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.
Highlights
Photovoltaic (PV) solar energy has been showing worldwide expansion, reaching an installed capacity of 627 GW [1]
The results showed an accuracy of 90.3% for the artificial neural network (ANN) and 100% for the Probabilistic Neural Network (PNN)
The findings showed a superior accuracy of the ANN, reaching 92.1%
Summary
Photovoltaic (PV) solar energy has been showing worldwide expansion, reaching an installed capacity of 627 GW [1]. The simulation is performed using MATLAB/Simulink® and PSIM® and validated with experimental data They trained the neural network using the simulated dataset, and the input variables are module temperature, irradiance, and voltage and current at the MPP. This approach detected short-circuited modules and string disconnections. The Radial Basis Function (RBF) neural network was trained with a simulated dataset and diagnosis faults on bypass diodes, short-circuit and open-circuit modules, and partial shading. This method was experimentally tested by the authors and showed good accuracy.
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