Vieira, Romênia G., Dhimish, Mahmoud, Araújo, Fábio M. U. de et al. (1 more author) (2020) PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic. Electronics. 2150. ISSN 2079-9292
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 02 Sep 2021 15:10 |
Last Modified: | 16 Oct 2024 17:49 |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177725 |