]//Power Engineering Society Summer Meeting,2001,2:944-948.
[13]汪峰,白晓民.基于最优潮流方法的传输容量计算研究[J].中国电机工程学报,2002,22(11):35-40.Wang Feng,Bai Xiaomin.OPF based transfer capability calculation[J].Proceedings of the CSEE,2002,22(11):35-40.
[14]陈静,李华强,刘慧.基于连续潮流法的交直流系统可利用传输能力的计算[J].电力系统保护与控制,2010,38(16):1-5.Chen Jing,Li Huaqiang,Liu Hui,et al.Calculation of available transfer capability for AC/DC power systems based on continuation power flow[J].Power System Protection and Control,2010,38(16):1-5.
[15]罗明亮,黄宇保,王建全.基于最优潮流法含暂态稳定约束的最大传输容量计算[J].电力系统保护与控制,2010,38(19):22-26.Luo Mingliang,Huang Yubao,Wang Jianquan.Calculation of transient stability-constrained TTC based on OPF method[J].Power System Protection and Control,2010,38(19):22-26.
[16]王成山,李慧聪,魏炜,等.考虑电压稳定性约束的输电能力综合计算[J].电力自动化设备,2004,24(1):9-13.Wang Chengshan,Li Huicong,Wei Wei,et al.Integrative computation of transfer capability with voltage stability constrain[J].Electric Power Automation Equipment,2004,24(1):9-13.
[17]Liu Youbo,Liu Tingjian,Xiang Yue,et al.Estimating TTC for wind farm integrated power systems based on nonparametric regression analytics[C]//IEEE Power&Energy Society General Meeting,Boston,MA,USA,2016:1-5.
[18]Reshef D N,Reshef Y A,Finucane H K,et al.Detecting novel associations in large data sets[J].Science,2011,334(6062):1518-1524.
[19]Faivishevsky L,Goldberger J.Unsupervised feature selection based on non-parametric mutual information[C]//IEEE International Workshop on Machine Learning for Signal Processing(MLSP),Santander,Spain,2012:1-6.
[20]陈伟根,滕黎,刘军,等.基于遗传优化支持向量机的变压器绕组热点温度预测模型[J].电工技术学报,2014,29(1):44-51.Chen Weigen,Teng Li,Liu Jun,et al.Transformer winding hot-spot temperature prediction model of support vector machine optimized by genetic algorithm[J].Transactions of China Electrotechnical Society,2014,29(1):44-51.
[21]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.
[22]Adankon M M,Cheriet M.Support vector machine[J].Computer Science,2002,1(4):1-28.
[23]王振树,李林川,牛丽.基于贝叶斯证据框架的支持向量机负荷建模[J].电工技术学报,2009,24(8):127-134.Wang Zhenshu,Li Linchuan,Niu Li.Load modeling based on support vector machine based on bayesian evidence framework[J].Transactions of China Electrotechnical Society,2009,24(8):127-134.
[24]Kundur P.Power system stability and control[M].New York:Mc Graw-Hill Professional,2005.