Identification of Underground Faults using Internet of Things (IoT)
This paper aims at the detection of the fault occurring in an underground cable system, pinpoint the location of the fault using Extreme Learning Machine and convey the parameters of the fault occurred using Internet of Things (IoT). The underground cables have seen a steep increase in usage due to...
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Published in | Journal of physics. Conference series Vol. 1714; no. 1; pp. 12018 - 12025 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Bristol
IOP Publishing
01.01.2021
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Online Access | Get full text |
ISSN | 1742-6588 1742-6596 |
DOI | 10.1088/1742-6596/1714/1/012018 |
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Abstract | This paper aims at the detection of the fault occurring in an underground cable system, pinpoint the location of the fault using Extreme Learning Machine and convey the parameters of the fault occurred using Internet of Things (IoT). The underground cables have seen a steep increase in usage due to its many inherent advantages compared to the overhead cables. However, various methods to pinpoint the presence of fault in the underground cables of large lengths has proved futile and the communication of the fault information has been proved to be expensive. Our proposed project uses Extreme Learning Machine to detect the type of fault and to pinpoint the location of the fault in the system. Extreme Learning Machine Classification and Regression Algorithms are utilized to predict the type of fault in the system and the distance of the fault from the sending end. The regression algorithm is compared to other ML algorithms relevant to the subject matter at hand. The fault location conveyed by the use of GSM is limited by the fact that only text messages in the form of Short Messaging Services can be sent to the user concerned increasing the cost incurred by the user. Our project uses the internet to communicate through a big arsenal of methods, like e-mail and other social media services possible, thus widening the scope of communication while decreasing the size of the product. |
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AbstractList | This paper aims at the detection of the fault occurring in an underground cable system, pinpoint the location of the fault using Extreme Learning Machine and convey the parameters of the fault occurred using Internet of Things (IoT). The underground cables have seen a steep increase in usage due to its many inherent advantages compared to the overhead cables. However, various methods to pinpoint the presence of fault in the underground cables of large lengths has proved futile and the communication of the fault information has been proved to be expensive. Our proposed project uses Extreme Learning Machine to detect the type of fault and to pinpoint the location of the fault in the system. Extreme Learning Machine Classification and Regression Algorithms are utilized to predict the type of fault in the system and the distance of the fault from the sending end. The regression algorithm is compared to other ML algorithms relevant to the subject matter at hand. The fault location conveyed by the use of GSM is limited by the fact that only text messages in the form of Short Messaging Services can be sent to the user concerned increasing the cost incurred by the user. Our project uses the internet to communicate through a big arsenal of methods, like e-mail and other social media services possible, thus widening the scope of communication while decreasing the size of the product. |
Author | Rengaraj, R Venkatakrishnan, G R Dev Ganesh, S Abinandhan, R Adithya Pillai, R Aravind, K |
Author_xml | – sequence: 1 givenname: R surname: Rengaraj fullname: Rengaraj, R email: rengarajr@ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering – sequence: 2 givenname: G R surname: Venkatakrishnan fullname: Venkatakrishnan, G R email: venkatakrishnangr@ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering – sequence: 3 givenname: R surname: Adithya Pillai fullname: Adithya Pillai, R email: adithyapillai16006@eee.ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering – sequence: 4 givenname: R surname: Abinandhan fullname: Abinandhan, R email: abinandhan16004@eee.ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering – sequence: 5 givenname: S surname: Dev Ganesh fullname: Dev Ganesh, S email: devganesh160023@eee.ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering – sequence: 6 givenname: K surname: Aravind fullname: Aravind, K email: aravind16012@eee.ssn.edu.in organization: Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering |
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Cites_doi | 10.1016/j.neucom.2005.12.126 10.31142/ijtsrd19037 |
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References | JPCS_1714_1_012018bib4 JPCS_1714_1_012018bib5 JPCS_1714_1_012018bib2 JPCS_1714_1_012018bib6 JPCS_1714_1_012018bib7 Huang (JPCS_1714_1_012018bib3) 2006; 70 Perktold (JPCS_1714_1_012018bib8) 2020 Dinesh (JPCS_1714_1_012018bib1) 2018; 3 |
References_xml | – volume: 70 start-page: 489 year: 2006 ident: JPCS_1714_1_012018bib3 article-title: Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 3 start-page: 668 issn: 2456-6470 year: 2018 ident: JPCS_1714_1_012018bib1 article-title: Design and Detection of Underground Cable Fault Using Raspberry Pi & IoT System publication-title: International Journal of Trend in Scientific Research and Development doi: 10.31142/ijtsrd19037 – ident: JPCS_1714_1_012018bib2 – year: 2020 ident: JPCS_1714_1_012018bib8 article-title: statsmodelsv0.12.0. dev0(+250) – ident: JPCS_1714_1_012018bib4 – ident: JPCS_1714_1_012018bib7 – ident: JPCS_1714_1_012018bib5 – ident: JPCS_1714_1_012018bib6 |
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SubjectTerms | Algorithms Artificial neural networks Cellular communication Extreme Learning machine (ELM) Fault detection Fault Identification Fault location Internet of Things Machine learning Physics Short message service Text messaging Time Domain Reflectometry Underground cables |
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Title | Identification of Underground Faults using Internet of Things (IoT) |
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