An Efficient LoRa-Enabled Fault Detection Using Self-Powered IoT Device

Year : 2025 | Volume : 12 | Issue : 01 | Page : 1-13
    By

    Yashaswini T.,

  • Supriya J,

  • Yashaswini T.,

  • Sahana V,

  • Safira Fatima1,

  1. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  2. Assistant Professor, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  3. Assistant Professor, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  4. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
  5. Student, Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_186448’);});Edit Abstract & Keyword

This study describes a revolutionary internet of things (IoT) solution for effective defect detection in a variety of applications. By utilizing an IoT device that generates energy from the surroundings, the suggested solution gets around the drawbacks of conventional battery-operated gadgets. The suggested approach makes use of a self-sustaining IoT gadget that can capture energy from the surroundings to get beyond the drawbacks of conventional battery-powered IoT devices. Longer functioning without frequent battery changes or external power sources is ensured by this architecture. The system makes use of LoRa technology’s long-range, low-power characteristics to provide dependable data transfer over long distances, even in difficult or faraway places. The self-powered IoT device uses sophisticated sensors to gather vital operational data, which is then sent to a cloud-based platform for analysis. The self-powered IoT gadget gathers vital information about the functioning of the monitored system by incorporating cutting-edge sensor technologies. After that, this data is sent to a cloud-based platform that uses advanced fault detection algorithms. These algorithms examine the gathered data to spot irregularities, forecast any problems, and promptly notify the appropriate parties. The efficacy of several fault detection methods is examined in the study. The design and execution of the self-powered IoT device are also examined in the article, with particular attention paid to sensors selection, power management tactics, and energy harvesting methods. Additionally covered will be the LoRa communication module, emphasizing its function in guaranteeing dependable data transfer and network connectivity. According to data from experiments, the system can effectively and independently identify defects, which makes it a useful tool for condition surveillance and predictive maintenance in a variety of industrial applications. To sum up, this study presents a novel IoT system that tackles the difficulties posed by conventional fault detection techniques. The suggested system provides a dependable and sustainable surveillance solution by fusing self-powered technology with LoRa communication.

Keywords: Internet of things (IoT), long-range wide area network (LoRa WAN), fault detection, distribution transformer, transmission, general packet radio service (GPRS)

[This article belongs to Journal of Microcontroller Engineering and Applications ]

How to cite this article:
Yashaswini T., Supriya J, Yashaswini T., Sahana V, Safira Fatima1. An Efficient LoRa-Enabled Fault Detection Using Self-Powered IoT Device. Journal of Microcontroller Engineering and Applications. 2025; 12(01):1-13.
How to cite this URL:
Yashaswini T., Supriya J, Yashaswini T., Sahana V, Safira Fatima1. An Efficient LoRa-Enabled Fault Detection Using Self-Powered IoT Device. Journal of Microcontroller Engineering and Applications. 2025; 12(01):1-13. Available from: https://journals.stmjournals.com/jomea/article=2025/view=0



document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_186448’);});Edit

References

1. Hidayatullah NA, Kurniawan AC, Kalam A. Power transmission and distribution monitoring using internet of things (IoT) for smart grid. IOP Conf Ser Mater Sci Eng. 2018; 384 (1): 012039. doi: 10.1088/1757-899X/384/1/012039.
2. Baimel D, Tapuchi S, Baimel N. Smart grid communication technologies – overview, research challenges and opportunities. In: 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Capri, Italy, June 22–24, 2016. pp. 116–120. doi: 10.1109/SPEEDAM.2016.7526014.
3. Sallam A, Malik O. Microgrids and smart grids. In: Electric Distribution Systems. 2nd edition. Boca Raton, FL, USA: Wiley; 2019. pp. 553–580. doi: 10.1002/9781119509332.CH20.
4. Saleem Y, Crespi N, Rehmani MH, Copeland R. Internet of things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions. IEEE Access. 2019; 7: 62962–63003. doi: 10.1109/ACCESS.2019.2913984.
5. Yuan J, Shen J, Pan L, Zhao C, Kang J. Smart grids in China. Renew Sustain Energy Rev. 2014; 37: 896–906. doi: 10.1016/j.rser.2014.05.051.
6. Malik FH, Lehtonen M. A review: agents in smart grids,. Electric Power Syst Res. 2016; 131: 71–79.
7. Parejo A, Personal E, Larios D, Guerrero J, García A, León C. Monitoring and fault location sensor network for underground distribution lines. Sensors. 2019; 19 (3): 576. doi: 10.3390/S19030576.
8. Odongo G, Musabe R, Hanyurwimfura D. A multinomial DGA classifier for incipient fault detection in oil-impregnated power transformers. Algorithms. 2021; 14 (4): 128. doi: 10.3390/a14040128.
9. Rigatos G. Condition monitoring of the electric power transmission and distribution system. In: Intelligent Renewable Energy Systems. Cham, Switzerland: Springer; 2016. pp. 463–505. doi: 10.1007/978-3-319-39156-4_10.
10. Taneja J. Measuring electricity reliability in Kenya. Energy Policy. 2018; 12: 6. doi: 10.3403/30077701.
11. Jamali S, Bahmanyar A, Bompard E. Fault location method for distribution networks using smart meters. Measurement. 2017; 102: 150–157. doi: 10.1016/j.measurement.2017.02.008.
12. Nduhuura P, Garschagen M, Zerga A. Impacts of electricity outages in urban households in developing countries: a case of Accra, Ghana. Energies. 2021; 14 (12): 3676. doi: 10.3390/EN14123676.
13. Al Mhdawi AK, Al-Raweshidy HS. A smart optimization of fault diagnosis in electrical grid using distributed software-defined IoT system. IEEE Syst J. 2020; 14 (2): 2780–2790. doi: 10.1109/JSYST.2019.2921867.
14. Hilorme T, Sokolova L, Portna O, Lysiak L, Boretskaya N. Smart grid concept as a perspective for the development of Ukrainian energy platform. IBIMA Business Rev. 2019; 2019: 1–13. doi: 10.5171/2019.923814.
15. Hakeem SAA, Hady AA, Kim HW. RPL routing protocol performance in smart grid applications based wireless sensors: experimental and simulated analysis. Electronics. 2019; 8 (2): 1–23. doi: 10.3390/ELECTRONICS8020186.
16. Baležentis T, Štreimikiene D. Sustainability in the electricity sector through advanced technologies: energy mix transition and smart grid technology in China. Energies. 2019; 12: 1142. doi: 10.3390/EN12061142.
17. Kappagantu R, Daniel SA. Challenges and issues of smart grid implementation: a case of Indian scenario. J Electric Syst Inform Technol. 2018; 5 (3): 453–467. doi: 10.1016/j.jesit.2018.01.002.
18. Pamuk N, Uyaroglu Y. The fault diagnosis for power system using fuzzy Petri nets. Przeglad Elektrotechniczny. 2012; 88 (7a): 99–102.
19. He ZY, Yang JW, Zeng QF, Zang TL. Fault section estimation for power systems based on adaptive fuzzy Petri nets. Int J Comput Intell Syst. 2014; 7 (4): 605–614. doi: 10.1080/18756891.2014.960259.
20. Tan M, Li J, Zhao S, Cheng X. Method of power grid fault diagnosis using intuitionistic fuzzy Petri nets with inhibitor arcs. In: 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), Dali, China, May 24–27, 2019. pp. 568–573. doi: 10.1109/DDCLS.2019.8908886.
21. Sun J, Qin S-Y, Song Y-H. Fault diagnosis of electric power systems based on fuzzy Petri nets. IEEE Trans Power Syst. 2004; 19 (4): 2053–2059. doi: 10.1109/TPWRS.2004.836256.
22. Wang G, Liu Y, Chen X, Yan Q, Sui H, Ma C, Zhang J. Power transformer fault diagnosis system based on internet of things. EURASIP J Wireless Commun Netw. 2021; 2021 (1): 1–24. doi: 10.1186/s13638-020-01871-6.
23. Krishna RN, Niranjan L, Shyamsundar N, Venkatesan C. IoT based transmission line fault monitoring system. Int J Res Anal Rev. 2020; 7 (3): 465–468.
24. Mnati M, Van den Bossche A, Chisab R. A smart voltage and current monitoring system for three phase inverters using an Android smartphone application. Sensors. 2017; 17 (4): 872. doi: 10.3390/S17040872.
25. Mehmood MU, Ulasyar A, Khattak A, Imran K, Zad HS, Nisar S. Cloud based IoT solution for fault detection and localization in power distribution systems. Energies. 2020; 13 (11): 2686. doi: 10.3390/EN13112686.
26. Cui X. The internet of things: daily life, automated. In: Moran S, editor. Ethical Ripples of Creativity and Innovation. London, UK: Palgrave Macmillan; 2017. pp. 61–68. doi: 10.1057/9781137505545.
27. Stankovic JA. Research directions for the internet of things IEEE Internet of Things J. 2014; 1 (1): 3–9. doi: 10.1109/JIOT.2014.2312291.
28. Gupta G, Van Zyl R. Energy harvested end nodes and performance improvement of LoRa networks. Int J Smart Sensing Intell Syst. 2021; 14 (1): 1–15. doi: 10.21307/IJSSIS-2021-002.
29. Bouguera T, Diouris JF, Chaillout JJ, Jaouadi R, Andrieux G. Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors. 2018; 18 (7): 1–23. doi: 10.3390/S18072104.
30. Khutsoane O, Isong B, Abu-Mahfouz AM. IoT devices and applications based on LoRa/LoRaWAN. In: IECON 2017 – 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, October 29–November 1, 2017. pp. 6107–6112. doi: 10.1109/IECON.2017.8217061.
31. Adelantado F, Vilajosana X, Tuset-Peiro P, Martinez B, Melia-Segui J, Watteyne T. Understanding the limits of LoRaWAN. IEEE Commun Mag. 2017; 55 (9): 34–40. doi: 10.1109/MCOM.2017.1600613.
32. Iqbal M, Abdullah AYM, Shabnam F. An application based comparative study of LPWAN technologies for IoT environment. In: 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, June 5–7, 2020. pp. 1857–1860. doi: 10.1109/TENSYMP50017.2020.9230597.
33. Augustin A, Yi J, Clausen T, Townsley W. A study of LoRa: long range & low power networks for the internet of things. Sensors. 2016; 16 (9): 1466. doi: 10.3390/S16091466.
34. Fraile LP, Tsampas S, Mylonas G, Amaxilatis D. A comparative study of LoRa and IEEE 802.15.4-based IoT deployments inside school buildings. IEEE Access. 2020; 8: 160957–160981. doi: 10.1109/ACCESS.2020.3020685.
35. Ariff MAIM, Then YL, Tay FS. Establish connection between remote areas and city to improve healthcare services. In: 2019 International Conference on Green and Human Information Technology (ICGHIT), Kuala Lumpur, Malaysia, January 15–17, 2019. 18–23, doi: 10.1109/ICGHIT.2019.00012.
36. Mekkil K. A comparative study of LPWAN technologies for largescale IoT deployment. ICT Express. 2019; 5 (1): 1–7. doi: 10.1016/j.icte.2017.12.005.
37. Samad A. A Comparative Study on Emerging Radio Technologies for IoT. Tech. Rep. JKL072-5472. Seoul, South Korea, Jaewoo Corp.; 2020.
38. Gambiroza JC, Mastelic T, Solic P, Cagalj M. Capacity in LoRaWAN networks: challenges and opportunities. In: 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, June 18–21, 2019. pp. 1–6. doi: 10.23919/SpliTech.2019.8783184.
39. Salih TA, Noori MS. Using LoRa technology to monitor and control sensors in the greenhouse. IOP Conf Ser Mater Sci Eng. 2020; 928 (3): 032058. doi: 10.1088/1757899X/928/3/032058.
40. Jung M, Niculita O, Skaf Z. Comparison of different classification algorithms for fault detection and fault isolation in complex systems. Procedia Manuf. 2018; 19: 111–118. doi: 10.1016/j.promfg.2018.01.016.
41. Kaur A, Brar YS, Leena G. Fault detection in power transformers using random neural networks. Int J Electric Comput Eng. 2019; 9 (1): 78–84. doi: 10.11591/IJECE.V9I1.PP78-84.
42. Devalal S, Karthikeyan A. LoRa technology – an overview. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, March 29–31, 2018. pp. 284–290. doi: 10.1109/ICECA.2018.8474715.
43. International Renewable Energy Agency. A Renewable Energy Roadmap Report. [Online]. Abu Dhabi, United Arab Emirates: International Renewable Energy Agency; 2015. Available at https://www.irena.org/publications
44. Albert B, Kendagor K, Prevost RJ. Energy diversity and development in Kenya. Joint Force Q. 2013; 70: 94–99.

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 10/01/2025
Accepted 16/01/2025
Published 28/01/2025
Publication Time 18 Days
111

[first_name] [last_name]

My IP

PlumX Metrics