Semantic Scholar Open Access 2018 14 sitasi

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

Yaoqin Cui Zhehan Yi Jiajun Duan Di Shi Zhiwei Wang

Lihat Sumber DOI

Abstrak

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 proposed algorithm.

Topik & Kata Kunci

Computer Science Engineering Mathematics

Penulis (5)

Yaoqin Cui

Zhehan Yi

Jiajun Duan

Di Shi

Zhiwei Wang

Format Sitasi

APA MLA BibTeX

Cui, Y., Yi, Z., Duan, J., Shi, D., Wang, Z. (2018). A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation. https://doi.org/10.1109/ISGT.2019.8791596

Akses Cepat

Lihat di Sumber doi.org/10.1109/ISGT.2019.8791596

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 14×
Sumber Database: Semantic Scholar
DOI: 10.1109/ISGT.2019.8791596
Akses: Open Access ✓