A supervised ensemble learning method for fault diagnosis in photovoltaic strings

Abstract

This study proposes a fault diagnosis method based on the use of a machine learning (ML) technique called ensemble learning (EL) for photovoltaic (PV) systems. EL methods aim to obtain better generalizability and prediction accuracy than a single ML algorithm by combining the predictions of multiple algorithms. In this context, first the most relevant features are selected by using grid-search with crossvalidation. Then each learning algorithm and the EL model that will combine them have been improved in terms of parameter optimization. Results show that, with the appropriate features and optimized parameters for each single learning algorithm and the EL model, the proposed method not only improves the classification performance but also has a strong generalization ability for PV system fault diagnosis.