Detection and classification of faults aimed at preventive maintenance of PV systems
This work addresses the challenge of fine fault detection in PV systems to enhance energy production and system longevity, representing an incremental improvement in domain-specific methods.
The paper tackled the problem of detecting and classifying fine faults, particularly snail trail type, in PV systems for preventive maintenance, achieving high accuracy with improved computational time.
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.