According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
独立直流微电网中存在的大量恒功率负载(constant power load,CPL)会减小直流微网的阻尼,易引发母线电压的振荡甚至大幅跌落。为此,提出一种有源阻尼方法来提高系统的稳定性,在不增加传感器的前提下,通过在储能电池变换器端口处并联虚...独立直流微电网中存在的大量恒功率负载(constant power load,CPL)会减小直流微网的阻尼,易引发母线电压的振荡甚至大幅跌落。为此,提出一种有源阻尼方法来提高系统的稳定性,在不增加传感器的前提下,通过在储能电池变换器端口处并联虚拟电阻来提高变换器的阻尼,从而抑制其谐振峰值;给出有源阻尼参数的设计方法,并利用频率稳定判据分析所提并联虚拟电阻方法对系统稳定性的影响;还通过仿真对比传统的阻尼方法与本文所提方法的性能。实验结果验证了并联虚拟电阻方法可在较宽频段有效提高独立直流微电网系统稳定性。展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金Supported by National Natural Science Foundation of China （60674039, 60704004） and Innovation Fund for Outstanding Scholar of Henan Province （084200510009 ）
文摘独立直流微电网中存在的大量恒功率负载(constant power load,CPL)会减小直流微网的阻尼,易引发母线电压的振荡甚至大幅跌落。为此,提出一种有源阻尼方法来提高系统的稳定性,在不增加传感器的前提下,通过在储能电池变换器端口处并联虚拟电阻来提高变换器的阻尼,从而抑制其谐振峰值;给出有源阻尼参数的设计方法,并利用频率稳定判据分析所提并联虚拟电阻方法对系统稳定性的影响;还通过仿真对比传统的阻尼方法与本文所提方法的性能。实验结果验证了并联虚拟电阻方法可在较宽频段有效提高独立直流微电网系统稳定性。
