Detecting Hot Spots in Photovoltaic Panels Using Low-Cost Thermal Cameras

Dávila-Sacoto, Miguel; Hernández-Callejo, Luís; Alonso‐Gómez, Víctor; Gallardo-Saavedra, Sara; González, L.G.

doi:10.1007/978-3-030-38889-8_4

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The experiments focused primarily on capturing bus voltages across various delays by employing the co-simulation platform used for this study. Given the variance in delay times among different EV manufacturers and communication tiers [23], a simulation incorporating diverse delay time values was performed (refer to Figure 16). It was noted that with extended delay times in updating the power consumed by the EV, the voltages at the customer buses tend to rise.…”

Section: Resultsmentioning

confidence: 99%

Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors

Davila-Sacoto,

Hernández-Callejo,

González

et al. 2024

Sensors

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Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation’s power load demand and significantly improves the end user’s voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation.

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Section: Resultsmentioning

confidence: 99%

Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors

Davila-Sacoto,

Hernández-Callejo,

González

et al. 2024

Sensors

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“…Since the classification of anomalies with thermal cameras is based on their geometry, a first step for their correct classification is the identification of the solar panel. Related research has also focused on the detection of the solar panels array [18,19].…”

Section: Introductionmentioning

confidence: 99%

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Díaz

Vlaminck

Lefkaditis³

et al. 2020

Sensors

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The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

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Methodology for Inspection of Defects in Photovoltaic Plants by Drone and Electroluminescence

Hernández-Callejo

Gallardo-Saavedra

Morales-Aragonés

et al. 2022

Communications in Computer and Information Science

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Detecting Hot Spots in Photovoltaic Panels Using Low-Cost Thermal Cameras

Cited by 5 publications

References 17 publications

Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors

Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Methodology for Inspection of Defects in Photovoltaic Plants by Drone and Electroluminescence

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