Online Tuned Neural Networks for Fuzzy Supervisory Control of PV-Battery Systems

被引：0

作者：

Ciabattoni, Lucio ^{[1
]}

Ippoliti, Gianluca ^{[1
]}

Longhi, Sauro ^{[1
]}

Cavalletti, Matteo ^{[2
]}

机构：

[1] Univ Politecn Marche, Dipartimento Ingn Informaz, I-60131 Ancona, Italy

[2] Comp Energy Res, I-60035 Jesi, Italy

来源：

2013 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES (ISGT) | 2013年

关键词：

NORMALIZATION; MANAGEMENT;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The paper deals with a neural network based fuzzy supervisor control to manage power flows in a Photo-Voltaic (PV) - Battery system. An on-line self-learning prediction algorithm is used to forecast, over a determined time horizon, the power mismatch between PV production and electrical consumptions. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power flows are scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.

引用

页数：6

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