scholarly journals A New Method of Small-Signal Calibration Based on Kalman Filter

2019 ◽  
Vol 9 (2) ◽  
pp. 01-06
Author(s):  
XU Yi xiong ◽  
WANG Cheng jun ◽  
XU Ya jun ◽  
YANG Jiang wei
Keyword(s):  
Kalman Filter ◽  
New Method ◽  
Small Signal

scholarly journals A New Method to Extract Gate Bias-Dependent Parasitic Resistances in GaAs pHEMTs

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 266
Author(s):  
Ruirui Dang ◽  
Lijie Yang ◽  
Zhihao Lv ◽  
Chunyi Song ◽  
Zhiwei Xu
Keyword(s):  
Parameter Extraction ◽  
New Method ◽  
Small Signal ◽  
Gate Bias ◽  
Large Signal ◽  
S Parameter ◽  
Proposed Model ◽  
Measurement Results ◽  
Parasitic Parameter ◽  
Signal Models

Accurate large signal GaAs pHEMT models are essential for devices’ performance analysis and microwave circuit design. This, in turn, mandates precise small signal models. However, the accuracy of small signal models strongly depends on reliable parasitic parameter extraction of GaAs pHEMT, which also greatly influences the extraction of intrinsic elements. Specifically, the parasitic source and drain resistances, R s and R d , are gate bias-dependent, due to the two-dimensional charge variations. In this paper, we propose a new method to extract R s and R d directly from S-parameter measurements of the device under test (DUT), which save excessive measurements and complicated parameter extraction. We have validated the proposed method in both simulation and on-wafer measurement, which achieves better accuracy than the existing state-of-the-art in a frequency range of 0.5–40 GHz. Furthermore, we develop a GaAs pHEMT power amplifier (PA) to further validate the developed model. The measurement results of the PA at 9–15 GHz agree with the simulation results using the proposed model.

A New Method of Fault Phase Identification for High Voltage Transmission Lines Based on the Fault Component of Phase Voltage

2013 ◽  
Vol 732-733 ◽  
pp. 1056-1064
Author(s):  
Yang Chen ◽  
Yan Hu ◽  
Neng Ling Tai
Keyword(s):  
Kalman Filter ◽  
High Voltage ◽  
Transmission Lines ◽  
High Sensitivity ◽  
New Method ◽  
Phase Identification ◽  
Phase Voltage ◽  
Two Phase ◽  
Kalman Filter Algorithm ◽  
High Voltage Transmission

Since the existing fault phase identification methods can not identify all fault types quickly and accurately for high voltage transmission lines, this article proposed a new method of fault phase identification based on the fault component of phase voltage difference and the kalman filter algorithm. The method defined the fault components ratio of one phase voltage to the difference of the other two phase voltages as a fault phase identification factor. By analyzing the characteristics of fault phase identification factors in each fault type, the fault phase can be identified. Simulation results show that using the kalman filter algorithm to extract fundamental component is faster and more accurate. Meanwhile, the method can identify fault phases within half a cycle and is scarcely influenced by fault resistances, fault locations and fault initial phase angles. It also has a high sensitivity when the fault is on the side of strong source.

Novel Method to Predict Non-Measured Parameters of Industrial Gas Turbines Using Kalman Filter Technique

2019 ◽  
Author(s):  
Jae Hong Lee ◽  
Tong Seop Kim ◽  
Do Won Kang ◽  
Jeong Lak Sohn ◽  
Jung Ho Lee
Keyword(s):  
Kalman Filter ◽  
Life Cycle ◽  
Gas Turbine ◽  
Case Studies ◽  
Gas Turbines ◽  
New Method ◽  
Stable Operation ◽  
Inlet Temperature ◽  
Analysis Program ◽  
Model Based

Abstract Gas turbines are most widely used for power generation and operate under various conditions and loads. Gas turbine control is important to cope with various situations, and the turbine inlet temperature (TIT) is the most important parameter because it is directly related to the power output and life cycle of the turbine. Thus, precise prediction and control of the TIT are important in terms of the stable operation and life cycle management of gas turbines. This paper proposes a new method to predict non-measured parameters such as the air flow and TIT using Kalman filter techniques. The Kalman filter is widely used for estimating the instantaneous state of a system and can estimate non-measured parameters. The Kalman filter algorithm was implemented in a gas turbine analysis program using MATLAB. The reliability of the new method was verified through various case studies using virtual data and real operating data. The results were compared with those of a model-based gas turbine diagnostics program. The computing time of the Kalman filter and model-based diagnostics program were also compared to confirm the capability of the new method. The results indicate that the new method is more suitable for diagnostics and monitoring applications than the model-based analysis program. Finally, two case studies were performed to confirm the feasibility of the new method using two virtual datasets. The results confirm that the Kalman filter can predict the non-measured parameters precisely.

Kalman/Particle Filter for Integrated Navigation System

2013 ◽  
Vol 756-759 ◽  
pp. 2142-2146 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
The Other ◽  
New Method ◽  
Integrated Navigation ◽  
Computation Complexity ◽  
Integrated Navigation System ◽  
Simulation Results

Particle filter is introduced. Since the particle filter would bring hard computation, a new Kalman/Particle mixed filter used on SINS/GPS integrated navigation system was proposed. The new method divides the system into two sub-models, one is linear, the other one is nonlinear, and then implement Kalman filter and particle filter separately. The simulation results show that their performance is almost equal, but the computation complexity of the Kalman/particle filter is much lower than traditional particle filter.

scholarly journals New Method of Spatial Extrapolation of Meteorological Fields on the Mesoscale Level Using a Kalman Filter Algorithm for a Four-Dimensional Dynamic–Stochastic Model

2007 ◽  
Vol 24 (2) ◽  
pp. 182-193 ◽  
Author(s):  
V. S. Komarov ◽  
A. V. Lavrinenko ◽  
A. V. Kreminskii ◽  
N. Ya Lomakina ◽  
Yu B. Popov ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Stochastic Model ◽  
Statistical Estimation ◽  
Velocity Fields ◽  
New Method ◽  
Atmospheric Parameters ◽  
Time Variations ◽  
Dynamic Stochastic ◽  
Kalman Filter Algorithm

Abstract A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic–stochastic model of space–time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

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