基于PMU的降阶二次状态估计算法研究
|
摘要
针对当前电力系统中同步相量测量单元(PMU)测量点少、无法直接进行状态估计的问题,提出一种新的状态估计算法.考虑到PMU系统量测精度比SCADA系统高,为了扩大高精度量测信息对状态估计结果的影响,算法将状态变量分成PMU可观测变量和PMU不可观测变量,并分别进行状态估计.对于PMU可观测变量,利用PMU量测量进行线性状态估计;对于PMU不可观测变量,利用线性状态估计的结果结合SCADA量测量进行降阶的快速分解状态估计.这种解耦的算法相对于传统混合状态估计算法计算更加简洁,并且有较高的数值稳定性.最后,利用多个IEEE标准测试系统对算法进行验证,通过与基本加权最小二乘法和快速分解算法对比,证明该算法在数值稳定性和精度上有很大优势.
At present,the phase measurement units were not sufficient for static state estimation in most existing power systems. A novel method was proposed to solve the problem in this paper. Realizing that PMU measurements were more accurate than SCADA ones,the proposed approach estimated PMU unobservable states and PMU observable states separately so as to expand the effect of PMU measurements on the result. For the PMU observable states,they were estimated by PMU measurements using a linear estimator. And the PMU unobservable states were estimated by conventional measurements in a reduced-order nonlinear estimator.When compared with conventional approaches the proposed decoupled method features reduced computational complexity and greater numerical stability. In the end,this method was verified that it had more advantages than weighed least square method and fast decomposition algorithm by simulations on standard IEEE test systems.
At present,the phase measurement units were not sufficient for static state estimation in most existing power systems. A novel method was proposed to solve the problem in this paper. Realizing that PMU measurements were more accurate than SCADA ones,the proposed approach estimated PMU unobservable states and PMU observable states separately so as to expand the effect of PMU measurements on the result. For the PMU observable states,they were estimated by PMU measurements using a linear estimator. And the PMU unobservable states were estimated by conventional measurements in a reduced-order nonlinear estimator.When compared with conventional approaches the proposed decoupled method features reduced computational complexity and greater numerical stability. In the end,this method was verified that it had more advantages than weighed least square method and fast decomposition algorithm by simulations on standard IEEE test systems.
引文
[1]于尔铿.电力系统状态估计[M].北京:水利电力出版社,1985.
[2]鞠萍.电力系统广域测量技术[M].北京:机械工业出版社,2008.7.
[3]DE L R J,CENTENO V,THORP J S,et al.Syn-chronized phasor measurement applications in power systems[J].IEEE transactions on smart grid,2010,1(1):20-27.
[4]卢志刚,许世范,史增洪,等.部分电压和电流相量可测量时电压相量的状态估计[J].电力系统自动化,2000,24(1):42-44.
[5]卓亮.基于PMU的电力系统动态状态估计研究[D].上海:上海交通大学电子信息与电气工程学院,2012.
[6]BI T S,QIN X H,YANG Q X.A novel hybrid state estimator for including synchronized phasor measurements[J].Electric power systems research,2008,78(8):1343-1352.
[7]秦晓辉,毕天姝,杨奇逊.计及PMU的混合非线性状态估计新方法[J].电力系统自动化,2007,31(4):28-32.
[8]林桂华,安天瑜,周苏荃,等.计及PMU量测信息的量测量变换状态估计[J].电网技术,2009,33(17):37-40.
[9]赵红嘎,薛禹胜,汪德星,等.计及PMU支路电流相量的状态估计模型[J].电力系统自动化,2004,28(17):37-40.
[10]厉超,卫志农,倪明,等.计及PMU量测的分步线性状态估计模型[J].电网技术,2014,38(6):1700-1704.
[11]颜磊,艾欣,王尧,等.一种将PMU支路电流量测引入非线性状态估计的方法[J].电网技术,2014,38(10):2816-2821.
[12]陈亮,赵思涵,索志刚,等.一种计及零注入电流的PMU量测线性状态估计方法[J].电力科学与工程,2016(2):26-31.
[13]闫丽梅,崔佳,徐建军,等.基于PMU/SCADA混合量测的电力系统求积分卡尔曼滤波的状态估计[J].电机与控制学报,2014,18(6):79-84.
[14]KASHYAP N,WERNER S,HUANG Y F,et al.Power system state estimation under incomplete PMU obser vability-a reduced-order approach[J].IEEE Journal of selected topics in signal processing,2014,8(6):1051-1062.
[15]鲍威,蒋雪冬,陈利跃,等.考虑观测冗余度最大的0-1线性规划电力系统PMU最优配置[J].电网技术,2014,38(8):2051-2056.
[16]丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法[J].中国电机工程学报,2006,26(2):58-63.
[17]王克文,蒋德珑,孙栗.电力系统状态估计算法的模糊综合评价与分析[J].郑州大学学报(工学版),2011,32(3):85-89.
[2]鞠萍.电力系统广域测量技术[M].北京:机械工业出版社,2008.7.
[3]DE L R J,CENTENO V,THORP J S,et al.Syn-chronized phasor measurement applications in power systems[J].IEEE transactions on smart grid,2010,1(1):20-27.
[4]卢志刚,许世范,史增洪,等.部分电压和电流相量可测量时电压相量的状态估计[J].电力系统自动化,2000,24(1):42-44.
[5]卓亮.基于PMU的电力系统动态状态估计研究[D].上海:上海交通大学电子信息与电气工程学院,2012.
[6]BI T S,QIN X H,YANG Q X.A novel hybrid state estimator for including synchronized phasor measurements[J].Electric power systems research,2008,78(8):1343-1352.
[7]秦晓辉,毕天姝,杨奇逊.计及PMU的混合非线性状态估计新方法[J].电力系统自动化,2007,31(4):28-32.
[8]林桂华,安天瑜,周苏荃,等.计及PMU量测信息的量测量变换状态估计[J].电网技术,2009,33(17):37-40.
[9]赵红嘎,薛禹胜,汪德星,等.计及PMU支路电流相量的状态估计模型[J].电力系统自动化,2004,28(17):37-40.
[10]厉超,卫志农,倪明,等.计及PMU量测的分步线性状态估计模型[J].电网技术,2014,38(6):1700-1704.
[11]颜磊,艾欣,王尧,等.一种将PMU支路电流量测引入非线性状态估计的方法[J].电网技术,2014,38(10):2816-2821.
[12]陈亮,赵思涵,索志刚,等.一种计及零注入电流的PMU量测线性状态估计方法[J].电力科学与工程,2016(2):26-31.
[13]闫丽梅,崔佳,徐建军,等.基于PMU/SCADA混合量测的电力系统求积分卡尔曼滤波的状态估计[J].电机与控制学报,2014,18(6):79-84.
[14]KASHYAP N,WERNER S,HUANG Y F,et al.Power system state estimation under incomplete PMU obser vability-a reduced-order approach[J].IEEE Journal of selected topics in signal processing,2014,8(6):1051-1062.
[15]鲍威,蒋雪冬,陈利跃,等.考虑观测冗余度最大的0-1线性规划电力系统PMU最优配置[J].电网技术,2014,38(8):2051-2056.
[16]丁军策,蔡泽祥,王克英.基于广域测量系统的混合量测状态估计算法[J].中国电机工程学报,2006,26(2):58-63.
[17]王克文,蒋德珑,孙栗.电力系统状态估计算法的模糊综合评价与分析[J].郑州大学学报(工学版),2011,32(3):85-89.