Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system
This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy t...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI
2018
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| Online Access: | https://eprints.nottingham.ac.uk/50298/ |
| _version_ | 1848798213240258560 |
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| author | Tiwari, Ramji Krishnamurthy, Kumar Neelakandan, Ramesh Padmanaban, Sanjeevikumar Wheeler, Patrick |
| author_facet | Tiwari, Ramji Krishnamurthy, Kumar Neelakandan, Ramesh Padmanaban, Sanjeevikumar Wheeler, Patrick |
| author_sort | Tiwari, Ramji |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink. |
| first_indexed | 2025-11-14T20:16:12Z |
| format | Article |
| id | nottingham-50298 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:16:12Z |
| publishDate | 2018 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-502982020-05-04T19:31:45Z https://eprints.nottingham.ac.uk/50298/ Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system Tiwari, Ramji Krishnamurthy, Kumar Neelakandan, Ramesh Padmanaban, Sanjeevikumar Wheeler, Patrick This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink. MDPI 2018-02-09 Article PeerReviewed Tiwari, Ramji, Krishnamurthy, Kumar, Neelakandan, Ramesh, Padmanaban, Sanjeevikumar and Wheeler, Patrick (2018) Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system. Electronics, 7 (2). 20/1-20/17. ISSN 2079-9292 http://www.mdpi.com/2079-9292/7/2/20 doi:10.3390/electronics7020020 doi:10.3390/electronics7020020 |
| spellingShingle | Tiwari, Ramji Krishnamurthy, Kumar Neelakandan, Ramesh Padmanaban, Sanjeevikumar Wheeler, Patrick Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title | Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title_full | Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title_fullStr | Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title_full_unstemmed | Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title_short | Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system |
| title_sort | neural network based maximum power point tracking control with quadratic boost converter for pmsg—wind energy conversion system |
| url | https://eprints.nottingham.ac.uk/50298/ https://eprints.nottingham.ac.uk/50298/ https://eprints.nottingham.ac.uk/50298/ |