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 pro-vided 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 on-line to provide real-time mode estimation.