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

55 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)257-274
Number of pages18
JournalRenewable Energy
Volume117
Early online date24 Oct 2017
DOIs
Publication statusPublished - Mar 2018

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