PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Romênia G. Vieira, Mahmoud Dhimish, Fábio M.U. de Araújo, Maria I.S. Guerra

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This work introduces a new fault detection method for photovoltaic systems. The method identifies short‐circuited modules and disconnected strings on photovoltaic systems combining two machine learning techniques. The first algorithm is a multilayer feedforward neural network, which uses irradiance, ambient temperature, and power at the maximum power point as input variables. The neural network output enters a Sugeno type fuzzy logic system that precisely determines how many faulty modules are occurring on the power plant. The proposed method was trained using a simulated dataset and validated using experimental data. The obtained results showed 99.28% accuracy on detecting short‐circuited photovoltaic modules and 99.43% on detecting disconnected strings.

Original languageEnglish
Article number2150
Number of pages20
JournalElectronics (Switzerland)
Volume9
Issue number12
Early online date15 Dec 2020
DOIs
Publication statusPublished - 15 Dec 2020

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