A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
This work addresses challenges in PV system efficiency for renewable energy applications, but it appears incremental as it builds on existing neural network methods with specific enhancements.
The paper tackled the problem of steady-state oscillations and loss of tracking direction in photovoltaic maximum power point tracking under fast-changing environments and partial shading, by proposing a resilient backpropagation neural network-based algorithm with a supervision mechanism, achieving improved performance as verified through multiple case studies.
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.