基于复合功角及稳定裕度的多机系统分群研究
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摘要
在多机电力系统中,首先需要进行机组分群的研究,才能开展后续的低频振荡分析、暂态稳定分析及动态等值计算等。根据实测的各机组功角、角速度、功率等状态量计算复合功角,通过复合功角间隙筛选多机系统的主导模式,然后选取其中稳定裕度最小的主导模式作为最终分群结果。最后通过WEPRI-36系统的仿真计算,验证了无论针对多机系统的失稳情况还是稳定情况,该方法都可进行快速有效的分群及等值计算。
It is necessary to study generator groups identification before the analysis of low frequency oscillation, transient stability and dynamic equivalent. Complex angle is calculated based on real-time measured angle, angular velocity and unbalanced power. Three dominant modes can be obtained by complex angle gap. The final identification is the dominant mode which has the lowest stability margin. Case studies on the WEPRI-36 power system are provided to illustrate that the method can be used for fast and effective group identification and equivalent calculation whatever the instability or stability of generator groups.
It is necessary to study generator groups identification before the analysis of low frequency oscillation, transient stability and dynamic equivalent. Complex angle is calculated based on real-time measured angle, angular velocity and unbalanced power. Three dominant modes can be obtained by complex angle gap. The final identification is the dominant mode which has the lowest stability margin. Case studies on the WEPRI-36 power system are provided to illustrate that the method can be used for fast and effective group identification and equivalent calculation whatever the instability or stability of generator groups.
引文
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[3]WU F F,TSAI Y K.Identification of groups ofε-coherent generators[J].IEEE Transactions on Circuits and Systems,1983,30(4):234-241.
[4]郭鹏,刘文颖,但扬清,等.基于模糊聚类的可调节/可中断负荷辨识[J].电网与清洁能源,2017,33(1):31-37.GUO Peng,LIU Wenying,DAN Yangqing,et al.Adjustable and interruptible load identification based on fuzzy clustering[J].Power System and Clean Energy,2017,33(1):31-37.
[5]杜晓岩,尹华杰,叶超,等.基于改进模糊C均值聚类和MPSO的风电场等值研究[J].广东电力,2016,29(8):38-44.DU Xiaoyan,YIN Huajie,YE Chao,et al.Equivalent model of wind power farm based on improved fuzzy c means clustering and MPSO[J].Guangdong Electric Power,2016,29(8):38-44.
[6]吴钢,徐枫,王冰,等.基于遗传算法和模糊聚类算法的改进BP神经网络风速预测[J].电子设计工程,2016,24(11):120-123.WU Gang,XU Feng,WANG Bing,et al.The improved BP network wind speed forecasting method based on gemetic algorithm and fuzzy c-means algorithm[J].Electronic Design Engineering,2016,24(11):120-123.
[7]王华芳,卫志农,杨博,等.利用基于模糊划分的ISODATA模糊聚类方法识别电力系统同调机群[J].电网技术,2005,29(2):19-22.WANG Huafang,WEI Zhinong,YANG Bo,et al.An ISODATA fuzzy clustering method based on fuzzy partition to recognizing coherent generator groups[J].Power System Technology,2005,29(2):19-22.
[8]姜伊欣.基于GOOSE技术与模糊算法的智能继电保护方案设计[J].电子设计工程,2018,26(4):145-149.JIANG Yixin.Design of intelligent relay protection scheme based on GOOSE technology and fuzzy algorithm[J].Electronic Design Engineering,2018,26(4):145-149.
[9]薛禹胜,潘学萍,ZHANG Guorui,等.计及时变系统完整非线性的振荡模式分析[J].电力系统自动化,2008,32(18):1-7.XUE Yusheng,PAN Xueping.ZHANG Guorui,et al.Inclusion of full nonlinearity for oscillation mode analysis[J].Automation of Electric Power Systems,2008,32(18):1-7.
[10]潘学萍,薛禹胜,张晓明.轨迹特征根的解析估算及其误差分析[J].电力系统自动化,2008,32(19):10-14.PAN Xueping,XUE Yusheng,ZHANG Xiaoming.Analytical calculation of power system trajectory eigenvalues and its error analysis[J].Automation of Electric Power Systems,2008,32(19):10-14.
[11]郝思鹏,薛禹胜,唐茂林,等.通过轨迹特征根分析时变振荡特性[J].电力系统自动化,2009,33(6):1-5.HAO Sipeng,XUE Yusheng,TANG Maolin,et al.Trajectory eigenvalues analysis time variant oscillationcharacters[J].Automation of Electric Power Systems,2009,33(6):1-5.
[12]楼伯良,邓晖,华文,等.基于多项式模型的受扰电压轨迹快速预测方法[J].智慧电力,2017,45(11):85-90.LOU Bolang,DENG Hui,HU A Wen,et al.Perturbed voltage trajectory prediction method based on polynomial model[J].Smart Power,2017,45(11):85-90.
[13]王晨辉,张晓亮,梁晓传.云计算架构下基于BP神经网络负载预测策略的研究[J].电力信息与通信技术,2016,14(11):46-50.WANG Chenhui,ZHANG Xiaoliang,LIANG Xiaochuan.Research on load forecasting strategy based on BP neural network under cloud computing architecture[J].Electric Power Information and Communication Technology,2016,14(11):46-50.