Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application
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 p...
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| Published in: | International journal of hydrogen energy Vol. 46; no. 9; pp. 6709 - 6719 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
03-02-2021
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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.
•Proposed a converter for fuel cell with high voltage gain and low switching loss.•Novel MPPT is proposed to extract maximum power at variable operating conditions.•Incorporation of Novel RBFN strategy and Quadratic Boost converter for fuel cell.•Validated the proposed system performance with notable classical methodologies. |
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| ISSN: | 0360-3199 1879-3487 |
| DOI: | 10.1016/j.ijhydene.2020.11.121 |