针对输电线路杆塔电容取电系统在遭受雷击时的过电压问题,以新开发的110 k V输电线杆塔电容取电系统为研究对象,在介绍该电容取电系统基本工作原理、整机工频测试结果的基础上,进一步建立了取电系统的雷电暂态计算模型,利用电磁暂态计...针对输电线路杆塔电容取电系统在遭受雷击时的过电压问题,以新开发的110 k V输电线杆塔电容取电系统为研究对象,在介绍该电容取电系统基本工作原理、整机工频测试结果的基础上,进一步建立了取电系统的雷电暂态计算模型,利用电磁暂态计算程序ATP-EMTP软件对雷击下取电系统过电压进行仿真计算,确定了取电系统的雷电过电压水平,并提出相应的过电压保护措施。仿真结果表明,采用氧化锌避雷器和压敏电阻可以有效的限制雷电过电压幅值,实现对取电系统的过电压保护。展开更多
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
文摘针对输电线路杆塔电容取电系统在遭受雷击时的过电压问题,以新开发的110 k V输电线杆塔电容取电系统为研究对象,在介绍该电容取电系统基本工作原理、整机工频测试结果的基础上,进一步建立了取电系统的雷电暂态计算模型,利用电磁暂态计算程序ATP-EMTP软件对雷击下取电系统过电压进行仿真计算,确定了取电系统的雷电过电压水平,并提出相应的过电压保护措施。仿真结果表明,采用氧化锌避雷器和压敏电阻可以有效的限制雷电过电压幅值,实现对取电系统的过电压保护。
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.
