Adaptive tracking of system oscillatory modes using an extended RLS algorithm
•A nonstationary RLS algorithm is combined with a Kalman filter to deal with measured ambient power system data.•By tracking the evolving dynamics of system oscillations, the system instability conditions can be determined.•Extensions and generalizations to current adaptive filtering algorithms to a...
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| Veröffentlicht in: | Electric power systems research Jg. 114; S. 28 - 38 |
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Elsevier B.V
01.09.2014
Elsevier |
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| ISSN: | 0378-7796, 1873-2046 |
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| Abstract | •A nonstationary RLS algorithm is combined with a Kalman filter to deal with measured ambient power system data.•By tracking the evolving dynamics of system oscillations, the system instability conditions can be determined.•Extensions and generalizations to current adaptive filtering algorithms to account for nonstationarity are tested.•The correspondence between the Kalman and RLS variables is examined.•Early simulation studies conducted on time-synchronized data show that this method can be used for real-time applications.
The study of low-frequency electromechanical modes in power systems has experienced much progress in the past few years. In this research, a nonstationary recursive least-squares (RLS) algorithm with variable forgetting factor is combined with a Kalman filter to simultaneously estimate low-frequency electromechanical modes from measured ambient power system data. Extensions and generalizations to current adaptive filtering algorithms to account for nonstationarity are implemented and tested and the correspondence between the Kalman and RLS variables is examined.
Applications of the proposed nonstationary RLS algorithm to track the evolving dynamics of critical power system electromechanical modes in both, simulated and measured data, are presented. Comparison with other RLS and least-mean squares algorithms demonstrate the accuracy of the proposed framework in tracking changes in modal parameters over time. The issues of computational efficiency and memory requirements are discussed in detail. |
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| AbstractList | •A nonstationary RLS algorithm is combined with a Kalman filter to deal with measured ambient power system data.•By tracking the evolving dynamics of system oscillations, the system instability conditions can be determined.•Extensions and generalizations to current adaptive filtering algorithms to account for nonstationarity are tested.•The correspondence between the Kalman and RLS variables is examined.•Early simulation studies conducted on time-synchronized data show that this method can be used for real-time applications.
The study of low-frequency electromechanical modes in power systems has experienced much progress in the past few years. In this research, a nonstationary recursive least-squares (RLS) algorithm with variable forgetting factor is combined with a Kalman filter to simultaneously estimate low-frequency electromechanical modes from measured ambient power system data. Extensions and generalizations to current adaptive filtering algorithms to account for nonstationarity are implemented and tested and the correspondence between the Kalman and RLS variables is examined.
Applications of the proposed nonstationary RLS algorithm to track the evolving dynamics of critical power system electromechanical modes in both, simulated and measured data, are presented. Comparison with other RLS and least-mean squares algorithms demonstrate the accuracy of the proposed framework in tracking changes in modal parameters over time. The issues of computational efficiency and memory requirements are discussed in detail. |
| Author | Messina, A.R. Moreno, I. |
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| Cites_doi | 10.1049/el:20000727 10.1109/59.630467 10.1109/79.295229 10.1109/TPWRS.2008.919415 10.1109/TPWRS.2007.901104 10.1214/aos/1176349739 10.1109/TPWRS.2008.2002173 10.1111/j.1467-9892.1991.tb00084.x 10.1109/TAC.1976.1101260 10.1147/rd.183.0267 |
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| Keywords | Power phasor measurement WAMS Kalman filtering RLS algorithms Performance evaluation Phase measurement Adaptive algorithm Adaptive system Power system control Kalman filter Adaptive filtering Recursive algorithm Low frequency Implementation Power system measurement Algorithm performance Electrical network Least squares method Recursive method Comparative study Electromechanical system Least mean squares methods |
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| SubjectTerms | Applied sciences Capacitors. Resistors. Filters Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Kalman filtering Miscellaneous Operation. Load control. Reliability Power networks and lines Power phasor measurement RLS algorithms Various equipment and components WAMS |
| Title | Adaptive tracking of system oscillatory modes using an extended RLS algorithm |
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