Frequency and time domain modeling of high speed amplifier

2015 ◽  
Author(s):  
Katarzyna Opalska
Keyword(s):  
Time Domain ◽  
High Speed ◽  
Domain Modeling ◽  
Time Domain Modeling

A Steering Control System to Minimize Propulsion Losses of High-Speed Containerships—Part I: System Dynamics

1982 ◽  
Vol 104 (1) ◽  
pp. 1-8 ◽  
Author(s):  
R. E. Reid ◽  
J. W. Moore
Keyword(s):  
Control System ◽  
Modeling And Simulation ◽  
Time Domain ◽  
High Speed ◽  
Steering System ◽  
Performance Criteria ◽  
Domain Modeling ◽  
Added Resistance ◽  
Steering Control ◽  
Time Domain Modeling

The problem of steering control of high-speed containerships to minimize propulsion losses is addressed. The approach involves time domain modeling and simulation. A dynamic model of a containership and steering system in a seaway is constructed. Performance criteria for added resistance associated with yawing and steering are discussed. Losses resulting from yawing of the uncontrolled ship in heavy weather are shown by simulation to be significant. The results presented form a basis for design of a controller to minimize steering related losses.

scholarly journals Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2118
Author(s):  
Elias Kaufhold ◽  
Simon Grandl ◽  
Jan Meyer ◽  
Peter Schegner
Keyword(s):  
Time Domain ◽  
Single Phase ◽  
Low Voltage ◽  
Black Box ◽  
Steady States ◽  
Domain Modeling ◽  
Dynamic Simulations ◽  
Time Domain Modeling ◽  
Artificial Neural ◽  
Grid Side

This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.

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