Fault diagnosis and isolation based on Neuro-Fuzzy models applied to a photovoltaic system

被引：2

作者：

Cabeza, Raquelita Torres ^{[1
]}

Potts, Alain Segundo ^{[1
]}

机构：

[1] Fed Univ ABC, Santo Andre, SP, Brazil

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 14期

关键词：

Fault diagnosis; Photovoltaic Systems; Model; Neuro-fuzzy models; Diagnosis and Isolation System; Artificial Intelligence; CLASSIFICATION; STABILITY; NETWORKS;

D O I：

10.1016/j.ifacol.2021.10.380

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an implementation of a fault diagnosis scheme based on the neuro-fuzzy model identification of a photovoltaic system in both of the circuits conversion (DC and AC). The field of Artificial Intelligence has achieved excellent results in the correct isolation of faults and the publications are different, demonstrating their application in industrial plants. For faults diagnosis will be applied Artificial Intelligence techniques based on neural networks, distances and support vector machines. These methods will be implemented and applied to the photovoltaic system. The results obtained demonstrate the good performance of the proposed modelling and diagnosis scheme. Copyright (C) 2021 The Authors.

引用

页码：358 / 363

页数：6

共 24 条

[1] Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system [J].