A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications

被引：0

作者：

Boutebba, Okba ^{[1
]}

Laudani, Antonino ^{[2
]}

Lozito, Gabriele Maria ^{[2
]}

Corti, Fabio ^{[3
]}

Reatti, Alberto ^{[3
]}

Semcheddine, Samia ^{[1
]}

机构：

[1] Power Elect & Ind Control Lab LEPCI, Dept Elect, Setif, Algeria

[2] Univ Roma Tre, Dipartimento Ingn, Rome, Italy

[3] Univ Firenze, Dipartimento Ingn Informaz DINFO, Florence, Italy

来源：

2020 20TH IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2020 4TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE) | 2020年

关键词：

Maximum Power Point Tracking; Photovoltaics; DC-DC Converters; Neural Networks; Adaptive backstepping; Single-Diode Model;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Maximum power point tracking is a key asset to ensure an efficient energy conversion when a photovoltaic power source is involved. In this work, a novel approach combining a Neural-Network based tracking technique with an highly efficient algorithm for non-inverting buck-boost DC-DC converter (NIBB) control is proposed. The approach is validated through comparison against the well-known P&O algorithm, resulting superior both in terms of identifying the correct operating point for the PV device, and in terms of dynamic stability of the converter.

引用

页数：6

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