A High Efficient Time-domain Modeling Method for Partial Discharge Propagation in XLPE Cables with Large Length

2021 ◽  
Author(s):  
Saike Yang ◽  
Li Wang ◽  
Xianyu Yue ◽  
Hongjie Li
Keyword(s):  
Time Domain ◽  
Partial Discharge ◽  
Modeling Method ◽  
Domain Modeling ◽  
High Efficient ◽  
Time Domain Modeling ◽  
Large Length ◽  
Discharge Propagation

Time-Domain Modeling and Simulation of Partial Discharge on Medium-Voltage Cables by Vector Fitting Method

2014 ◽  
Vol 50 (2) ◽  
pp. 993-996 ◽  
Author(s):  
Tang Ming ◽  
Sun Jianyang ◽  
Guo Jianzhao ◽  
Li Hongjie ◽  
Zhang Wei ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Time Domain ◽  
Partial Discharge ◽  
Domain Modeling ◽  
Medium Voltage ◽  
Vector Fitting ◽  
Fitting Method ◽  
Time Domain Modeling

Modeling and Transient Response of Cable Network

2012 ◽  
Vol 433-440 ◽  
pp. 2868-2873
Author(s):  
Xing Zhou ◽  
Zhen Yu Xiang ◽  
Er Wei Cheng ◽  
Li Si Fan
Keyword(s):  
Transmission Line ◽  
Time Domain ◽  
Modeling Method ◽  
Domain Model ◽  
Domain Modeling ◽  
Cable Network ◽  
Cable Networks ◽  
Time Domain Modeling ◽  
The Time Domain ◽  
Line Network

This paper introduces modeling method for calculating nodes responses of transmission-line network directly in the time domain. Arising from classical telegrapher equations, the time-domain model of transmission line is gained. The transmission-line model, together with transmission-line nodes model, form the network model. Using the time-domain modeling method, transient responses for two given networks are gained. The injection experiments to cable networks are done to validate the calculating results. The consistency of calculating results with measure results indicates the model is feasible.

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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