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DC-DC Converter Duty Cycle ANN Estimation for DG Applications
Journal of Electrical Systems (2013)
  • Adel El-Shahat, Suez University
This paper proposes Artificial Neural Network (ANN) model for the required DC-DC Converter Duty Cycle feeding Maximum Power to resistive load to be used for distributed generation (DG) applications. It proposes a PV module when coupled to a load through DC-DC Converter to supply this resistive load with the maximum power from the PV module. Some of DC-DC converters topologies are discussed in brief with concentration on Cúk and SEPIC Converters operations. The mechanism of load matching is described to give the required converter duty cycle at maximum power point (MPP). Relations in 3D figures are introduced for the most probable situations for irradiance and temperature with the corresponding PV voltage and current. Also, 3D figures for the desired duty cycle, output voltage and current of DC-DC converter to gain the maximum power to the resistive load at various irradiance and temperature values. Moreover; Artificial Neural Network (ANN) is used to implement a neural model with its algebraic function to take the probable system situations and outs the proposed converter duty cycle to give maximum power for the load. All the neural model are done with their hidden and output layers’ suitable neurons numbers and suitable performance goals depending on the 3D simulation figures shown in the paper. 
  • Distributed generation,
  • Maximum power,
  • DC-DC converter,
  • PV module,
  • ANN,
Publication Date
March, 2013
Citation Information
Adel El-Shahat. "DC-DC Converter Duty Cycle ANN Estimation for DG Applications" Journal of Electrical Systems Vol. 9 Iss. 1 (2013) p. 13 - 38 ISSN: 1112-5209
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