To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning.
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However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a fault identification and localization
Therefore, implementing efficient fault identification and diagnosis is crucial to increase the productivity and efficiency of PV installations, ensure their safe operation and reduce maintenance
Accordingly, in this study, we propose an enhanced YOLO-based deep learning model, named PV-YOLOv12n, specifically optimized for detecting defects in PV panels.
Photovoltaic (PV) arrays are critical in renewable energy systems, but their efficiency is often hindered by faults arising from dynamic environmental conditions such as shading, dust, and
Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a
However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a
All of the 1048 panels were successfully identified, parsed, and turned into polygons. Moreover, our fault detection algorithm, using two spatial autocorrelation techniques, was able to
The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point
To address the difficulty of detecting photovoltaic array faults using photovoltaic array model parameters in photovoltaic power stations, a photovoltaic array fault detection method based
To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning.
For this purpose, seven efficient features including array 2-voltages, array 2-currents, fill factor, maximum power to irradiance ratio, and voltage-temperature product, are extracted from the I-V