Accelerated frequency-dependent method of characteristics for the simulation of multiconductor transmission lines in the time domain
Simulation of transients in transmission lines can be time consuming and resource demanding when performed directly in the time domain using small time steps. This paper proposes an efficient implementation of the frequency-dependent method of characteristics. The proposed implementation exploits th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/189923 |
| Acceso en línea: | http://dx.doi.org/10.1016/j.epsr.2018.11.006 http://hdl.handle.net/11449/189923 |
| Access Level: | acceso abierto |
| Palabra clave: | Electromagnetic transient analysis Multiconductor transmission lines Transmission line modeling |
| Sumario: | Simulation of transients in transmission lines can be time consuming and resource demanding when performed directly in the time domain using small time steps. This paper proposes an efficient implementation of the frequency-dependent method of characteristics. The proposed implementation exploits the circuit topology of the system to reduce the number of state equations. State-space matrices are then grouped and sparsity techniques are used to solve the system of the ordinary differential equations faster. We post-process the calculated state variables to reduce the memory usage and the number of memory accesses needed in each iteration and propose a technique to correct the error due to the time discretization of the travel time along the transmission line. To decouple the multiconductor transmission line equations, the real part of the modal transformation matrix at a fix frequency is considered. Presented results demonstrate that the proposed approach is accurate for overhead transmission lines, and significantly reduces the overall computation time and memory consumption of the model. |
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