A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

@article{Cui2019ARP,
  title={A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation},
  author={Yao Cui and Zhehan Yi and Jiajun Duan and Di Shi and Z. Wang},
  journal={2019 IEEE Power \& Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
  year={2019},
  pages={1-5}
}
  • Yao Cui, Zhehan Yi, +2 authors Z. Wang
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill climbing, and incremental conductance (Inc-Cond) etc.) are trial-and-error-based, which may result in steady-state oscillations and loss of… Expand
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