Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection

Mahmoud Dhimish, Violeta Holmes, Behrooz Mehrdadi, Mark Dales

Research output: Contribution to journalArticle

  • 2 Citations

Abstract

This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface.

The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.
LanguageEnglish
Pages257-274
Number of pages8
JournalRenewable Energy
Volume117
Early online date24 Oct 2017
DOIs
StatePublished - Mar 2018

Fingerprint

Fault detection
Fuzzy logic
Neural networks
Fuzzy systems
Membership functions
Learning systems
Experiments

Cite this

Dhimish, Mahmoud ; Holmes, Violeta ; Mehrdadi, Behrooz ; Dales, Mark. / Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection. In: Renewable Energy. 2018 ; Vol. 117. pp. 257-274
@article{fab36d0ef82c4945ac2c9dc4c20da711,
title = "Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection",
abstract = "This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface.The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1\{%}. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.",
keywords = "Photovoltaic system, Photovoltaic faults, Fault detection, ANN networks, Fuzzy logic systems",
author = "Mahmoud Dhimish and Violeta Holmes and Behrooz Mehrdadi and Mark Dales",
year = "2018",
month = "3",
doi = "10.1016/j.renene.2017.10.066",
language = "English",
volume = "117",
pages = "257--274",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection. / Dhimish, Mahmoud; Holmes, Violeta; Mehrdadi, Behrooz; Dales, Mark.

In: Renewable Energy, Vol. 117, 03.2018, p. 257-274.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection

AU - Dhimish,Mahmoud

AU - Holmes,Violeta

AU - Mehrdadi,Behrooz

AU - Dales,Mark

PY - 2018/3

Y1 - 2018/3

N2 - This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface.The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.

AB - This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface.The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.

KW - Photovoltaic system

KW - Photovoltaic faults

KW - Fault detection

KW - ANN networks

KW - Fuzzy logic systems

UR - https://www.journals.elsevier.com/renewable-energy

U2 - 10.1016/j.renene.2017.10.066

DO - 10.1016/j.renene.2017.10.066

M3 - Article

VL - 117

SP - 257

EP - 274

JO - Renewable Energy

T2 - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -