Automated Measurement of the Bias Dependence of Low Frequency Small-signal Parameter Dispersions in GaAs MESFETs

ESSDERC ’89 ◽  
1989 ◽  
pp. 263-266
Author(s):  
M. T. De’Freitas ◽  
J. G. Swanson
Keyword(s):  
Low Frequency ◽  
Small Signal ◽  
Automated Measurement ◽  
Signal Parameter

Thermal Impedance Modeling of Si–Ge HBTs From Low-Frequency Small-Signal Measurements

2011 ◽  
Vol 32 (2) ◽  
pp. 119-121 ◽  
Author(s):  
Amit Kumar Sahoo ◽  
Sébastien Fregonese ◽  
Thomas Zimmer ◽  
Nathalie Malbert
Keyword(s):  
Low Frequency ◽  
Small Signal ◽  
Thermal Impedance ◽  
Impedance Modeling

scholarly journals DC Characterization and Fast Small-Signal Parameter Extraction of Organic Thin Film Transistors With Different Geometries

2020 ◽  
Vol 41 (10) ◽  
pp. 1512-1515
Author(s):  
August Arnal ◽  
Albert Crespo-Yepes ◽  
Eloi Ramon ◽  
Lluis Teres ◽  
Rosana Rodriguez ◽  
...  
Keyword(s):  
Thin Film ◽  
Thin Film Transistors ◽  
Parameter Extraction ◽  
Organic Thin Film Transistors ◽  
Small Signal ◽  
Organic Thin Film ◽  
Signal Parameter ◽  
Dc Characterization

A reliable and efficient small‐signal parameter extraction method for GaN HEMTs

2018 ◽  
Vol 33 (3) ◽  
Author(s):  
Yongbo Chen ◽  
Yuehang Xu ◽  
Yong Luo ◽  
Changsi Wang ◽  
Zhang Wen ◽  
...  
Keyword(s):  
Extraction Method ◽  
Parameter Extraction ◽  
Small Signal ◽  
Signal Parameter ◽  
Gan Hemts

scholarly journals Probabilistic small-signal stability analysis of power system with solar farm integration

2019 ◽  
pp. 1276-1289 ◽  
Author(s):  
SAMUNDRA GURUNG ◽  
SUMATE NAETILADDANON ◽  
ANAWACH SANGSWANG
Keyword(s):  
Power System ◽  
Density Function ◽  
Forecast Error ◽  
Low Frequency ◽  
Test System ◽  
System Stability ◽  
Small Signal ◽  
Small Signal Stability ◽  
Oscillatory Dynamics ◽  
Signal Stability

Currently, large-scale solar farms are being rapidly integrated in electrical grids all over the world. However, the photovoltaic (PV) output power is highly intermittent in nature and can also be correlated with other solar farms located at different places. Moreover, the increasing PV penetration also results in large solar forecast error and its impact on power system stability should be estimated. The effects of these quantities on small-signal stability are difficult to quantify using deterministic techniques but can be conveniently estimated using probabilistic methods. For this purpose, the authors have developed a method of probabilistic analysis based on combined cumulant and Gram– Charlier expansion technique. The output from the proposed method provides the probability density function and cumulative density function of the real part of the critical eigenvalue, from which information concerning the stability of low-frequency oscillatory dynamics can be inferred. The proposed method gives accurate results in less computation time compared to conventional techniques. The test system is a large modified IEEE 16-machine, 68-bus system, which is a benchmark system to study low-frequency oscillatory dynamics in power systems. The results show that the PV power fluctuation has the potential to cause oscillatory instability. Furthermore, the system is more prone to small-signal instability when the PV farms are correlated as well as when large PV forecast error exists.

Parameter Estimation of Low-Frequency Signal with Negative Frequency Contribution

2013 ◽  
Vol 380-384 ◽  
pp. 3457-3460 ◽  
Author(s):  
Si Wei Tan ◽  
Zhi Liang Ren ◽  
Jiong Sun
Keyword(s):  
Parameter Estimation ◽  
Phase Difference ◽  
Spectrum Analysis ◽  
Low Frequency ◽  
High Accuracy ◽  
Signal Parameter ◽  
Frequency Signal ◽  
Spectrum Method ◽  
Analysis Theory ◽  
Simulation Results

According to the problem that there is a decline in accuracy of low-frequency signal parameter estimation by using the algorithm of all-phase FFT, an improved phase difference correcting spectrum method based on all-phase FFT is proposed. The contribution of negative frequency to FFT calculation was considered while using phase difference correcting spectrum method. The all-phase FFT spectrum analysis theory was presented as well as a traditional phase difference correcting method based on it. The equations of parameter estimation such as frequency, amplitude and phase for low-frequency signals were derived with the negative frequency contribution to spectrum analysis. The simulation results show that the method proposed in this paper can be used to estimate the parameters of low-frequency signals in a high accuracy, and also achieves an improvement in anti-noise ability.

EXPERIMENTAL RESULTS FOR THE SMALL-SIGNAL STUDY OF THE PWM BOOST DC–DC CONVERTER WITH AN INTEGRAL-LEAD CONTROLLER

1995 ◽  
Vol 05 (04) ◽  
pp. 747-755 ◽  
Author(s):  
MARIAN K. KAZIMIERCZUK ◽  
ROBERT C. CRAVENS, II
Keyword(s):  
Closed Loop ◽  
Transfer Functions ◽  
Low Frequency ◽  
Input Voltage ◽  
Open Loop ◽  
Small Signal ◽  
Loop Control ◽  
Pulse Width Modulated ◽  
Bode Plots ◽  
Step Responses

An experimental verification of previously derived small-signal low-frequency open- and closed-loop characteristics and step responses of a voltage-mode-controlled pulse-width-modulated (PWM) boost DC–DC converter is presented. The Bode plots of the voltage transfer function of the control circuit, the converter and the PWM modulator, the open-loop control-to-output and input-to-output transfer functions, the loop gain, and the closed-loop control-to-output and input-to-output transfer functions are measured. The step responses to the changes in the input voltage, the duty cycle, and the reference voltage are measured. The theoretical results were in good agreement with the measured results. The small-signal model of the converter is experimentally verified.

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