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.
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页数：6
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