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-oscillations02020nas a2200253 4500008003900000022001400039245008400053210006900137260001200206300001400218490000700232520124900239653001301488653000901501653003901510653003601549653001801585100001701603700001501620700002101635700001801656700002001674856007201694 2013 d a0885-895000aMode shape estimation algorithms under ambient conditions: A comparative review0 aMode shape estimation algorithms under ambient conditions A comp c05/2013 a779 - 7870 v283 aThis paper provides a comparative review of five existing ambient electromechanical mode shape estimation algorithms, i.e., the Transfer Function (TF), Spectral, Frequency Domain Decomposition (FDD), Channel Matching, and Subspace Methods. It is also shown that the TF Method is a general approach to estimating mode shape and that the Spectral, FDD, and Channel Matching Methods are actually special cases of it. Additionally, some of the variations of the Subspace Method are reviewed and the Numerical algorithm for Subspace State Space System IDentification (N4SID) is implemented. The five algorithms are then compared using data simulated from a 17-machine model of the Western Electricity Coordinating Council (WECC) under ambient conditions with both low and high damping, as well as during the case where ambient data is disrupted by an oscillatory ringdown. The performance of the algorithms is compared using the statistics from Monte Carlo simulations and results from measured WECC data, and a discussion of the practical issues surrounding their implementation, including cases where power system probing is an option, is provided. The paper concludes with some recommendations as to the appropriate use of the various techniques.10aAA07-00110aAARD10aAutomatic Switchable Network (ASN)10aphasor measurement units (PMUs)10apower systems1 aDosiek, Luke1 aZhou, Ning1 aPierre, John, W.1 aHuang, Zhenyu1 aTrudnowski, Dan uhttps://certs.lbl.gov/publications/mode-shape-estimation-algorithms01125nas a2200181 4500008003900000245008400039210006900123260003300192300001000225520052300235653001300758653000900771653003600780100001500816700002100831700002000852856007100872 2013 d00aSome considerations in using Prony analysis to estimate electromechanical modes0 aSome considerations in using Prony analysis to estimate electrom aVancouver, BCbIEEEc07/2013 a1 - 53 aProny analysis has been used to estimate oscillation modes from ringdown responses in a power grid. When applying Prony analysis, several factors must be considered to estimate the modes accurately. In this paper, a general prediction model is proposed for the Prony analysis. The influence of decimation factors, model orders, and linear solvers on estimation accuracy is studied using the Monte Carlo method with a goal of providing a reference for applying Prony analysis to estimate electromechanical modes.

10aAA07-00110aAARD10aphasor measurement units (PMUs)1 aZhou, Ning1 aPierre, John, W.1 aTrudnowski, Dan uhttps://certs.lbl.gov/publications/some-considerations-using-prony01837nas a2200241 4500008003900000022001400039245008100053210006900134260001200203300001600215490000700231520109000238653001301328653000901341653003901350653003601389653002701425653001001452100001501462700002101477700002001498856007701518 2012 d a0885-895000aA Stepwise Regression Method for Estimating Dominant Electromechanical Modes0 aStepwise Regression Method for Estimating Dominant Electromechan c05/2012 a1051 - 10590 v273 aProny analysis has been applied to estimate inter-area oscillation modes using phasor measurement unit (PMU) measurements. To suppress noise and signal offset effects, a high-order Prony model usually is used to over-fit the data. As such, some trivial modes are intentionally added to improve the estimation accuracy of the dominant modes. Therefore, to reduce the rate of false alarms, it is important to distinguish between the dominant modes that reflect the dynamic features of a power system and the trivial modes that are artificially introduced to improve the estimation accuracy. In this paper, a stepwise-regression method is applied to automatically identify the dominant modes from Prony analysis. A Monte Carlo method is applied to evaluate the performance of the proposed method using data obtained from simulations. Field-measured PMU data are used to verify the applicability of the proposed method. A comparison of results obtained using the proposed approach with results from a traditional energy-sorting method shows the improved performance of the proposed method.10aAA07-00110aAARD10aAutomatic Switchable Network (ASN)10aphasor measurement units (PMUs)10aPower system stability10aRTGRM1 aZhou, Ning1 aPierre, John, W.1 aTrudnowski, Dan uhttps://certs.lbl.gov/publications/stepwise-regression-method-estimating01513nas 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-accuracy01760nas a2200253 4500008003900000020002200039245011400061210006900175260003500244300001000279520090000289653001301189653000901202653003901211653002101250653003601271653002801307100001501335700001801350700002501368700002101393700002201414856007001436 2010 d a978-1-4244-6549-100aAutomatic implementation of Prony analysis for electromechanical mode identification from phasor measurements0 aAutomatic implementation of Prony analysis for electromechanical aMinneapolis, MNbIEEEc07/2010 a1 - 83 aSmall signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper develops a recursive algorithm for implementing Prony analysis and propose an oscillation detection method to detect ringdown data in real time. By automatically detecting ringdown data, the proposed method helps to guarantee that Prony analysis is properly and timely applied on the ringdown data. Thus, the mode estimation results can be performed reliably and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis.

10aAA07-00110aAARD10aAutomatic Switchable Network (ASN)10agrid reliability10aphasor measurement units (PMUs)10apower system monitoring1 aZhou, Ning1 aHuang, Zhenyu1 aTuffner, Francis, K.1 aPierre, John, W.1 aJin, Shuangshuang uhttps://certs.lbl.gov/publications/automatic-implementation-prony02086nas a2200253 4500008003900000022001400039245005800053210005800111260001200169300001400181490000700195520135600202653001301558653000901571653003901580653000901619100002101628700001501649700002501664700002001689700002001709700002901729856007401758 2010 d a0885-895000aProbing Signal Design for Power System Identification0 aProbing Signal Design for Power System Identification c05/2010 a835 - 8430 v253 aThis paper investigates the design of effective input signals for low-level probing of power systems. In 2005, 2006, and 2008 the Western Electricity Coordinating Council (WECC) conducted four large-scale system-wide tests of the western interconnected power system where probing signals were injected by modulating the control signal at the Celilo end of the Pacific DC intertie. A major objective of these tests is the accurate estimation of the inter-area electromechanical modes. A key aspect of any such test is the design of an effective probing signal that leads to measured outputs rich in information about the modes. This paper specifically studies low-level probing signal design for power-system identification. The paper describes the design methodology and the advantages of this new probing signal which was successfully applied during these tests. This probing input is a multi-sine signal with its frequency content focused in the range of the inter-area modes. The period of the signal is over 2 min providing high-frequency resolution. Up to 15 cycles of the signal are injected resulting in a processing gain of 15. The resulting system response is studied in the time and frequency domains. Because of the new probing signal characteristics, these results show significant improvement in the output SNR compared to previous tests.10aAA07-00110aAARD10aAutomatic Switchable Network (ASN)10aWECC1 aPierre, John, W.1 aZhou, Ning1 aTuffner, Francis, K.1 aHauer, John, F.1 aTrudnowski, Dan1 aMittelstadt, William, A. uhttps://certs.lbl.gov/publications/probing-signal-design-power-system01776nas a2200241 4500008003900000020002200039245010900061210006900170260003500239300001000274520096800284653001301252653000901265653003901274653003601313653002601349100001501375700001801390700001701408700002001425700002101445856006801466 2009 d a978-1-4244-4241-600aElectromechanical mode shape estimation based on transfer function identification using PMU measurements0 aElectromechanical mode shape estimation based on transfer functi aCalgary, CanadabIEEEc07/2009 a1 - 73 aPower system mode shapes are a key indication of how dynamic components participate in low-frequency oscillations. Traditionally, mode shapes are calculated from a linearized dynamic model. For large-scale power systems, obtaining accurate dynamic models is very difficult. Therefore, measurement-based mode shape estimation methods have certain advantages, especially for the application of real-time small signal stability monitoring. In this paper, a measurement-based mode shape identification method is proposed. The general relationship between transfer function (TF) and mode shape is derived. As an example, a least square (LS) method is implemented to estimate mode shape using an autoregressive exogenous (ARX) model. The performance of the proposed method is evaluated by Monte-Carlo studies using simulation data from a 17-machine model. The results indicate the validity of the proposed method in estimating mode shapes with reasonably good accuracy.10aAA07-00110aAARD10aAutomatic Switchable Network (ASN)10aphasor measurement units (PMUs)10aPower system modeling1 aZhou, Ning1 aHuang, Zhenyu1 aDosiek, Luke1 aTrudnowski, Dan1 aPierre, John, W. uhttps://certs.lbl.gov/publications/electromechanical-mode-shape