This study presents a defect classification method using the k-nearest neighbors (kNN) algorithm, optimized with current-voltage curves. This method identi...
To address the current limitations of low precision and high image data requirements in defect detection algorithms base…
In this paper, we address the problem of PV Panel Detection using a Convolutional Neural Network framework called YOLO. …
Conventional manual inspection techniques are labor-intensive and susceptible to human error. This study utilizes drone-…
This paper introduces a potential strategy for fault identification and classification through the utilization of machin…
The health condition evaluation of photovoltaic plants is considered a significant challenge for years. This paper propo…
To tackle these issues, a new machine-learning model will be presented. This model can accurately identify and categoriz…
Photovoltaic (PV) panels can experience various defects due to operational conditions, environmental factors, or human e…
Our study, which used actual PV power plant data for modeling, achieved a specific fault diagnosis accuracy of 93.18%, i…
Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. Th…
This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects …
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