• Login
    View Item 
    •   Home
    • Theses & Dissertations
    • 2017 - Mines Theses & Dissertations
    • View Item
    •   Home
    • Theses & Dissertations
    • 2017 - Mines Theses & Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Mines RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    Login

    Mines Links

    Arthur Lakes LibraryColorado School of Mines

    Statistics

    Display Statistics

    Compressive power systems: applications of compressive sensing and sparse recovery in the analysis of smart power grids

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Babakmehr_mines_0052E_11196.pdf
    Size:
    7.213Mb
    Format:
    PDF
    Download
    Author
    Babakmehr, Mohammad
    Advisor
    Simões, M. Godoy
    Date issued
    2017
    Keywords
    graph theory
    signal processing
    sparse recovery
    power systems
    compressive sensing
    smart grid
    
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/11124/170676
    Abstract
    During the last two decades, an intelligence revolution intensively changed the technology of electrical power networks, forming a new generation of power systems called smart grids (SGs). In general, “the term SG refers to electricity networks that can intelligently integrate the behavior and actions of all parameters and users connected to them”. This revolution transformed the traditional power grid from a single-layer physical system into a huge cyber-physical network, using a layer of information that flows through the system. This information includes the status of several parameters in the network such as bus voltages, branch currents, and load consumption. In addition to the actuating or controlling commands fed back to the network from controllers and decision making units. Nowadays, the Supervisory Control and Data Acquisition (SCADA) system in addition to the Wide Area Mentoring System (WAMS) provide electrical data for each local system in near real time. The most popular sensing technology used widely in SG data collection systems is the high sampling rate synchronous Phasor Measurement Unit (PMU). Nevertheless, collecting, storing, transferring, and analyzing the huge amount of data flowing through the information layer of the SG, together with the uncertainty caused by renewable-based distributed generators and unpredictable load characteristics, challenge the standard methods for security, monitoring, and control. In this thesis, we aim to exploit the inherent sparse nature of both structure and data in SGs to introduce new, fast and reliable techniques to address the challenges related to real time data analysis, monitoring and security in smart power systems. Our work is primarily inspired by a new paradigm in the field of signal processing widely known as the theory of Compressive Sensing and Sparse Recovery (CS-SR). Generally, CS-SR implies that a sufficiently sparse phenomenon can be recovered from a small set of randomly collected measurements. In our early chapters, combining the sparse sampling theory from the field of CS with concepts borrowed from graph theory, we introduce a set of sparse recovery-based mathematical formulations to address famous global monitoring challenges such as power line outage localization, network topology identification, network dynamic behavior modeling and tracking. Lastly, we develop a modified sparse representation-based classification approach to deal with a challenging local monitoring problem widely known as power quality events recognition.
    Rights
    Copyright of the original work is retained by the author.
    Collections
    2017 - Mines Theses & Dissertations

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.