Anomaly detection in modern solar power plants using data-driven approaches is vital in reducing downtimes and increasing efficiency. In this paper, three machine learning models'' performances were analyzed to
This work presents a methodology for automatic fault detection in photovoltaic arrays, which is intended to be implemented in Colombia, in zones with difficult access and not interconnected
The faults in the PV panel, PV string and MPPT controller can be effectively identified using this method. The detection of fault is done by comparing the ideal and measured parameters. Any difference in measured and ideal values indicate the presence of a fault.
This advanced approach offers accurate detection and classification of various types of faults, including partial shading anomalies open and short circuit faults, degradation of PV modules. It provides a comprehensive framework for effective fault diagnosis in PV arrays.
This work presents a methodology for automatic fault detection in photovoltaic arrays, which is intended to be implemented in Colombia, in zones with difficult access and not interconnected to the
A fault diagnosis technique for photovoltaic systems based on neural networks is proposed by (Chine et al., 2016 ). Two different algorithms are developed to detect and classify eight different faults. The results demonstrated that this technique is highly capable of localizing and identifying the different kind of faults.
Additionally, the review emphasizes the significance of data acquisition and monitoring in PV systems for successful fault detection. The application of model-based fault detection methods in PV systems, while demonstrating efficacy, is not without its limitations.
The method includes as inputs the solar irradiation and module temperature of the PVM and then using this information together with the characteristics captured from the PV power generation system, provide fault diagnosis, including P m, I m, V m and V oc of the PVA during operation. Investigated faults are reported in Table 8.