scholarly journals Development of low-frequency electrical therapy device with chaotic vibration and its performance analysis

2012 ◽  
Vol 3 (4) ◽  
pp. 621-628
Author(s):  
Mamoru Suefuji ◽  
Takuya Shindou ◽  
Kantaro Fujiwara ◽  
Kenya Jin'no ◽  
Tohru Ikeguchi
Author(s):  
Baoling Guo ◽  
Seddik Bacha ◽  
Mazen Alamir ◽  
Julien Pouget

AbstractAn extended state observer (ESO)-based loop filter is designed for the phase-locked loop (PLL) involved in a disturbed grid-connected converter (GcC). This ESO-based design enhances the performances and robustness of the PLL, and, therefore, improves control performances of the disturbed GcCs. Besides, the ESO-based LF can be applied to PLLs with extra filters for abnormal grid conditions. The unbalanced grid is particularly taken into account for the performance analysis. A tuning approach based on the well-designed PI controller is discussed, which results in a fair comparison with conventional PI-type PLLs. The frequency domain properties are quantitatively analysed with respect to the control stability and the noises rejection. The frequency domain analysis and simulation results suggest that the performances of the generated ESO-based controllers are comparable to those of the PI control at low frequency, while have better ability to attenuate high-frequency measurement noises. The phase margin decreases slightly, but remains acceptable. Finally, experimental tests are conducted with a hybrid power hardware-in-the-loop benchmark, in which balanced/unbalanced cases are both explored. The obtained results prove the effectiveness of ESO-based PLLs when applied to the disturbed GcC.


2013 ◽  
Vol 722 ◽  
pp. 153-156
Author(s):  
Hao Zhang ◽  
Jing Feng ◽  
Xin Ran Song

In this paper, some commonly used algorithms of PWM inverter were described, and the analysis of the influence of SVPWM algorithm on low-frequency characteristics of inverter was given, then a new PWM algorithm to improve low frequency characteristics of inverter was introduced and simulation and performance analysis of the two algorithms were given.


2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Xiulei Wei ◽  
Ruilin Lin ◽  
Shuyong Liu ◽  
Chunhui Zhang

Chaotic data analysis is important in many areas of science and engineering. However, the chaotic signals are inevitably contaminated by complicated noise in the collection process which greatly interferes with the analysis of chaos identification. The chaotic vibration is extremely nonlinear and has a broad range of frequencies; linear filtering methods are not effective for chaotic signal noise reduction. Then an improved ensemble empirical mode decomposition (EEMD) based on singular value decomposition (SVD) and Savitzky-Golay (SG) filtering method was proposed. Firstly, the noise energy of first level intrinsic mode function (IMF) was estimated by “3σ” criterion, and then SVD was used to extract the signal details from first IMF, and the singular value was selected to reconstruct the IMF according to noise energy of the first IMF. Secondly, the remaining IMFs are divided into high frequency and low frequency components based on consecutive mean square error (CMSE), and the useful signals of high frequency components and low frequency components are extracted based on SVD and SG filtering method, respectively. The superiority of the proposed method is demonstrated with simulated signal, two-degree-of-freedom chaotic vibration signals, and the experimental signals based on double potential well theory.


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