IoT and Cloud Based Remote Monitoring of Wind Turbine

Year 2019, Volume: 15 Issue: 4, 337 - 342, 30.12.2019

Batın Demircan , Ersin Akyüz

https://doi.org/10.18466/cbayarfbe.540812

Cited By: 6

Abstract

With the industry 4.0
revolution, the concept of industrial production will be reshaped with
information technologies and will rapidly shift to a new production
understanding. The Internet of things and cloud computing will play a vital role
as the most important elements of this transformation. In this study,
parameters that are crucial for the performance evaluation of a small power
wind turbine are measured. Measurements can be used to evaluate the performance
of the system and to avoid errors in the system. In the designed system, basic
parameters such as wind speed, air temperature, battery voltage and battery
current were measured and recorded through datalogger. These measurements were
sent to the Microsoft Azure cloud computing system and recorded here. At the
same time, visualization with the aid of the cloud system was performed and
viewed in real time on the web via Microsoft Power BI platform.

Keywords

Internet of things, Wind turbine, Cloud computing, Industry 4.0

References

• 1. Zabala Innovation Consulting, Plan for Dissemination of Results, https://www.romeoproject.eu/wp-content/uploads/2017/09/D9.1ROMEO_Dissemination_Plan.pdf (accessed August 27, 2018).
• 2. Annual combined onshore and offshore wind energy statistics, Wind in power 2017 Annual combined onshore and offshore wind energy Statistics. https://windeurope.org/wp-content/uploads/files/about-wind/statistics/WindEurope-Annual-Statistics-2017.pdf (accessed 14.03.2019).
• 3. Horn, G, Eliassen, F, Salvatore, Venticinque, S, Martino, BD, Bücher, M, Wood, L. 2016. An Architecture for Using Commodity Devices and Smart Phones in Health Systems. IEEE Workshop on ICT solutions for eHealth, 255-260.
• 4. Internet of Things in 2020: Roadmap for the future. Internet of Things 27, https://docbox.etsi.org/erm/Open/CERP%2020080609-10/Internet-of-Things_in_2020_EC-EPoSS_Workshop_Report_2008_v1-1.pdf (accessed 14.03.2019).
• 5. Fioccola, GB, Sommese R, Tufano, I, Canonico, R, Ventre G. 2016. Polluino: An Efficient Cloud-based Management of IoT Devices for Air Quality Monitoring. Control. Instrumentation, Energy & Communication (CIEC).
• 6. Manzano, S, Ortiz, RP, Guevara, D, Villacorta, A. 2014. An overview of remote monitoring PV Systems: Acquisition, Storage processing and publication of Real Time Data based on cloud computing. 4th International Workshop on Integration of Solar Power into Power Systems.
• 7. Zhao, JC, Zhang, JF, Feng, Y, Guo, J. The Study and Application of the IoT Technology in Agriculture. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China, 2010, pp462-465.
• 8. Illic, MD, Xie, L, Khan, UA. 2010. Modeling of Future Cyber–Physical Energy Systems for Distributed Sensing and Control. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 825-838.
• 9. Jayavardhana, G, Rajkumar B, Slaven, M, Marimuthu, P. 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645-1660.
• 10. Vuppala, SK, Kumar, HSK. Service Applications Exploiting the Internet of Things. SRII Global Conference, San Jose, CA, USA, 2014, pp195-202.
• 11. Internet of Things, Research and Innovation to Market Development. River Publishers Series in Communication, 2014
• 12. Atzori, L, Iera, A, Morabito, G. 2010. The Internet of Things: A survey. Computer Networks, 2787-2805.
• 13. Jamali, MS, Kumar, P, Khan, UA. Internet of Things: Architecture& Integration with Other Networks. First International Conference on Modern Communication & Computing Technologies (MCCT-14), Nawabshah, Pakistan, 2014, pp 88.
• 14. Bin, S,Yuan, L,Xiayoi, W. Research on Data mining Models for the Internet of Things. International Conference on Image Analysis and Signal Processing, Zhejiang, China, 2010, pp127-132.
• 15. Wang, Q, Gao, J. 2012. Research and application of risk and condition-based maintenance task optimization technology in an oil transfer station. Journal of Loss Prevention in the Process Industries, 6, 1018-1027.
• 16. Rannat, K, Lotus, M, Meriste, M, Preden, J. 2012. On dynamic models for wind farms as systems of systems. 7th International Conference on System of Systems Engineering (SoSE), 113-118.
• 17. Sajid, A., Abbas, H, Saleem, K. 2016. Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges. IEEE Access, 1375 – 1384.
• 18. Moness, M, Moustafa, AM. 2016. A Survey of Cyber-Physical Advances and Challenges of Wind Energy Conversion Systems: Prospects for Internet of Energy. IEEE Internet of Thinks Journal, 3, 2.
• 19. Karnouskos, S, Colombo, AW. Architecting the next generation of service-based SCADA/DCS system of systems. IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, Melbourne, VIC, Australia, 2011, pp359-364.
• 20. Community Research and Development Information Service, Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind, https://cordis.europa.eu/project/rcn/210289_en.html (accessed August 09, 2018).
• 21. Sujatha, K, Deepalakshmi, B, Cao, SQ. 2018. Optimal condition monitoring of wind turbines using intelligent image processing and Internet of Things. International Journal of Renewable Energy Technology, 9, 1-2.
• 22. Kalyanraj, D, Lenin, S, Sabareswar, S. Wind Turbine Monitoring and Control Systems Using Internet of Things. 21st Century Energy Needs - Materials, Systems and Applications (ICTFCEN), Kharagpur, India, 2016.
• 23. Microsoft Corporation, What is Azure?. https://azure.microsoft.com/en-us/ (accessed July 15, 2018).
• 24. Deneysan, YE-1040 Rüzgâr Türbini Eğitim Seti, http://deneysan.com/tr/urunler/yenilenebilir-enerji/ye-1040-ruzgar-turbini-egitim-seti/119 (accessed June 02, 2018).
• 25. Akyuz, E, Oktay, Z, Dincer, E. 2012. Performance investigation of hydrogen production from a hybrid wind-PV system. International Journal of Hydrogen Energy, 37, 21, 15841-16758.
• 26. Raspberry Pi Community, What is a Raspberry Pi?,https://www.raspberrypi.org/help/faqs/#introWhatIs (accessed July 15,2018).
• 27. CR1000 Measurement and Control Datalogger, https://www.campbellsci.com/cr1000 (accessed June 06,2018).
• 28. IBM’s Emerging Technology Services, Node-RED Flow-based programming for the Internet of Things, https://nodered.org (accessed June 03, 2018).
• 29. OASIS, A Comparison of AMQP and MQTT, https://lists.oasis-open.org/archives/amqp/201202/msg00086/StormMQ_WhitePaper_-_A_Comparison_of_AMQP_and_MQTT.pdf (accessed April 07, 2018).