Solar panel surface dirt detection and removal based on arduino color recognition

Olorunfemi, Benjamin Oluwamuyiwa; Nwulu, Nnamdi; Ogbolumani, Omolola A.

doi:10.1016/j.mex.2022.101967

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…Pembacaan sensor semakin akurat fluktuasi data berada di range 0.67±0.73 pada inlet dan outlet. Hal ini dikarenakan proses kalibrasi dilakukan untuk membuat data pembacaan sensor semakin akurat juga bertujuan untuk memperkecil fluktuasi agar membatasi data berada di range dari nilai idealnya [16], [21], [22]. Jadi dapat disimpulkan baik dalam sistem yang sama proses kalibrasi sangat berguna dalam memproses data pembacaan sensor selain sebagai penambah akurasi juga bisa memfilter noise dan mengurangi fluktuasi data.…”

Section: Hasil Dan Pembahasanunclassified

Calibration of Arduino-based Temperature Sensors for Parabolic Solar Collectors with Phase Change Material

Ahmadi,

Setiawan,

Gunawati

et al. 2023

motivection

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The development of energy storage materials requires facilities for testing the materials' ability to store and release energy effectively. This study focuses on the development and calibration of a temperature measurement system for parabolic solar collectors equipped with heat storage media. We employed type-K thermocouples and MAX6675 modules with Arduino as our measurement tools. The calibration process involved comparing sensor measurements with those obtained from commercially calibrated instruments using seven different test materials. The calibration results demonstrated that our sensors exhibited an error rate ranging from 0.66 to 0.73, indicating their accuracy and suitability for monitoring temperature fluctuations in solar thermal collectors. Pengembangan material penyimpan energi thermal membutuhkan sarana untuk uji coba kemampuan bahan dalam menyimpan dan melepaskan energi. Penelitian ini fokus pada pengembangan dan kalibrasi sistem pengukuran temperatur untuk kolektor surya parabola yang memiliki media penyimpanan panas. Kami menggunakan sensor termokopel tipe-K dan modul MAX6675 dengan Arduino sebagai alat pengukuran. Proses kalibrasi melibatkan perbandingan hasil pengukuran sensor dengan alat komersil yang sudah terkalibrasi pada tujuh bahan uji yang berbeda. Hasil kalibrasi menunjukkan bahwa sensor kami memiliki tingkat kesalahan antara 0.66 hingga 0.73, menunjukkan bahwa sensor kami sudah akurat dan dapat digunakan untuk memonitor perubahan suhu pada kolektor surya termal.

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Section: Hasil Dan Pembahasanunclassified

Calibration of Arduino-based Temperature Sensors for Parabolic Solar Collectors with Phase Change Material

Ahmadi,

Setiawan,

Gunawati

et al. 2023

motivection

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“…Another work covered the topic of color sensing to detect dirt accumulation on solar panels [8]. The proposed method uses TCS3200 and Arduino Uno to detect dirt quickly and accurately.…”

Section: Related Workmentioning

confidence: 99%

Solar Panels Contamination Detection Using CNN

NAJAFLI,

IMAMVERDIYEV

2023

MSJ

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Solar panels, as an important source of renewable energy, are exposed to external factors such as dust accumulation and environmental pollution, which reduces their efficiency. To ensure timely cleaning of panels from contamination, monitoring of their condition is required. Monitoring can be carried out directly by a person or using visual detectors. This paper examines the second case, namely the use of AI to assess panel contamination. The AI used to assess the condition of the panels may be trained on images captured by cameras with a different resolution than the surveillance system in which it is embedded, which may result in the AI's inability to produce results under new conditions. This work evaluates the impact of changing the quality of images processed by AI on its effectiveness. To do this, a CNN (Convolutional Neural Network) model was used and a set of images of clean and dirty solar panels was taken, followed by their separation by resolution. The model was trained on a set of images of one quality and tested on others, and this approach was applied for each resolution. As results were obtained, elements from other sets were added to each set to stabilize the training of the neural network. During the work, the importance of using images of different qualities for training a neural network was assessed, as well as the minimum set to prevent overtraining due to the lack of quality differences. Taking into account the results obtained, it is possible to prevent the creation of inflexible neural networks, as well as save on using the minimum required data to stabilize training.

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“…To address this concern, various cleaning approaches-including manual cleaning, equipment-based brush cleaning, electrostatic cleaning, vibrating cleaning, heliotex cleaning, forced-air cleaning, etc., were introduced in the literature. 12,13,[19][20][21][22][23] While manual cleaning is time-consuming and laborious, equipment-based cleaning approaches demand complex construction and external power sources. Similarly, several other routinely used commercial goods made up of glass, wood, metal, etc., suffer from unwanted and unavoidable fouling of liquids (aqueous and oil/oily contaminants)-causing inconvenience to keep these substrates clean and dry.…”

Section: Introductionmentioning

confidence: 99%

“…, were introduced in the literature. 12,13,19–23 While manual cleaning is time-consuming and laborious, equipment-based cleaning approaches demand complex construction and external power sources. Similarly, several other routinely used commercial goods made up of glass, wood, metal, etc.…”

Section: Introductionmentioning

confidence: 99%

Covalent crosslinking chemistry for controlled modulation of nanometric roughness and surface free energy

Sarkar,

Dhar,

Das

et al. 2024

Chem. Sci.

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Solar panel surface dirt detection and removal based on arduino color recognition

Cited by 14 publications

References 34 publications

Calibration of Arduino-based Temperature Sensors for Parabolic Solar Collectors with Phase Change Material

Calibration of Arduino-based Temperature Sensors for Parabolic Solar Collectors with Phase Change Material

Solar Panels Contamination Detection Using CNN

Covalent crosslinking chemistry for controlled modulation of nanometric roughness and surface free energy

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