An Intelligent MPPT controller based on direct neural control for partially shaded PV system

被引：80

作者：

Kofinas, P. ^{[1
,2
]}

Dounis, Anastasios I. ^{[2
]}

Papadakis, G. ^{[3
]}

Assimakopoulos, M. N. ^{[4
]}

机构：

[1] Univ Piraeus, Dept Digital Syst, Piraeus, Greece

[2] Technol Educ Inst Piraeus, Dept Automat, Piraeus, Greece

[3] Agr Univ Athens, Dept Nat Resources & Agr Engn, GR-11855 Athens, Greece

[4] Univ Athens, Dept Phys, Grp Bldg Environm Studies, Athens, Greece

来源：

ENERGY AND BUILDINGS | 2015年 / 90卷

关键词：

Intelligent MPPT controller; Direct neural control; Maximum power point tracking; Photovoltaic system; On-line learning; Gradient descent algorithm; Big Bang-Big Crunch optimization; POWER POINT TRACKING;

D O I：

10.1016/j.enbuild.2014.12.055

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

The development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power operation in a photovoltaic system (PV). In this study, a direct neural control (DNC) scheme is developed. The intelligent MPPT controller consists of a hybrid learning mechanism; an on-line learning rule based on gradient decent method and an off-line learning rule based on BigBang-Big Crunch (BB-BC) algorithm. The effectiveness of the proposed system is tested under partial shading conditions by applying the cascaded converter topology. The feasibility of the DNC is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：51 / 64

页数：14

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