Robust RLS Methods for On-line Estimation of Power System Electromechanical Modes
This paper proposes a Robust Recursive Least Square (RRLS) algorithm for on-line identification of power system modes based on measurement data. The measurement data can either be ambient or ringdown. Also, the mode estimation is provided in real-time. The validity of the proposed RRLS algorithm is demon-strated 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 Means 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 degra-dation. 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 on-line to provide real time mode estimation.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 915711
- Report Number(s):
- PNNL-SA-51754; ITPSEG; 830403000; TRN: US200816%%172
- Journal Information:
- IEEE Transactions on Power Systems, 22(3):1240-1249, Vol. 22, Issue 3; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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