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During 2005 and 2006, the Western Electricity Coordinating Council (WECC) performed three major tests of western system dynamics. These tests used a Wide-Area Measurement System (WAMS) based primarily on phasor measurement units (PMUs) to determine response to events including the insertion of the 1400-MW Chief Joseph braking resistor, probing signals, and ambient events. Test security was reinforced through real-time analysis of wide-area effects, and high-quality data provided dynamic profiles for interarea modes across the entire western interconnection. The tests established that low-level optimized pseudo-random [+ or -]4-20-MW probing with the Pacific DC Intertie (PDCI) roughly doubles the apparent noise that is natural to the power system, providing sharp dynamic information with negligible interference to system operations. Such probing is an effective alternative to use of the 1400-MW Chief Joseph dynamic brake, and it is under consideration as a standard means for assessing dynamic security. Index Terms--Performance monitoring, phasor measurements, system testing, Wide-Area Measurement System (WAMS).

This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudorandom probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data. Index Terms--Autoregressive moving average processes, least squares methods, power system identification, power system measurements, power system monitoring, power system parameter estimation, power system stability, recursive estimation, robustness.

A theoretical basis and signal-processing approach for estimating a power system's electromechanical mode-shape properties using time-synchronized phasor measurements are presented. The relationship between modal eigenvectors and measurable power system quantities are derived. Spectral correlation analysis is used to implement the approach with demonstrative examples. This includes simulation examples as well as measured data from the western North American power system. Index Terms--Electromechanical dynamics, mode shape, smallsignal stability, synchronized phasor measurements.

The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three mode-estimation block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with Spectral analysis (YWS); and sub-space system identification (N4SID). The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance. Index Terms--Electromechanical dynamics, mode estimation, phasor measurement units (PMUs), power system stability, situational awareness, software certification.

This paper proposes a robust recursive least square (RRLS) algorithm for online identification of power system modes based on measurement data. The measurement data can be either ambient or ringdown. Also, the mode estimation is provided in real-time. The validity of the proposed RRLS algorithm is demonstrated with both simulation data from a 17-machine model and field measurement data from a wide area measurement system (WAMS). Comparison with the conventional recursive least square (RLS) and least mean square (LMS) algorithms shows that the proposed RRLS algorithm can identify the modes from the combined ring-down and ambient signals with outliers and missing data in real-time without noticeable performance degradation. An adaptive detrend algorithm is also proposed to remove the signal trend based on the RRLS algorithm. It is shown that the algorithm can keep up with the measurement data flow and work online to provide real-time mode estimation. Index Terms--Autoregressive, electromechanical mode, identification, power system parameter estimation, power system small signal stability, robust recursive least square (RRLS).

Increasing levels of wind-turbine generation in modern power systems is initiating a need for accurate wind-generation transient stability models. Because many wind generators are often grouped together in wind parks, equivalence modeling of several wind generators is especially critical. In this paper, a reduced-order dynamic fixed-speed wind-generator model appropriate for transient stability simulation is presented. The model is derived using a model reduction technique of a high-order finite-element model. Then, an equivalencing approach is presented that demonstrates how several wind generators in a wind park can be combined into a single reduced-order model. Simulation cases are presented to demonstrate several unique properties of a power system containing wind generators. The results in this paper focus on horizontal-axis turbines using an induction machine directly connected to the grid as the generator. Index Terms--Transient stability simulation, wind-generator modeling, wind-park modeling, wind-turbine modeling.

Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model-based approaches and uses measurement-based techniques. This paper discusses the development of an autoregressive moving average (ARMA) block-processing technique to estimate these !ow-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary !ow-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-min blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance. Index Terms--Estimation, interconnected power systems, power system control, power system modeling, power system monitoring, power system stability, power system state estimation, power system transients, signal analysis, signal sampling.

Recently, a 500-kV thyristor controlled series capacitor (TCSC) was installed in the Bonneville Power Administration system in the northwestern United States. Extensive field testing has included modulation experiments to determine the effectof the TCSC on low-frequency oscillations.This paper discusses modulation procedures, analysis methods, and results for estimating the damping effectiveness of the TCSC. Modulation methods include driving the TCSC with step and random noise, and analysis techniques include time (Prony analysis) and frequency-domain identification. Results indicate that: 1) the TCSC can have significant impact on system dynamics; and 2) under a very small feedback gain, the TCSC provides measurable added damping.