IoT Based Solar Panel Analysis using Thermal Imaging

Phoolwani, Udit Kumar; Sharma, Tanveer; Singh, Abhishek K.; Gawre, Suresh Kumar

doi:10.1109/sceecs48394.2020.114

Cited by 17 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ones in the voltage coefficient matrix, A , indicate locations at which the switch did not bypass any module, and zeros are locations at which the switch did bypass the module. To find the voltage for each module in the string, as the voltage coefficient matrix A is square matrix, the inverse can be used as in (9).…”

Section: B Module Fault Localizationmentioning

confidence: 99%

A Novel Fault Localization Technique for PV Systems Using a Single-Voltage Sensor

Alhejab,

Abbasi,

Ahmed

2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: B Module Fault Localizationmentioning

confidence: 99%

A Novel Fault Localization Technique for PV Systems Using a Single-Voltage Sensor

Alhejab,

Abbasi,

Ahmed

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, in ref. [ 20 ], the performance of PV arrays in the presence of faults was analyzed using a PV analyzer, an electrical power meter, and an IRT camera. Moreover, with the innovation of IoT technology, ref.…”

Section: Related Workmentioning

confidence: 99%

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

Cardinale-Villalobos,

Jimenez-Delgado,

García-Ramírez

et al. 2023

Sensors

View full text Add to dashboard Cite

Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/m2, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/m2. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors.

show abstract

“…Infrared images are able to identify hot spots on the surface of the panels. Phoolwani et al (2020) analyzed at normal and some faulty condition the solar power meter using thermal camera to compare performances. Results were obtained at normal, partial shading and dust accumulated conditions using PV analyzer.…”

Section: Literature Reviewmentioning

confidence: 99%

Dust detection in solar panel using image processing techniques: A review

Dantas

Mendes

Maia

et al. 2020

RSD

View full text Add to dashboard Cite

The performance of a photovoltaic panel is affected by its orientation and angular inclination with the horizontal plane. This occurs because these two parameters alter the amount of solar energy received by the surface of the photovoltaic panel. There are also environmental factors that affect energy production, one example is the dust. Dust particles accumulated on the surface of the panel reduce the arrival of light to the solar modules, reducing the amount of generated energy. The cleaning or mitigation of the modules is important and, to optimize these processes, constant monitoring and evaluation must be carried out. In order to increase the efficiency of photovoltaic panels, the use of image processing methods can be considered for the detection of dust. Therefore, the creation of a document that gathers and analyzes the results of different works developed to solve this problem facilitates access to information, allowing a better understanding of what has already been done and how it can be improved. The objective of this article is to review researches that uses image processing techniques to detect dust on solar panels, in order to compile information to assist research in the area and provide inspiration for future studies.

show abstract

IoT Based Solar Panel Analysis using Thermal Imaging

Cited by 17 publications

References 8 publications

A Novel Fault Localization Technique for PV Systems Using a Single-Voltage Sensor

A Novel Fault Localization Technique for PV Systems Using a Single-Voltage Sensor

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

Dust detection in solar panel using image processing techniques: A review

Contact Info

Product

Resources

About