Application of Synchrosqueezed Wavelet Transforms for Extraction of the Oscillatory Parameters of Subsynchronous Oscillation in Power Systems

Abstract

Abstract:The most classical subsynchronous oscillation (SSO) mode extraction methods have some shortcomings, such as lower mode identification and poor anti-noise properties. Thus, this paper proposes a new time-frequency analysis method, namely, synchrosqueezed wavelet transforms (SWT). SWT combines the advantages of empirical mode decomposition (EMD) and wavelet, which has the adaptability of EMD, and improve the ability of anti-mode mixing on EMD and wavelet. Thus, better anti-noise property and higher mode identification can be achieved. Firstly, the SSO signal is transformed by SWT and its time-frequency spectrum is obtained. Secondly, the attenuation characteristic of each intrinsic mode type (IMT) component in its time-frequency spectrum is analyzed by an automatic identification algorithm, and determine which IMT component needs reconstruction. After that, the selected IMT components with divergent characteristic are reconstructed. Thirdly, high-accuracy detection for mode parameter identification is achieved by the Hilbert transform (HT). Simulation and application examples prove the effectiveness of the proposed method.

Artificial neural network and synchrosqueezing wavelet transform based control of power quality events in distributed system integrated with distributed generation sources

“…Advantages Disadvantages DFT 4 Applied to periodic signal to find out its frequency content only Does not provide time information of signal STFT 5 Used to find the phase components and sinusoidal frequency of local sections of signal with change in time Fixed resolution at all the time S-transform 6,7 Gives information about relative amplitude of various sinusoids in signal at any time Fixed width of window and poor time resolutions at high frequency events WT 8,9 Provides multi resonance analysis and fast technique Spreading effect due to mother wavelet WPT 10,11 More flexibility than DFT Provides most suitable sub bands for signal representation High computational burden HHT 12 Good adaptive ability It requires over sampled data for exact estimation of instantaneous frequency SWT 13,14 Adaptive in nature Better resolution Reduces spreading effect of mother wavelet…”

Stability analysis of sub‐synchronous oscillation in power system connected with virtual synchronous DFIGs

“…This paper select the IEEE two-area four-machine system and integrate it with virtual synchronous DFIG for further verification of the proposed method. As shown in Figure 18, Area 1 contains two 900 MW synchronous generators and a wind farm FIGURE 14 Variation of energy dissipation rate of virtual synchronous DFIG with time and integration parameter K s qi1 FIGURE 15 Variation of the energy dissipation rate with virtual inertia time constant T j consisting of some 1.5 MW DFIGs; Area 2 contains two 900 MW synchronous generators. The dynamic modelling of the synchronous generator can be found in [21], and the 1.5 MW DFIG applies the parameters in Table 1.…”

“…Wavelet transform is a signal time-frequency analysis method suitable for nonlinear system, which can monitor the variation of frequency during system oscillation with fast calculation speed. However, wavelet transform can only obtain the characteristics of signals in certain frequency band, but it cannot analyse the characteristic of signal at a specific frequency [13,14]. Fourier transform is commonly used to analyse the oscillation frequency.…”

Emotional Voice Conversion Using a Hybrid Framework With Speaker-Adaptive DNN and Particle-Swarm-Optimized Neural Network

“…If (ξ ) is concentrated around ξ = ω 0 , then W x (a, b) will be around a = ω 0 ω . For any a and b for which W x (a, b) = 0, the instantaneous frequency can be estimated as in (3) [68]:…”