Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT

Geno Peter, Albert Alexander Stonier, Punit Gupta, Daniel Gavilanes (), Manuel Masias Vergara () and Jong Lung Sin
Additional contact information
Geno Peter: CRISD, School of Engineering and Technology, University of Technology Sarawak, No.1 Jalan Universiti, Sibu 96000, Malaysia
Albert Alexander Stonier: School of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
Punit Gupta: School of computer Science, University College Dublin, D04 V1W8 Dublin, Ireland
Daniel Gavilanes: Center for Nutrition & Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
Manuel Masias Vergara: Center for Nutrition & Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
Jong Lung Sin: Sarawak Electricity Supply Corporation, Kuching 93050, Malaysia

Energies, 2022, vol. 15, issue 21, 1-22

Abstract: Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly.

Keywords: fault monitoring; normalizing technologies; Microsoft Azure Internet of Things (IoT); System Average Interruption Duration Index; System Average Interruption Frequency Index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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