Infrared scanning of photovoltaic panels

Why is IR imaging used in PV power plants?

1. Introduction Thermography, also called infrared (IR) imaging, has been a frequently used tool for years to detect faulty or underperforming modules and strings in PV power plants. IR is so attractive because the images are taken during operation in a non-contact and non-destructive way without interfering with the electrical system.

How do PV panels differ from infrared thermal imaging?

In contrast to infrared thermal imaging detection in PV panels, the detection of electronic components differs due to their complex and intricate structures. Often, external excitation is required to induce heating for these electronic components.

What are the advantages of infrared (IR) imaging for PV modules?

g techniques, which identify faults and problems developing with PV modules.The use o infrared (IR) imaging for the evaluation of PV modules has many advantages. First of all, a great number of failures developed on PV modules can be detected

Can PV modules be tested with infrared thermal imaging?

Traditional electrical performance testing of PV modules is a mature testing method, but it has limited fault-detection capabilities . With the advent of digital cameras, charge-coupled devices (CCDs), and uncooled focal plane array (UFPA) detectors, optical-based infrared thermal imaging detection has gained popularity [78, 79].

Can remote sensing detect anomalies in photovoltaic systems?

In this research, a remote sensing method is proposed for the fast and efficient detection of anomalies in photovoltaic (PV) systems. An infrared radiation (IR) camera mounted on flying vehicles (e.g., drone) to capture IR images of solar panels. Then, convolutional neural networks (CNN) are developed to detect abnormal cells in the PV systems.

How do IR cameras detect abnormal cells in solar panels?

An infrared radiation (IR) camera mounted on flying vehicles (e.g., drone) to capture IR images of solar panels. Then, convolutional neural networks (CNN) are developed to detect abnormal cells in the PV systems. The CNN model are then quantized and implemented on edge devices for real-time detection of anomalies.