基于RBF神经网络的电网脆弱性评估及其趋势估计
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摘要
建立了分层网状拓扑结构下的电网脆弱性评价体系,针对该体系提出了基于径向基函数(Radial Basis Function,RBF)神经网络的电网脆弱性评估方法。将电网综合脆弱性分为状态脆弱性和结构脆弱性,并与相应的子指标构成脆弱性网状评价体系,同时以高斯(Gauss)函数作为RBF神经网络函数的核函数解决指标间的非线性问题。通过MATLAB中的RBF神经网络函数对IEEE14母线系统计算分析,验证了该方法的全面性与有效性。最后,针对节点多个测量周期的脆弱性测度建立自回归(Auto Regression,AR)模型,通过判定AR模型的差分方程稳定性,分析了节点脆弱性测度的发展趋势。
This paper establishes the hierarchical network topology of network vulnerability evaluation system. The system was proposed based on radial basis function (RBF) neural network method of grid vulnerability assessment. The comprehensive vulnerability of the power grid is divided into the state vulnerability and structural vulnerability,and the corresponding sub-indexes constitute the vulnerability network evaluation system. Meanwhile,taking Gauss functions as the kernel function of RBF neural network function to solve nonlinear problem between the indicators. The calculation and analysis of IEEE14-bus system is carried out to verify the comprehensiveness and effectiveness of the method through using the RBF neural network function in MATLAB. Finally,the auto regressive (AR) model is established according to the multiple nodes of the measurement cycle vulnerability,the AR model is to determine the stability of difference equation and analyzes the development trend of node vulnerability measure.
This paper establishes the hierarchical network topology of network vulnerability evaluation system. The system was proposed based on radial basis function (RBF) neural network method of grid vulnerability assessment. The comprehensive vulnerability of the power grid is divided into the state vulnerability and structural vulnerability,and the corresponding sub-indexes constitute the vulnerability network evaluation system. Meanwhile,taking Gauss functions as the kernel function of RBF neural network function to solve nonlinear problem between the indicators. The calculation and analysis of IEEE14-bus system is carried out to verify the comprehensiveness and effectiveness of the method through using the RBF neural network function in MATLAB. Finally,the auto regressive (AR) model is established according to the multiple nodes of the measurement cycle vulnerability,the AR model is to determine the stability of difference equation and analyzes the development trend of node vulnerability measure.
引文
[1]卢锦玲,姬群星,朱永利.基于能量函数法的电网脆弱性评估[J].电网技术,2008,32(7):30-45.Lu Jinling,Ji Qunxing,Zhu Yongli.Power Grid Vulnerability Assessment Based on Energy Function[J].Power System Technology,2008,32(7):30-45.
[2]Kinney R,Crucitti P,Albert R,et al.Modeling cascading failures in the North American power grid[J].The European Physical Journal,2005,46(1):101-107.
[3]倪向萍,梅生伟,张雪敏.基于复杂网络理论的输电线路脆弱度评估方法[J].电力系统自动化,2008,32(4):1-5.Ni Xiangping,Mei Shengwei,Zhang Xuemin.Transmission lines’vulnerability assessment based on complex network theory[J].Automation of Electric Power Systems,2008,32(4):1-5.
[4]丁明,过羿,张晶晶.基于效用风险熵权模糊综合评判的复杂电网节点脆弱性评估[J].电工技术学报,2015,30(3):214-223.Ding Ming,Guo Yi,Zhang Jingjing.Node Vulnerability Assessment for Complex Power Grids Based on Effect Risk Entropy-Weighted Fuzzy Comprehensive Evaluation[J].Transactions of China Electrotechnical Society,2015,30(3):214-223.
[5]Watts D J,Strogatz S H.Collective dynamics of‘small-world’networks[J].Nature,1998,393:440-442.
[6]Lu Z X,Meng Z W,Zhou S X.Cascading failure analysis of bulk power system using small-world network model[C].Proceedings of the 8th International Conference on Probabilistic Methods Applied to Power Systems,Iowa State University,Ames Iowa,2004.
[7]肖盛,张建华.基于小世界拓扑模型的电网脆弱性评估[J].电网技术,2010,34(8):64-68.Xiao Sheng,Zhang Jianhua.Assessment of Power Grid Vulnerability Based on Small-World Topological Model[J].Power System Technology,2010,34(8):64-68.
[8]丁明,过羿,张晶晶.基于效用风险熵的复杂电网连锁故障脆弱性辨识[J].电力系统自动化,2013,37(17):52-57.Ding Ming,Guo Yi,Zhang Jingjing.Vulnerability identification for cascading failures of complex power grid based on effect risk entropy[J].Automation of Electric Power Systems,2013,37(17):52-57.
[9]丁明,韩平平.加权拓扑模型下的小世界电网脆弱性评估[J].中国电机工程学报,2008,28(10):20-25.Ding Ming,Han Pingping.Vulnerability assessment to small-world power grid based on weighted topological model[J].Proceedings of the CSEE,2008,28(10):20-25.
[10]魏震波,刘俊勇,朱国俊,等.基于可靠性加权拓扑模型下的电网脆弱性评估模型[J].电工技术学报,2010,25(8):132-136.Wei Zhenbo,Liu Junyong,Zhu Guojun,et al.Vulnerability evaluation model to power grid based on reliability parameter weighted topological model[J].Transactions of China Electrotechnical Society,2010,25(8):132-136.
[11]李勇,刘俊勇,刘晓宇,等.基于潮流熵测度的连锁故障脆弱线路评估及其在四川主干电网中的应用[J].电力自动化设备,2013,33(10):40-46.Li Yong,Liu Junyong,Liu Xiaoyu,et al.Vulnerability assessment based on power flow entropy for lines in cascading failures and its application in Sichuan backbone power grid[J].Electric Power Automation Equipment,2013,33(10):40-46.
[12]杨帆.大规模消纳可再生能源对电网脆弱性影响的评价研究[D].华北电力大学,2016.Yang Fan.Evaluation research on influce of large-scale consumption of renewable energy to power grids vulnerability[D].North China Electric Power University,2016.
[13]老大中.变分法基础[M].北京:国防工业出版社,2007:1-20.
[14]史忠植.神经网络[M].北京:高等教育出版社,2009:140-164.
[15]周品.MATLAB神经网络设计与应用[M].北京:清华大学出版,2013:267-268.
[16]马东宇.基于Gaussian型RBF神经网络的函数逼近与应用[D].中南大学,2011.
[17]刘苏苏,孙立民.支持向量机与RBF神经网络回归性能比较研究[J].计算机工程与设计,2011,32(12):4202-4205.Liu Susu,Sun Limin.Performance comparison of regression prediction on support vector machine and RBF neural network[J],Computer Engineering and Design,2011,32(12):4202-4205.
[18]Simon Haykin,Adaptive Filter Theory[M].New York:Pearson Education Limited,2013:1-40.
[19]Min Gan,Hui Peng,Xiaoyan Peng,et al.A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling,Information Sciences 180(2010):4370-4383.
[20]V.Haggan,T.Ozaki,Modeling nonlinear random vibrations using an amplitude-dependent autoregressive time series model,Biometrika 68(1981):189-196.
[21]阮炯.差分方程和常微分方程[M].上海:复旦大学出版社,2002.
[2]Kinney R,Crucitti P,Albert R,et al.Modeling cascading failures in the North American power grid[J].The European Physical Journal,2005,46(1):101-107.
[3]倪向萍,梅生伟,张雪敏.基于复杂网络理论的输电线路脆弱度评估方法[J].电力系统自动化,2008,32(4):1-5.Ni Xiangping,Mei Shengwei,Zhang Xuemin.Transmission lines’vulnerability assessment based on complex network theory[J].Automation of Electric Power Systems,2008,32(4):1-5.
[4]丁明,过羿,张晶晶.基于效用风险熵权模糊综合评判的复杂电网节点脆弱性评估[J].电工技术学报,2015,30(3):214-223.Ding Ming,Guo Yi,Zhang Jingjing.Node Vulnerability Assessment for Complex Power Grids Based on Effect Risk Entropy-Weighted Fuzzy Comprehensive Evaluation[J].Transactions of China Electrotechnical Society,2015,30(3):214-223.
[5]Watts D J,Strogatz S H.Collective dynamics of‘small-world’networks[J].Nature,1998,393:440-442.
[6]Lu Z X,Meng Z W,Zhou S X.Cascading failure analysis of bulk power system using small-world network model[C].Proceedings of the 8th International Conference on Probabilistic Methods Applied to Power Systems,Iowa State University,Ames Iowa,2004.
[7]肖盛,张建华.基于小世界拓扑模型的电网脆弱性评估[J].电网技术,2010,34(8):64-68.Xiao Sheng,Zhang Jianhua.Assessment of Power Grid Vulnerability Based on Small-World Topological Model[J].Power System Technology,2010,34(8):64-68.
[8]丁明,过羿,张晶晶.基于效用风险熵的复杂电网连锁故障脆弱性辨识[J].电力系统自动化,2013,37(17):52-57.Ding Ming,Guo Yi,Zhang Jingjing.Vulnerability identification for cascading failures of complex power grid based on effect risk entropy[J].Automation of Electric Power Systems,2013,37(17):52-57.
[9]丁明,韩平平.加权拓扑模型下的小世界电网脆弱性评估[J].中国电机工程学报,2008,28(10):20-25.Ding Ming,Han Pingping.Vulnerability assessment to small-world power grid based on weighted topological model[J].Proceedings of the CSEE,2008,28(10):20-25.
[10]魏震波,刘俊勇,朱国俊,等.基于可靠性加权拓扑模型下的电网脆弱性评估模型[J].电工技术学报,2010,25(8):132-136.Wei Zhenbo,Liu Junyong,Zhu Guojun,et al.Vulnerability evaluation model to power grid based on reliability parameter weighted topological model[J].Transactions of China Electrotechnical Society,2010,25(8):132-136.
[11]李勇,刘俊勇,刘晓宇,等.基于潮流熵测度的连锁故障脆弱线路评估及其在四川主干电网中的应用[J].电力自动化设备,2013,33(10):40-46.Li Yong,Liu Junyong,Liu Xiaoyu,et al.Vulnerability assessment based on power flow entropy for lines in cascading failures and its application in Sichuan backbone power grid[J].Electric Power Automation Equipment,2013,33(10):40-46.
[12]杨帆.大规模消纳可再生能源对电网脆弱性影响的评价研究[D].华北电力大学,2016.Yang Fan.Evaluation research on influce of large-scale consumption of renewable energy to power grids vulnerability[D].North China Electric Power University,2016.
[13]老大中.变分法基础[M].北京:国防工业出版社,2007:1-20.
[14]史忠植.神经网络[M].北京:高等教育出版社,2009:140-164.
[15]周品.MATLAB神经网络设计与应用[M].北京:清华大学出版,2013:267-268.
[16]马东宇.基于Gaussian型RBF神经网络的函数逼近与应用[D].中南大学,2011.
[17]刘苏苏,孙立民.支持向量机与RBF神经网络回归性能比较研究[J].计算机工程与设计,2011,32(12):4202-4205.Liu Susu,Sun Limin.Performance comparison of regression prediction on support vector machine and RBF neural network[J],Computer Engineering and Design,2011,32(12):4202-4205.
[18]Simon Haykin,Adaptive Filter Theory[M].New York:Pearson Education Limited,2013:1-40.
[19]Min Gan,Hui Peng,Xiaoyan Peng,et al.A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling,Information Sciences 180(2010):4370-4383.
[20]V.Haggan,T.Ozaki,Modeling nonlinear random vibrations using an amplitude-dependent autoregressive time series model,Biometrika 68(1981):189-196.
[21]阮炯.差分方程和常微分方程[M].上海:复旦大学出版社,2002.