An algorithm for the selection of structure for artificial networks. Case study: solar thermal energy systems

被引：9

作者：

Timma, Lelde ^{[1
]}

Blumberga, Dagnija ^{[1
]}

机构：

[1] Riga Tech Univ, Inst Energy Syst & Environm, LV-1048 Riga, Latvia

来源：

INTERNATIONAL SCIENTIFIC CONFERENCE ENVIRONMENTAL AND CLIMATE TECHNOLOGIES, CONECT 2014 | 2015年 / 72卷

关键词：

solar and pellet combisystem; artificial neural networks; fault detection and isolation; sustainable energy systems;

D O I：

10.1016/j.egypro.2015.06.019

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Despite perceived simplicity of solar thermal collectors, failures occur during operation. Therefore fault detection and isolation tools for these systems should be investigated. One of the critical parts for the development of fault detection and isolation is model selection. Within the paper, a specific algorithm for the selection of fault detection and an isolation model is elaborated and presented. The developed algorithm was applied for a solar and pellet combisystem. Through application of the proposed algorithm, a model based approach with recurrent structure of artificial neural networks is chosen for the development of a fault detection and isolation model. (C) 2015 The Authors. Published by Elsevier Ltd.

引用

页码：135 / 141

页数：7

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