Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator

Abstract

Critical to real-time oscillations monitoring is early detection when otherwise dormant natural modes become a serious threat to grid stability. The next urgent issue is to determine the frequency and damping of the problematic modes when the signal is embedded in noise and the system contains closely spaced natural modes. The present paper addresses the detection issue using the Teager-Kaiser energy operator (TKEO) which has shown to be a fast predictor of the instability onset time when applied to the output signals of an orthogonal filter bank. In the system stability context, linear filter decomposition (LFD) is preferred rather than empirical mode decomposition (EMD), well known for its tendency to generate artificial modes with no physical meaning. A narrowband LFD with a less than 0. 2-Hz bandwidth is achieved in the range 0.05 to 3 Hz through a cosine-modulated filter bank design. The effectiveness of the scheme in accurately detecting and tracking the frequency and damping of oscillatory modes is demonstrated using Monte Carlo simulations of three closely spaced modes and a detailed analysis of an actual event recorded by Hydro-Québec's WAMS in 2006.Index Terms-Filter bank, interarea oscillation, power system identification, power system monitoring, power system oscillations, power system stability, Teager-Kaiser energy (TKE), wide-area measurement systems (WAMS).

On phasor estimation for voltage sags detection in a smart grid context

“…Advanced signal processing techniques are therefore required to deal with the complexity and uncertainty associated with a smart grid. In [9][10][11][12][13], a Teager-Kaiser energy operator has been proposed for power system oscillations detection and analysis. However, this operator is highly affected by noises.…”

“…These methods are based on electrical quantity signatures analysis (current, voltage, power, etc.) [9][10][11][12]; and usually involves the use of reference frame transformations such as Park's vector [13] or space vector [8][9][10][11][12][13][14]; other methods are based on advanced signal processing tools [15][16][17] where the EEMD is investigated; but the wellknown method is based on the symmetrical components transformation [18][19]. This work attempts to highlight the use of signal processing techniques for phasor estimation in the context of smart grids.…”

Performance analysis of an EEMD-based Hilbert Huang transform as a bearing failure detector in wind turbines

“…The most popular include the Hilbert transform (HT) [16] and the Teager Energy Operator [17]. Furthermore for three-phase system, it has been recently shown that the Concordia transform can be used to perform demodulation [18].…”

EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component

“…For modulated signals, the most popular techniques are TKEO [18], HT, and it has been recently shown that the CT can be used for demodulation [19][20]. Those techniques were investigated in many research works for failure detection tasks.…”