• Login
    View Item 
    •   Home
    • Electrical and Computer Engineering
    • Faculty Research and Publications
    • Articles
    • View Item
    •   Home
    • Electrical and Computer Engineering
    • Faculty Research and Publications
    • Articles
    • 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

    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    A Comparative Evaluation of ...
    Size:
    393.6Kb
    Format:
    PDF
    Download
    Type
    Article
    Author
    Mian Qaisar, Saeed cc
    Alyamani, Nehal
    Waqar, Asad
    Krichen, Moez cc
    Subject
    Event-Driven Systems
    Circuits and Systems
    Signal Processing
    Machine Learning
    Computational Complexity
    Embedded Systems
    
    Metadata
    Show full item record
    Abstract
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN Computer Science. 3. 10.1007/s42979-021-00904-1. Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Power Quality Disturbances (PQDs) commonly occur in large scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.
    Publisher
    Springer Nature
    Sponsor
    Effat University
    Journal title
    SN Computer Science
    DOI
    10.1007/s42979-021-00904-1
    ae974a485f413a2113503eed53cd6c53
    10.1007/s42979-021-00904-1
    Scopus Count
    Collections
    Articles

    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.