• Login
    View Item 
    •   Home
    • Electrical and Computer Engineering
    • Faculty Research and Publications
    • Books
    • View Item
    •   Home
    • Electrical and Computer Engineering
    • Faculty Research and Publications
    • Books
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Effat University RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentProfilesView

    My Account

    Login

    Statistics

    Display statistics

    Effective Power Quality Disturbances Identification Based on Event-Driven Processing and Machine Learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Book chapter
    Author
    Mian Qaisar, Saeed cc
    Date
    2020
    
    Metadata
    Show full item record
    Abstract
    Power quality (PQ) disturbances cause rigorous issues in smart grids and industries. The identification of PQ disturbances and effective prevention of such events are essential. In this framework, vital aspects are a precise understanding and a real-time treatment of the PQ disturbances. A novel tactic is described in this chapter. It is founded on the basis of event-driven processing, analysis and machine learning for successful and efficient detection of PQ disturbances‎. The definition is based on an sophisticated combination of event-driven signal amplification and segmentation and local extraction of features and categorization to achieve an effective and appropriate precision method.Two rigorous classifiers namely k-Nearest Neighbor (KNN) and Naïve Bias are used for the automatic identification. For a case study, the framework functionality is checked, and findings are reported. Compared to conventional equivalents, the first order of magnitude reduction in the cumulative sample count is accomplished by the invented method. It leads to substantial benefit in the reduction of computational complexity and effectiveness in terms of power consumption and delays in processing. The designed framework also attains an appropriate precision of categorization for the case of four-class PQ disturbances.
    Publisher
    Scrivener Publishing, Wiley
    Book title
    Green Energy: Solar Energy, Photovoltaics, and Smart Cities
    DOI
    10.1002/9781119760801.ch7
    ae974a485f413a2113503eed53cd6c53
    10.1002/9781119760801.ch7
    Scopus Count
    Collections
    Books

    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.