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: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2009 |
| País: | Colombia |
| Recursos: | Universidad Nacional de Colombia |
| Repositório: | Repositorio UN |
| Idioma: | espanhol |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/26910 |
| Acesso em linha: | https://repositorio.unal.edu.co/handle/unal/26910 http://bdigital.unal.edu.co/17958/ |
| Access Level: | Acceso aberto |
| Palavra-chave: | System Identification PMSM Neural Network Recurrent Networks. |
| Resumo: | 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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