Neural network based system identification of a pmsm under load fluctuation
A neural network based approach is applied to model a PMSM. A multilayer recurrent network provides a near term fundamental current prediction using as an input the fundamental components of the voltage signals and the speed. The PMSM model proposed can be implemented in a condition based maintenanc...
- Autores:
-
Quiroga Méndez, Jabid Eduardo
Cartes, David
Edrington, Chris
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2009
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/26910
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/26910
http://bdigital.unal.edu.co/17958/
- Palabra clave:
- System
Identification
PMSM
Neural Network
Recurrent Networks.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | A neural network based approach is applied to model a PMSM. A multilayer recurrent network provides a near term fundamental current prediction using as an input the fundamental components of the voltage signals and the speed. The PMSM model proposed can be implemented in a condition based maintenance to perform fault detection, integrity assessment and aging process. The model is validated using a 15 hp PMSM experimental setup. The acquisition system is developed using Matlab®/Simulink® with dSpace® as an interface to the hardware, i.e. PMSM drive system. The model shows generalization capabilities and a satisfactory performance in the fundamental current determination on line under no load and load fluctuations. |
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