Modal Estimation Techniques Research Articles

Power System Mode Metering in the Presence of Non-Gaussian Noise

This letter presents a robust generalized correlation approach for monitoring of power system oscillatory modes. Due to the non-Gaussian nature of measurement noise, and presence of impulsive outliers, the wide-sense stationarity assumption of power system measurement data is violated even under ambient conditions thus limiting the effectiveness of conventional autocorrelation based approaches in mode metering. The fundamental definition of correlation function for power system measurements is extended to a generalized correlation function in order to incorporate higher order statistics into existing modal estimation technique. Simulations on synthetic and real-life test signals have been conducted to demonstrate the robustness of the proposed method.

Algorithm for the estimation of electromechanical modes via modal decomposition techniques

This paper proposes an algorithm for the application of mode decomposition techniques based on ringdown signals to estimate electromechanical modes in electrical power systems. The estimation of the electromechanical modes is done by defining parameters for the application of modal estimation technique successive times, followed by routines of identification and analysis of the results. Results are obtained by using the techniques LS-ESPRIT, TLS-ESPRIT and WTLS-ESPRIT. The simulations were performed for a distribution network in the presence of a group of three synchronous generators.

Multichannel Techniques for the Estimation of Power System Electromechanical Modes

Estimating power system electromechanical modes has been a topic of great interest since an unstable mode caused a system outage in the western United States in 1996. In recent years several methods have been proposed to estimate the modal frequency and damping from measured data. Most of these methods focus on examining one signal at a time. Some of the methods do, however, incorporate several measured signals and thus have the benefit of taking advantage of information from multiple data channels to increase the performance of the algorithms. This paper compares four published methods of multichannel electromechanical modal estimation techniques: the Modified Extended Yule-Walker (MEYW) method, the Frequency Domain Decomposition (FDD), the Numerical Algorithm For System Identification (N4SID), and a Least Squares (LS) estimate of a multichannel transfer function (MCTF). Through the use of simulated power system data, it is shown that the MCTF method displays superior performance, especially in resolving closely spaced modes. The use of probing signals is shown to increase the performance even further.