Abstract
Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.
Highlights
Photovoltaic systems have been developing quickly around the world over the last decade, and the global market is growing exponentially
As the results show in this table, Coefficient the neural network (NN) shows the highest classification accuracy, F1 score and
The purpose of the research was to identify the performance of module-level fault detection and classification to allow the development of a low-cost IoTbased sensor that could be deployed at large scale in low-power-output PV arrays
Summary
Photovoltaic systems have been developing quickly around the world over the last decade, and the global market is growing exponentially. [6] presented another detection technique based on P-V curve tracing for electrical fault detection and classification only at module level. This technique was tested in a laboratory with a small stand-alone PV system (600 W). Another tool for fault detection is the comparison between measured and simulated expected current, voltage and power values. Robust and advanced methods are required to study the thermograms for PV fault detection and classification Another third category of technique for PV fault detection is the application of ML using actual electrical measurement data, such as PV array current and voltage, on the DC side of the PV system.
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