A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允...为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允许输送容量具有混沌特性,可运用混沌理论预测线路输送的容量。然后采用基于奇异值分解的混沌时间序列Volterra方法对一条安装有DLR系统的110kV线路可输送的容量及线路可能发生的热过载故障进行预测。预测结果显示该方法能够反映容量序列未来变化的趋势及线路发生热过载故障的风险性,提高了预测的精度,是有效、可行的。展开更多
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘为更准确预测输电线路未来允许的输送容量,提出利用动态提高输电线路输送容量(dynamic line rating,DLR)系统及混沌理论预测线路的输送容量。利用改进的C-C算法可靠性高、计算速度快的特点对容量时间序列进行相空间重构,并证实了线路允许输送容量具有混沌特性,可运用混沌理论预测线路输送的容量。然后采用基于奇异值分解的混沌时间序列Volterra方法对一条安装有DLR系统的110kV线路可输送的容量及线路可能发生的热过载故障进行预测。预测结果显示该方法能够反映容量序列未来变化的趋势及线路发生热过载故障的风险性,提高了预测的精度,是有效、可行的。
