2311 919
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 4 , Issue 1, PP: . 41-55 , 2021 | Cite this article as | XML | Html |PDF

Title

A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel

  Ali A. Alwan 1 * ,   Abedallah Zaid Abualkishik 2

1  School of Theoretical & Applied Science, Ramapo College of New Jersey, USA
    (aaljuboo@ramapo.edu)

2  American University in the Emirates, Dubai, UAE
    (Abedallah.abualkishik@aue.ae)


Doi   :   https://doi.org/10.54216/JISIoT.040103


Abstract :

 In the era of fossil energy depletion and increasing environmental pollution, clean and renewable new energy represented by photovoltaic power generation has become an increasingly important part of multinational companies’ energy structure. With the advent of the era of photovoltaic parity, the use of photovoltaic tracking systems has become the best choice for many new large-capacity power stations. The cost of the support occupies a very large proportion in the investment of the entire power station construction. Therefore, the rationality of the design of the support, cost control and service life have become important ways for competition in the photovoltaic support industry. Based on the above background, the research content of this article is the application of artificial intelligence algorithms in the safety detection and reinforcement of photovoltaic steel supports. To be able to pass the monitoring data, this paper applies intelligent algorithms to perform faster and more accurate safety inspections on photovoltaic steel supports while minimizing labor costs, and to strengthen the photovoltaic steel supports, this paper chooses neural networks as the basic algorithm A structural model of a photovoltaic steel support was proposed. Finally, experimental simulations showed that the wavelet neural network reached 93.87%. Compared with traditional neural networks, wavelet neural networks perform better in fault prediction accuracy, but the speed needs to be improved. The method proposed in this paper has successfully completed the diagnosis of each component of the photovoltaic bracket in the safety inspection of the photovoltaic steel bracket, and meets the immediateness and accuracy required for the safety inspection of the photovoltaic bracket.

Keywords :

Photovoltaic System , Tracket Reinforcement , Artificial Intelligence Algorithm , Safety Detection , Wind Load

References :

[1] Mohanty S , Subudhi B , Ray P K . A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions[J]. IEEE Transactions on Sustainable Energy, 2015, 7(1):1-8.

[2] Soon T K , Mekhilef S . A Fast-Converging MPPT Technique for Photovoltaic System Under Fast-Varying Solar Irradiation and Load Resistance[J]. IEEE Transactions on Industrial Informatics, 2015, 11(1):176-186.

[3] Kazem H A , Al-Waeli A H A , Chaichan M T , et al. Design, measurement and evaluation of photovoltaic pumping system for rural areas in Oman[J]. Environment Development and Sustainability, 2016, 19(3):1-23.

[4] La N , Lepore M , Bourdon Y , et al. Renewable Energy Desalination: Development of Photovoltaic Electrodialysis Desalination System[J]. Guangdong Chemical Industry, 2015, 3(1):: 13.

[5] Vorndran S D , Chrysler B , Wheelwright B , et al. Off-axis holographic lens spectrum-splitting photovoltaic system for direct and diffuse solar energy conversion[J]. Applied Optics, 2016, 55(27):7522-7529.

[6] Chen X , Yang Y Y , Zhang Y J , et al. Influence of Illumination Probability of Photovoltaic System on Voltage of Power Distribution Networks[J]. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 2015, 43(4):112-118.

[7] Azali S , Sheikhan M . Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking[J]. Applied Intelligence, 2016, 44(1):88-110.

[8] Dhanapal S , Anita R . Voltage and Frequency Control of Stand Alone Self-Excited Induction Generator Using Photovoltaic System Based STATCOM[J]. Journal of Circuits, Systems, and Computers, 2016, 25(04):783-790.

[9] Guo X . A Novel CH5 Inverter for Single-Phase Transformerless Photovoltaic System Applications[J]. IEEE Transactions on Circuits & Systems II Express Briefs, 2017, 64(10):1197-1201.

[10] Khoo B , Wee C C , Mariappan M , et al. A Hybrid Artificial Intelligence Algorithm to Determine the Speed and Position in Multi Operation Mode Sensorless Brushed D.C. Motor[J]. Advanced Science Letters, 2017, 23(11):11374-11377.

[11] Price S , Flach P A . Computational support for academic peer review: A perspective from artificial intelligence[J]. Communications of the ACM, 2017, 60(3):70-79.

[12] Kuppusamy P G . An Artificial Intelligence Formulation and the Investigation of Glaucoma in Color Fundus Images by Using BAT Algorithm[J]. Journal of Computational & Theoretical Nanoscience, 2017, 14(4):1-5.

[13] Cath C , Wachter S , Mittelstadt B , et al. Artificial Intelligence and the 'Good Society': the US, EU, and UK approach[J]. Science and Engineering Ethics, 2017, 24(7625):1-24.

[14] Jiang F , Jiang Y , Zhi H , et al. Artificial intelligence in healthcare: Past, present and future[J]. Stroke & Vascular Neurology, 2017, 2(4):230.

[15] Citakoglu H . Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation[J]. Computers and Electronics in Agriculture, 2015, 118(2015):28-37.

[16] Ashrafian H , Darzi A , Athanasiou T . A novel modification of the Turing test for artificial intelligence and robotics in healthcare[J]. International Journal of Medical Robotics and Computer Assisted Surgery, 2015, 11(1):38-43.

[17] Kaplan J . Artificial intelligence: think again[J]. Communications of the ACM, 2016, 60(1):36-38.

[18] Johnson K W , Soto J T , Glicksberg B S , et al. Artificial Intelligence in Cardiology[J]. Journal of the American College of Cardiology, 2018, 71(23):2668-2679.

[19] Suresh L P , Dash S S , Panigrahi B K . Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Volume 1[J]. Advances in Intelligent Systems & Computing, 2015, 325(10):643-648.

[20] Ashrafian H . AIonAI: A Humanitarian Law of Artificial Intelligence and Robotics[J]. Science & Engineering Ethics, 2015, 21(1):29-40.

[21] Lee E J , Kim Y H , Kim N , et al. Deep into the Brain: Artificial Intelligence in Stroke Imaging[J]. Journal of Stroke, 2017, 19(3):277-285.

[22] Yeung S , Downing N L , Fei-Fei L , et al. Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety[J]. New England Journal of Medicine, 2018, 378(14):1271-1273.

[23] Dignum V . Ethics in artificial intelligence: introduction to the special issue[J]. Ethics & Information Technology, 2018, 20(1):1-3.

[24] Sernani P , Claudi A , Dragoni A F . Combining Artificial Intelligence and NetMedicine for Ambient Assisted Living: A Distributed BDI-based Expert System[J]. International Journal of E-Health and Medical Communications, 2015, 6(4):62-76.

[25] Lu X , Fei J. Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control[J]. International Journal of Advanced Robotic Systems, 2016, 13(3):1.

[26] Samir Lemeš,, Damir Štrbac,, Cabaravdic M. Using Industrial Robots to Manipulate the Measured Object in CMM[J]. International Journal of Advanced Robotic Systems, 2013, 10(281):1-9.

[27] Nair B B , Mohandas V P . Artificial intelligence applications in financial forecasting-a survey and some empirical results[J]. Intelligent Decision Technologies, 2015, 9(2):99-140.

[28] Bartsch G , Mitra A P , Mitra S A , et al. Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder[J]. Journal of Urology, 2016, 195(2):493-498.

[29] Das S , Dey A , Pal A , et al. Applications of Artificial Intelligence in Machine Learning: Review and Prospect[J]. International Journal of Computer Applications, 2015, 115(9):31-41.

[30] Jeganathan J , Knio Z , Amador Y , et al. Artificial Intelligence in Mitral Valve Analysis[J]. Annals of Cardiac Anaesthesia, 2017, 20(2):129.

 

 


Cite this Article as :
Style #
MLA Ali A. Alwan, Abedallah Zaid Abualkishik. "A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel." Journal of Intelligent Systems and Internet of Things, Vol. 4, No. 1, 2021 ,PP. . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)
APA Ali A. Alwan, Abedallah Zaid Abualkishik. (2021). A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel. Journal of Journal of Intelligent Systems and Internet of Things, 4 ( 1 ), . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)
Chicago Ali A. Alwan, Abedallah Zaid Abualkishik. "A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel." Journal of Journal of Intelligent Systems and Internet of Things, 4 no. 1 (2021): . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)
Harvard Ali A. Alwan, Abedallah Zaid Abualkishik. (2021). A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel. Journal of Journal of Intelligent Systems and Internet of Things, 4 ( 1 ), . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)
Vancouver Ali A. Alwan, Abedallah Zaid Abualkishik. A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel. Journal of Journal of Intelligent Systems and Internet of Things, (2021); 4 ( 1 ): . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)
IEEE Ali A. Alwan, Abedallah Zaid Abualkishik, A Proposed AI-based Algorithm for Safety Detection and Reinforcement of Photovoltaic Steel, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 4 , No. 1 , (2021) : . 41-55 (Doi   :  https://doi.org/10.54216/JISIoT.040103)