01444nas a2200169 4500008003900000245009500039210006900134260003600203300001000239520085000249653000901099653001001108100002401118700003601142700002101178856007501199 2014 d00aGeneralized injection shift factors and application to estimation of power flow transients0 aGeneralized injection shift factors and application to estimatio aPullman, WA, USAbIEEEc09/2014 a1 - 53 aThis paper proposes a method to estimate transmission line flows in a power system during the transient period following a loss of generation or increase in load contingency by using linear sensitivity injection shift factors (ISFs). Traditionally, ISFs are computed from an offline power flow model of the system with the slack bus defined. The proposed method, however, relies on generalized ISFs estimated via the solution of a system of linear equations that arise from high-frequency synchronized measurements obtained from phasor measurement units. Even though the generalized ISFs are obtained at the pre-disturbance steady-state operating point, by leveraging inertial and governor power flows during appropriate time-scales, they can be manipulated to predict active transmission line flows during the post-contingency transient period.10aAARD10aCERTS1 aChen, Yu, Christine1 aDominguez-Garcia, Alejandro, D.1 aSauer, Peter, W. uhttps://certs.lbl.gov/publications/generalized-injection-shift-factors01636nas a2200241 4500008003900000022001400039245009300053210006900146260001100215300001600226490000700242520084900249653000901098653003901107653001001146653001801156653003601174653002801210100002401238700003601262700002101298856007501319 2014 d a0885-895000aMeasurement-Based Estimation of Linear Sensitivity Distribution Factors and Applications0 aMeasurementBased Estimation of Linear Sensitivity Distribution F c5/2014 a1372 - 13820 v293 aIn this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real-time. The method does not rely on the system power flow model. Instead, it uses only high-frequency synchronized data collected from phasor measurement units to estimate the injection shift factors through linear least-squares estimation, after which other DFs can be easily computed. Such a measurement-based approach is desirable since it is adaptive to changes in system operating point and topology. We further improve the adaptability of the proposed approach to such changes by using weighted and recursive least-squares estimation. Through numerical examples, we illustrate the advantages of our proposed DF estimation approach over the conventional model-based one in the context of contingency analysis and generation re-dispatch.10aAARD10aAutomatic Switchable Network (ASN)10aCERTS10aload modeling10aphasor measurement units (PMUs)10apower system monitoring1 aChen, Yu, Christine1 aDominguez-Garcia, Alejandro, D.1 aSauer, Peter, W. uhttps://certs.lbl.gov/publications/measurement-based-estimation-linear01689nas a2200169 4500008003900000022001400039245009500053210006900148260001200217300001100229520111200240653000901352100002401361700003601385700002101421856007701442 2014 d a0885-895000aA Sparse Representation Approach to Online Estimation of Power System Distribution Factors0 aSparse Representation Approach to Online Estimation of Power Sys c10/2014 a1 - 123 aIn this paper, we propose a method to compute linear sensitivity distribution factors (DFs) in near real time without relying on a power flow model of the system. Specifically, we compute the injection shift factors (ISFs) of a particular line of interest with respect to active power injections at all buses (all other DFs can be determined from ISFs). The proposed ISF estimation method relies on the solution of an underdetermined system of linear equations that arise from high-frequency synchronized measurements obtained from phasor measurement units. We exploit a sparse representation (i.e., one in which many elements are zero) of the vector of desired ISFs via rearrangement by electrical distance and an appropriately chosen linear transformation, and cast the estimation problem into a sparse vector recovery problem. As we illustrate through case studies, the proposed approach provides accurate DF estimates with fewer sets of synchronized measurements than earlier approaches that rely on the solution of an overdetermined system of equations via the least-squares errors estimation method.
10aAARD1 aChen, Yu, Christine1 aDominguez-Garcia, Alejandro, D.1 aSauer, Peter, W. uhttps://certs.lbl.gov/publications/sparse-representation-approach-online01617nas a2200217 4500008003900000245009600039210006900135260003800204300001000242520084800252653000901100653003901109653001001148653002501158653003601183653002901219100002401248700003601272700002101308856007001329 2013 d00aOnline estimation of power system distribution factors — A sparse representation approach0 aOnline estimation of power system distribution factors A sparse aManhattan, KS, USAbIEEEc09/2013 a1 - 53 aThis paper proposes a method to compute linear sensitivity distribution factors (DFs) in near real-time without relying on a power flow model of the system. Instead, the proposed method relies on the solution of an underdetermined system of linear equations that arise from high-frequency synchronized measurements obtained from phasor measurement units. In particular, we exploit a sparse representation (i.e., one in which many elements are zero) of the desired DFs obtained via a linear transformation, and cast the estimation problem as an IO-norm minimization. As we illustrate through examples, the proposed approach is able to provide accurate DF estimates with fewer sets of synchronized measurements than earlier approaches that rely on the solution of an overdetermined system of equations via the least-squares errors method.

10aAARD10aAutomatic Switchable Network (ASN)10aCERTS10adistribution factors10aphasor measurement units (PMUs)10apower system reliability1 aChen, Yu, Christine1 aDominguez-Garcia, Alejandro, D.1 aSauer, Peter, W. uhttps://certs.lbl.gov/publications/online-estimation-power-system