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Please use this identifier to cite or link to this item: http://142.54.178.187:9060/xmlui/handle/123456789/891
Title: ENERGY THEFT DETECTION IN MULTI TENANT DATA CENTERS AND DISTRIBUTION LINE USING SMART GRIDS
Authors: Sivarathinabala, M.
Projoth, T. Niruban
Keywords: Engineering and Technology
Anamoly detection
Cloud computing
Multi tenant data center
Issue Date: 1-Nov-2019
Publisher: ARPN Journal of Engineering and Applied Sciences
Abstract: In recent years, High performance data centers are one of the challenging research areas in Cloud Computing. Multi Tenant Data centers are the infra structures that runs in large-scale Internet-based services. Energy consumption models are pivotal and efficient in designing and optimizing energy-efficient operations to curb excessive energy consumption in the data centers. Multi-tenant data centers (MTDCs) are the data centers which are popular with different operational structure. Despite the offered benefits, MTDCs are vulnerable to various cyber attacks. An important cyber attack is energy theft which can be launched by malicious tenants to reduce cost of the electricity consumption by attacking their own smart meters or neighboring meters to undercount its energy usage. Billions of money has been lost due to energy theft in data centers each year. Localization of energy theft detection is an effective way to limit the labor cost in detecting energy theft in data centers. It can be facilitated through deploying Digital Protective Relays (DPR) in the Power Distribution Unit of the data center. DPR is a microprocessor based device for fault detection. Along with DPR, an anamoly identification algorithm has been implemented called as Minimum Covariance Determinant .The smart meters along with Advance Metering Infrastructure is employed to measure the energy consumption of the tenants in data center, which is implemented in Smart grid environment. Such that data from both smart meter and Digital Protective Relay is send to the utility center to determine the Energy Theft in Multi Tenant Data Centers.
URI: http://142.54.178.187:9060/xmlui/handle/123456789/891
ISSN: 1819-6608
Appears in Collections:Journals

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