Robust RLS Methods for Online Estimation of Power System Electromechanical Modes

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 d...

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Veröffentlicht in:IEEE transactions on power systems 2007-08, Vol.22 (3), p.1240-1249
Hauptverfasser: Ning Zhou, Pierre, J.W., Trudnowski, D.J., Guttromson, R.T.
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container_issue 3
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container_title IEEE transactions on power systems
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creator Ning Zhou
Pierre, J.W.
Trudnowski, D.J.
Guttromson, R.T.
description 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 ringdown 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.
doi_str_mv 10.1109/TPWRS.2007.901104
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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 ringdown 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.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2007.901104</doi><tpages>10</tpages></addata></record>
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subjects Adaptive algorithms
Algorithms
Area measurement
Autoregressive
electromechanical mode
identification
Least mean squares
Least squares method
Least squares methods
On-line systems
Online
Power measurement
Power system measurements
Power system modeling
power system parameter estimation
Power system simulation
power system small signal stability
Power systems
Real time
Recursive
Resonance light scattering
robust recursive least square (RRLS)
Robustness
Wide area measurements
title Robust RLS Methods for Online Estimation of Power System Electromechanical Modes
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