Detection and classification of faults aimed at preventive maintenance
  of PV systems
Edgar Hernando Sepúlveda Oviedo Louise Travé-Massuyès Audine Subias Marko Pavlov Corinne Alonso
Diagnosis in PV systems aims to detect, locate and identify faults.
Diagnosing these faults is vital to guarantee energy production and extend the
useful life of PV power plants. In the literature, multiple machine learning
approaches have been proposed for this purpose. However, few of these works
have paid special attention to the detection of fine faults and the specialized
process of extraction and selection of features for their classification. A
fine fault is one whose characteristic signature is difficult to distinguish to
that of a healthy panel. As a contribution to the detection of fine faults
(especially of the snail trail type), this article proposes an innovative
approach based on the Random Forest (RF) algorithm. This approach uses a
complex feature extraction and selection method that improves the computational
time of fault classification while maintaining high accuracy.