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.

%B IEEE Transactions on Power Systems %P 1 - 11 %8 08/2015 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2015.2456919 %0 Conference Paper %B 2015 IEEE Power & Energy Society General Meeting2015 IEEE Power & Energy Society General Meeting %D 2015 %T Time-localization of forced oscillations in power systems %A Jim Follum %A John W. Pierre %K AA09-002 %X In 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. %B 2015 IEEE Power & Energy Society General Meeting2015 IEEE Power & Energy Society General Meeting %I IEEE %C Denver, CO, USA %P 1 - 5 %8 07/2015 %R 10.1109/PESGM.2015.7286129 %0 Journal Article %J IEEE Transactions on Power Systems %D 2013 %T Mode shape estimation algorithms under ambient conditions: A comparative review %A Luke Dosiek %A Ning Zhou %A John W. Pierre %A Zhenyu Huang %A Dan Trudnowski %K AA07-001 %K AARD %K Automatic Switchable Network (ASN) %K phasor measurement units (PMUs) %K power systems %X This 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. %B IEEE Transactions on Power Systems %V 28 %P 779 - 787 %8 05/2013 %N 2 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2012.2210570 %0 Conference Paper %B 2013 IEEE Power & Energy Society (PES) General Meeting %D 2013 %T Some considerations in using Prony analysis to estimate electromechanical modes %A Ning Zhou %A John W. Pierre %A Dan Trudnowski %K AA07-001 %K AARD %K phasor measurement units (PMUs) %XProny 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.

%B 2013 IEEE Power & Energy Society (PES) General Meeting %I IEEE %C Vancouver, BC %P 1 - 5 %8 07/2013 %R 10.1109/PESMG.2013.6672888 %0 Journal Article %J IEEE Transactions on Power Systems %D 2012 %T A Stepwise Regression Method for Estimating Dominant Electromechanical Modes %A Ning Zhou %A John W. Pierre %A Dan Trudnowski %K AA07-001 %K AARD %K Automatic Switchable Network (ASN) %K phasor measurement units (PMUs) %K Power system stability %K RTGRM %X Prony 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. %B IEEE Transactions on Power Systems %V 27 %P 1051 - 1059 %8 05/2012 %N 2 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2011.2172004 %0 Conference Paper %B 2011 North American Power Symposium (NAPS 2011) %D 2011 %T Evaluation of mode estimation accuracy for small-signal stability analysis %A Jim Follum %A Ning Zhou %A John W. Pierre %K AA09-002 %K AARD %K Automatic Switchable Network (ASN) %K load modeling %K power system monitoring %X This 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. %B 2011 North American Power Symposium (NAPS 2011) %I IEEE %C Boston, MA, USA %P 1 - 7 %8 08/2011 %@ 978-1-4577-0417-8 %R 10.1109/NAPS.2011.6024893 %0 Conference Paper %B IEEE Power and Energy Society (PES) General Meeting %D 2010 %T Automatic implementation of Prony analysis for electromechanical mode identification from phasor measurements %A Ning Zhou %A Zhenyu Huang %A Francis K. Tuffner %A John W. Pierre %A Shuangshuang Jin %K AA07-001 %K AARD %K Automatic Switchable Network (ASN) %K grid reliability %K phasor measurement units (PMUs) %K power system monitoring %XSmall 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.

%B IEEE Power and Energy Society (PES) General Meeting %I IEEE %C Minneapolis, MN %P 1 - 8 %8 07/2010 %@ 978-1-4244-6549-1 %R 10.1109/PES.2010.5590169 %0 Journal Article %J IEEE Transactions on Power Systems %D 2010 %T Probing Signal Design for Power System Identification %A John W. Pierre %A Ning Zhou %A Francis K. Tuffner %A John F. Hauer %A Dan Trudnowski %A William A. Mittelstadt %K AA07-001 %K AARD %K Automatic Switchable Network (ASN) %K WECC %X This 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. %B IEEE Transactions on Power Systems %V 25 %P 835 - 843 %8 05/2010 %N 2 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2009.2033801 %0 Conference Paper %B 2009 IEEE Power & Energy Society General Meeting %D 2009 %T Electromechanical mode shape estimation based on transfer function identification using PMU measurements %A Ning Zhou %A Zhenyu Huang %A Luke Dosiek %A Dan Trudnowski %A John W. Pierre %K AA07-001 %K AARD %K Automatic Switchable Network (ASN) %K phasor measurement units (PMUs) %K Power system modeling %X Power 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. %B 2009 IEEE Power & Energy Society General Meeting %I IEEE %C Calgary, Canada %P 1 - 7 %8 07/2009 %@ 978-1-4244-4241-6 %R 10.1109/PES.2009.5275924