PCA-PSO-ELM配网供电可靠性预测模型
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摘要
为了提升配网供电可靠性的预测精度,提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠性预测模型。从多方面分析影响供电可靠性的指标,利用主成分分析得到综合变量,实现对数据的降维。在此基础上,构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值,完成对训练供电可靠性预测模型的训练。以某大型电网的47个供电局样本30种影响供电可靠性因素为例进行仿真分析,并将PCA-PSO-ELM算法与3种回归拟合算法对比,验证了该方法的有效性。模型充分考虑了多方面的供电可靠性影响因素,适用于多输入变量的情况,对于引导供电企业制定可靠性提升策略提供了科学有效的参考依据。
To enhance the predictive precision of power supply reliability in a distribution grid,a prediction model of power supply reliability in a distribution grid was proposed via principal component analysis(PCA) and particle swarm optimization extremity learning machine(PSO-ELM). First,the impact factors of power supply reliability were analyzed from various aspects. The linear combinations of the original variables were used to obtain the comprehensive variables for realizing the dimension reduction of the data. On the basis of data pretreatment,the artificial neural network was established,and PSO was applied to optimize the input weights and threshold values of ELM to complete model training. Finally,30 kinds of impact factors of power supply reliability of the samples,which were obtained from 47 power supply bureaus of a large power grid,were used for simulative analysis. The PCA-PSO-ELM algorithm was compared with three kinds of regressive fitting algorithms to verify the validity of the method. The prediction model could fully consider the impact factors of power supply reliability in many aspects. It is applicable for multiple input variables and can provide a scientific and effective reference to guide power supply enterprises in working out a reliability lifting strategy.
To enhance the predictive precision of power supply reliability in a distribution grid,a prediction model of power supply reliability in a distribution grid was proposed via principal component analysis(PCA) and particle swarm optimization extremity learning machine(PSO-ELM). First,the impact factors of power supply reliability were analyzed from various aspects. The linear combinations of the original variables were used to obtain the comprehensive variables for realizing the dimension reduction of the data. On the basis of data pretreatment,the artificial neural network was established,and PSO was applied to optimize the input weights and threshold values of ELM to complete model training. Finally,30 kinds of impact factors of power supply reliability of the samples,which were obtained from 47 power supply bureaus of a large power grid,were used for simulative analysis. The PCA-PSO-ELM algorithm was compared with three kinds of regressive fitting algorithms to verify the validity of the method. The prediction model could fully consider the impact factors of power supply reliability in many aspects. It is applicable for multiple input variables and can provide a scientific and effective reference to guide power supply enterprises in working out a reliability lifting strategy.
引文
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[10]HUANG Guangbin,WANG Dianhui,LAN Yuan.Extreme learning machines:a survey[J].International journal of machine learning and cybernetics,2011,2(2):107-122.
[11]虞英杰.基于PSO算法的BP神经网络模型对水体富营养化预测的研究[D].广州:暨南大学,2011.YU Yingjie.Prediction of eutrophication by BP neural network model based on PSO algorithm[D].Guangzhou:Jinan University,2011.
[12]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014,27(12):1710-1715.FU Hua,WANG Xinrui,WANG Zhijun,et al.Research on the soft sensor of coal and gas outburst based on PCA and PSO-ELM[J].Chinese journal of sensors and actuators,2014,27(12):1710-1715.
[13]Std.IEEE.IEEE Std 1366-2012,IEEE Guide for electric power distribution reliability indices[S].New York:IEEE,2012:1-40.
[14]瞿海妮,刘建清.国内外配电网供电可靠性指标比较分析[J].华东电力,2012,40(9):1566-1570.QU Haini,LIU Jianqing.Comparison and analysis on power supply reliability indicators of distribution network at home and abroad[J].East China electric power,2012,40(9):1566-1570.
[15]CHEN Hongming,XIAO Xiaocan.The application of SPSS factor analysis in the evaluation of corporate social responsibility[J].Journal of software,2012,7(6):1258-1264.
[2]BILLINTON R,ALLAN R N.Reliability evaluation of engineering systems:concepts and techniques[M].2nd ed.New York:Plenum Press,1992.
[3]宋云亭,张东霞,吴俊玲,等.国内外城市配电网供电可靠性对比分析[J].电网技术,2008,32(23):13-18.SONG Yunting,ZHANG Dongxia,WU Junling,et al.Comparison and analysis on power supply reliability of urban power distribution network at home and abroad[J].Power system technology,2008,32(23):13-18.
[4]曹伟.10k V配电网规划的供电可靠性评估和应用[D].长沙:湖南大学,2009.CAO Wei.Reliability assessment in 10k V distribution power network planning and its application[D].Changsha:Hunan University,2009.
[5]谷群辉,罗安,王击,等.一种实用的供电可靠性预测评估算法[J].电网技术,2003,27(12):76-79.GU Qunhui,LUO An,WANG Ji,et al.A practicable algorithm to forecast and evaluate reliability of power supply[J].Power system technology,2003,27(12):76-79.
[6]宋云亭,吴俊玲,彭冬,等.基于BP神经网络的城网供电可靠性预测方法[J].电网技术,2008,32(20):56-59.SONG Yunting,WU Junling,PENG Dong,et al.A BP neural network based method to predict power supply reliability of urban power network[J].Power system technology,2008,32(20):56-59.
[7]叶航超.支持向量机在电力系统可靠性分析中的应用研究[D].杭州:浙江大学,2013.YE Hangchao.The research of power system reliability analysis basins on SVM[D].Hangzhou:Zhejiang University,2013.
[8]刘亚南,卫志农,钟淋涓,等.基于PCA和RVM的电网供电可靠性预测模型研究[J].电力系统保护与控制,2012,40(20):101-105,112.LIU Ya'nan,WEI Zhinong,ZHONG Linjuan,et al.Study on the forecasting model of power supply reliability based on PCA and RVM[J].Power system protection and control,2012,40(20):101-105,112.
[9]赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2011,32(1):109-113.ZHAO Chunhui,HU Chunmei,SHI Hong.Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm[J].Journal of Harbin engineering university,2011,32(1):109-113.
[10]HUANG Guangbin,WANG Dianhui,LAN Yuan.Extreme learning machines:a survey[J].International journal of machine learning and cybernetics,2011,2(2):107-122.
[11]虞英杰.基于PSO算法的BP神经网络模型对水体富营养化预测的研究[D].广州:暨南大学,2011.YU Yingjie.Prediction of eutrophication by BP neural network model based on PSO algorithm[D].Guangzhou:Jinan University,2011.
[12]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014,27(12):1710-1715.FU Hua,WANG Xinrui,WANG Zhijun,et al.Research on the soft sensor of coal and gas outburst based on PCA and PSO-ELM[J].Chinese journal of sensors and actuators,2014,27(12):1710-1715.
[13]Std.IEEE.IEEE Std 1366-2012,IEEE Guide for electric power distribution reliability indices[S].New York:IEEE,2012:1-40.
[14]瞿海妮,刘建清.国内外配电网供电可靠性指标比较分析[J].华东电力,2012,40(9):1566-1570.QU Haini,LIU Jianqing.Comparison and analysis on power supply reliability indicators of distribution network at home and abroad[J].East China electric power,2012,40(9):1566-1570.
[15]CHEN Hongming,XIAO Xiaocan.The application of SPSS factor analysis in the evaluation of corporate social responsibility[J].Journal of software,2012,7(6):1258-1264.