An algorithm for the detection and frequency estimation of periodic forced oscillations in power systems is proposed. The method operates by comparing the periodogram of synchrophasor measurements to a detection threshold. This threshold is established by deriving a general expression for the distribution of the periodogram and is related to the algorithm's probabilities of false alarm and detection. Unlike classic detection algorithms designed for use with white Gaussian noise, the proposed algorithm uses a detection threshold that varies with frequency to account for the colored nature of synchrophasor measurements. Further, a detection method based on multiple segments of data is also proposed to improve the algorithm's performance as a monitoring tool in the online environment. A design approach that helps to ensure that the best available probability of detection from any one detection segment is constantly increasing with the duration of the forced oscillation is also developed. Results from application of the detection algorithm to simulated and measured power system data suggest that the algorithm provides the expected detection performance and can be used to detect forced oscillations in practical monitoring of power systems.

10aAA09-0021 aFollum, Jim1 aPierre, John, W. uhttps://certs.lbl.gov/publications/detection-periodic-forced01157nas a2200145 4500008003900000245006200039210006100101260003500162300001000197520067700207653001300884100001600897700002100913856007700934 2015 d00aTime-localization of forced oscillations in power systems0 aTimelocalization of forced oscillations in power systems aDenver, CO, USAbIEEEc07/2015 a1 - 53 aIn power systems forced oscillations occur, and identification of these oscillations is important for the proper operation of the system. Two of the parameters of interest in analyzing and addressing forced oscillations are the starting and ending points. To obtain estimates of these parameters, this paper proposes a time-localization algorithm based on the geometric analysis of the sample cross-correlation between the measured data and a complex sinusoid at the frequency of the forced oscillation. Results from simulated and measured synchrophasor data demonstrate the algorithm's ability to accurately estimate the starting and ending points of forced oscillations.10aAA09-0021 aFollum, Jim1 aPierre, John, W. uhttps://certs.lbl.gov/publications/time-localization-forced-oscillations01305nas a2200217 4500008003900000022001400039245012600053210006900179260001200248300001600260490000700276520060500283653001300888653000900901653003900910653001700949100001600966700001500982700001900997856007101016 2013 d a1532-500800aA Generalized Subspace Least Mean Square Method for High-resolution Accurate Estimation of Power System Oscillation Modes0 aGeneralized Subspace Least Mean Square Method for Highresolution c09/2013 a1205 - 12120 v413 aA generalized subspace least mean square method is presented for accurate and robust estimation of oscillation modes from exponentially damped power system signals. The method is based on the orthogonality of signal and noise eigenvectors of the signal autocorrelation matrix. Performance of the proposed method is evaluated using Monte Carlo simulation and compared with the Prony method. Test results show that the generalized subspace least mean square method is highly resilient to noise and significantly dominates the Prony method in tracking power system modes under noisy environments.

10aAA09-00210aAARD10aAutomatic Switchable Network (ASN)10aoscillations1 aZhang, Peng1 aZhou, Ning1 aAbdollahi, Ali uhttps://certs.lbl.gov/publications/generalized-subspace-least-mean01513nas a2200217 4500008003900000020002200039245007900061210006900140260003500209300001000244520080700254653001301061653000901074653003901083653001801122653002801140100001601168700001501184700002101199856007501220 2011 d a978-1-4577-0417-800aEvaluation of mode estimation accuracy for small-signal stability analysis0 aEvaluation of mode estimation accuracy for smallsignal stability aBoston, MA, USAbIEEEc08/2011 a1 - 73 aThis paper proposes a method for determining electromechanical mode estimate accuracy by relating mode estimate error to residual values. Mode frequency and damping ratio were estimated using Prony analysis and residuals were calculated for a 17-machine model with varying levels of load noise. Mode estimate error and residuals were found to increase proportionally to each other as noise values were increased, revealing a distinctly linear relationship. The use of these results to develop appropriate confidence in models is discussed. With the relationship established, a method of predicting mode estimate error values based on residuals in the western North American power system (wNAPS) was developed. The potential of this method to evaluate the confidence level of mode estimates is examined.10aAA09-00210aAARD10aAutomatic Switchable Network (ASN)10aload modeling10apower system monitoring1 aFollum, Jim1 aZhou, Ning1 aPierre, John, W. uhttps://certs.lbl.gov/publications/evaluation-mode-estimation-accuracy01571nas a2200241 4500008003900000020002200039245008200061210006900143260003600212300001000248520076300258653001301021653000901034653003901043653003601082653002901118100001501147700001801162700002501180700002001205700002901225856007501254 2011 d a978-1-4577-1000-100aA modified stepwise linear regression method for estimating modal sensitivity0 amodified stepwise linear regression method for estimating modal aDetroit, MI, USAbIEEEc07/2011 a1 - 73 aSmall signal stability problems are one of the major threats to grid stability and reliability. Low damping of inter area modes is usually considered to be a result of heavy power transfer over long distances. This paper proposes a modified stepwise regression method to estimate the modal sensitivity with respect to power flow on the transmission lines based on measurement. This sensitivity is used to identify dominant transmission lines, whose power flow has significant influence on the inter-area modal damping. It is shown through simulation study that the proposed method can effectively estimate the modal sensitivity with respect to line power flow. This, in turn, provides insight on how to improve damping through adjusting tie line flow.

10aAA09-00210aAARD10aAutomatic Switchable Network (ASN)10aphasor measurement units (PMUs)10apower system reliability1 aZhou, Ning1 aHuang, Zhenyu1 aTuffner, Francis, K.1 aTrudnowski, Dan1 aMittelstadt, William, A. uhttps://certs.lbl.gov/publications/modified-stepwise-linear-regression