Robust Intelligent Learning Algorithm Using Random Forest and Modified-Independent Component Analysis for PV Fault Detection: In Case of Imbalanced Data

Abstract

Photovoltaic (PV) systems are vulnerable to failures due to undesired conditions. Faults can occur unpredictably and remain challenging to recognize. In this study, inverter fault, voltage sag, partial shading, and open circuit were considered as the PV faults. However, the faults become eight target classes since each fault considers two different conditions: intermediate power point tracking and maximum power point tracking. Machine learning techniques have recently become the most interesting methods for solving PV failures. This paper proposes a random forest and modified independent component analysis (RF-MICA) to detect the occurrence of PV faults. The MICA was developed for dimensionality reduction for enhanced performance, whereas previous studies only focused on principal component analysis. Two strategies are introduced to address an imbalanced dataset: the synthetic minority oversampling technique as scenario 1 and random undersampling as scenario 2 for oversampling and undersampling methods, respectively. These techniques are essential for investigation because PV fault data can become unbalanced, and they have not been fully addressed in previous studies. The results indicated that the proposed model performs better than other algorithms, with high accuracy and low computational time. RF-MICA yielded accuracy rates of 99.88% and 99.43% for scenarios 1 and 2, respectively.