Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application
| Autor: | K.Kalyan Raj, Murugaperumal Krishnamoorthy, Suresh Srinivasan, M. Padma Lalitha, Ramji Tiwari |
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| Rok vydání: | 2021 |
| Předmět: |
Radial basis function network
Artificial neural network Renewable Energy Sustainability and the Environment Computer science Control (management) Energy Engineering and Power Technology Proton exchange membrane fuel cell High voltage 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Maximum power point tracking 0104 chemical sciences Power (physics) Fuel Technology Control theory 0210 nano-technology MATLAB computer computer.programming_language |
| Zdroj: | International Journal of Hydrogen Energy. 46:6709-6719 |
| ISSN: | 0360-3199 |
| Popis: | An artificial neural network (ANN) based maximum power point tracking (MPPT) technique for proton exchange membrane fuel cell (PEMFC) is analysed and proposed in this paper. The proposed ANN technique employs Radial basis function network (RBFN) based MPPT strategy to extract the maximum available power from fuel cell in different operating condition. In order to achieve high voltage rating, a novel high step up DC/DC converter is incorporated in the proposed configuration. To validate the performance of the proposed configuration, the result is compared with different DC/DC converter and MPPT control strategy. The proposed system is simulated in MATLAB/Simulink platform to analyse the performance of the system. |
| Databáze: | OpenAIRE |
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