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 language | English |
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Article number | 2150 |
Number of pages | 20 |
Journal | Electronics (Switzerland) |
Volume | 9 |
Issue number | 12 |
Early online date | 15 Dec 2020 |
DOIs | |
Publication status | Published - 15 Dec 2020 |