Monitoring Tool for Stand-Alone Photovoltaic System Using Artificial Neural Network

被引：1

作者：

Sabri, Nassim ^{[1
]}

Tlemcani, Abdelhalim ^{[1
]}

Chouder, Aissa ^{[2
]}

机构：

[1] Univ Medea, Lab Elect Engn & Automat LREA, Medea 26000, Algeria

[2] Univ Mohamed Boudiaf Msila, LGE, BP 166 Ichbilia, Msila 28000, Algeria

来源：

RENEWABLE ENERGY FOR SMART AND SUSTAINABLE CITIES: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGETIC SYSTEMS | 2019年 / 62卷

关键词：

SAPV system; ANN; Accuracy; GUI matlab; FAULT-DIAGNOSIS;

D O I：

10.1007/978-3-030-04789-4_12

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Fault classification using supervised machine learning Artificial Neural Network (ANN) is proposed to diagnose some defaults in Stand-alone photovoltaic (SAPV) system, where the data learning includes the voltage and current of PV panels, Battery and load are collected for different operation mode of the system (healthy and faulty). The proposed approach is applied to small SAPV system installed at LREA in the University of Medea, Algeria in which the results of classification show a high accuracy up to 97%. In addition, a Graphical User Interface (GUI) Matlab is created in computer to display the results of classification by the developed ANN.

引用

页码：114 / 121

页数：8

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