Time‐domain modeling of pulsed finite‐amplitude sound beams

1995 ◽  
Vol 97 (2) ◽  
pp. 906-917 ◽  
Author(s):  
Yang‐Sub Lee ◽  
Mark F. Hamilton
Keyword(s):  
Time Domain ◽  
Finite Amplitude ◽  
Domain Modeling ◽  
Sound Beams ◽  
Time Domain Modeling

Time-domain modeling of nonlinear distortion of pulsed finite amplitude sound beams

Ultrasonics ◽  
2000 ◽  
Vol 38 (1-8) ◽  
pp. 305-311 ◽  
Author(s):  
J.P. Remenieras ◽  
O. Bou Matar ◽  
V. Labat ◽  
F. Patat
Keyword(s):  
Time Domain ◽  
Nonlinear Distortion ◽  
Finite Amplitude ◽  
Domain Modeling ◽  
Sound Beams ◽  
Time Domain Modeling

Time‐domain modeling of finite‐amplitude sound in relaxing fluids

1996 ◽  
Vol 99 (6) ◽  
pp. 3312-3318 ◽  
Author(s):  
Robin O. Cleveland ◽  
Mark F. Hamilton ◽  
David T. Blackstock
Keyword(s):  
Time Domain ◽  
Finite Amplitude ◽  
Domain Modeling ◽  
Time Domain Modeling

Time‐domain modeling of finite‐amplitude sound in relaxing fluids

1995 ◽  
Vol 98 (5) ◽  
pp. 2865-2865 ◽  
Author(s):  
Robin O. Cleveland ◽  
Mark F. Hamilton ◽  
David T. Blackstock
Keyword(s):  
Time Domain ◽  
Finite Amplitude ◽  
Domain Modeling ◽  
Time Domain Modeling

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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