A hybrid method and its applications to analyse the low frequency oscillations in the interconnected power system

Determination of mode shapes in PMU signals using two‐stage mode decomposition and spectral analysis

“…Physically, an IMF is a zero-mean oscillatory waveform, not necessarily be of sinusoidal nature and may be modulated in both amplitude and frequency [24]. The use of EMD has been widely reported to estimate the oscillatory modes present in PMUs data [6].…”

“…Despite being the benchmark technique, EMD has inherent shortcomings [6]. The DMs of each level are supposed to be the mono-frequency component, but sometimes they are not so and exhibits mode mixing [25][26][27].…”

“…The local modes are observed when a single generator or plant oscillate against the rest of the system, whereas the inter-area modes exist when multiple generators in an area oscillate against generators in another area [4]. An inter-area oscillation is the most severe oscillation which affects the security operation of the interconnected power system and these are frequently observed, especially where the grids are interconnected via weak tie-line [4][5][6]. Generally, the frequency of local mode lies in the range of 0.7-2 Hz [4], while those of inter-area mode lies in the range of 0.1-1 Hz [5].…”

“…In recent times, with phasor measurement units (PMUs) signals being available, advanced signal processing techniques like phasor approach, Prony analysis, Hankel analysis, fast Fourier transform (FFT), and matrix pencil method (MPM) have been proposed for real-time monitoring of oscillations and mode shape analysis [12][13][14][15]. In [6], a hybrid method consisting the combination of 'improved Hilbert-Huang transform (HHT)' and 'relative phase computation algorithm' is proposed to determine the mode shapes.…”

Identification method for power system low‐frequency oscillations based on improved VMD and Teager–Kaiser energy operator

“…EMD is fast, efficient, and easy to be applied, but it is lack of strong theoretical support. Besides, some inherent shortcomings such as end effects and aliasing have negative impact on the decomposition performance [20]. Although the end effects have been effectively reduced by overlapping technique, extension approaches, grey model, and machine learning [21], some techniques to overcome aliasing are still not adaptive.…”