Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.688348
Title: Sparse modelling for machine to machine applications in smart grid
Author: Hao, Jinping
ISNI:       0000 0004 5917 4718
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2015
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Abstract:
Since the traditional power grid has been showing incapability of meeting the requirements of modern society, the development and implementation of the smart grid (SG) have been a common consensus among policymakers, business leaders, and other stakeholders. The SG is expected to have pervasive control and provide reliable services by utilizing modern information and communications technologies (ICTs). With the advent of smart grid, a number of technical and procedural challenges has emerged and developing efficient algorithms and effective solutions is increasingly urgent. In smart grid, the machine-to-machine (M2M) communication is regarded as one of the key techniques that allow pervasive control and monitoring. In this thesis, various challenges in M2M applications in SG are investigated. ~Thile sparsity plays an instrumental role in signal processing techniques, which enables the theory of compressive sensing (CS) , this thesis explores the sparse properties in M2M networks and exploits the sparsity in various M2M applications in smart grid.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.688348  DOI: Not available
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