A photovoltaic array including several modules in series may show mismatching due to discrepancies among the module conditions, mainly due to partial shadowing. Therefore, the shape of the current-voltage curve deeply changes with respect to the one corresponding to uniform operation. This article shows that a small set of points around the maximum power allows us to detect the occurrence of the mismatching. This approach exploits such a limited information to detect if the module is subjected to mismatching, so that the adoption of a GMPPT algorithm can be avoided. The curvature change is identified by using different machine learning techniques: decision trees, multilayer perceptrons, radial basis functions, and support vector machines. To reduce the classification error, before the fitting of the models, we implement a novel process of selection of the training samples based on a self-organizing map. This procedure makes easier the optimization of the number of hidden neurons. The support vector classifier and the multilayer perceptron with one hidden layer outperform the other approaches, being the former better than the last for extreme mismatching. However, the prediction time of this multilayer perceptron is significantly smaller than the required by the support vector machine.

Detecting Partial Shadowing and Mismatching Phenomena in Photovoltaic Arrays by Machine Learning Techniques

Piliougine Rocha, Michel
;
Guejia-Burbano, Rudy Alexis;Spagnuolo, Giovanni
2022-01-01

Abstract

A photovoltaic array including several modules in series may show mismatching due to discrepancies among the module conditions, mainly due to partial shadowing. Therefore, the shape of the current-voltage curve deeply changes with respect to the one corresponding to uniform operation. This article shows that a small set of points around the maximum power allows us to detect the occurrence of the mismatching. This approach exploits such a limited information to detect if the module is subjected to mismatching, so that the adoption of a GMPPT algorithm can be avoided. The curvature change is identified by using different machine learning techniques: decision trees, multilayer perceptrons, radial basis functions, and support vector machines. To reduce the classification error, before the fitting of the models, we implement a novel process of selection of the training samples based on a self-organizing map. This procedure makes easier the optimization of the number of hidden neurons. The support vector classifier and the multilayer perceptron with one hidden layer outperform the other approaches, being the former better than the last for extreme mismatching. However, the prediction time of this multilayer perceptron is significantly smaller than the required by the support vector machine.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4821951
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