1 research outputs found
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
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 tracking direction under fast-changing
ambient environment. In addition, partial shading is also a challenge due to
the difficulty of finding the global maximum power point on a multi-peak
characteristic curve. As an attempt to address the aforementioned issues, a
novel Rprop-NN MPPT algorithm is developed and elaborated in this work.
Multiple case studies are carried out to verify the effectiveness of the
proposed algorithm.Comment: 2019 IEEE ISGT N