• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Transient stability assessment of a power system using multi-layer SVM method.

    Date
    2021-04-02
    Author
    Wang, Qilin
    Pang, Chengzong
    Alnami, Hashim
    Metadata
    Show full item record
    Citation
    Wang, Q., Pang, C., & Alnami, H. (2021). Transient stability assessment of a power system using multi-layer SVM method. Paper presented at the 2021 IEEE Texas Power and Energy Conference, TPEC 2021, doi:10.1109/TPEC51183.2021.9384918
    Abstract
    With the rapid growth of power systems, more large interconnections and the integration of large renewable energies make the systems more complicated. Therefore, transient stability assessment (TSA) has always been considered as one of the top challenges to ensure the security and operation of power systems. The development of Artificial Intelligence (AI) technologies, such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been drawn attentions to the power industry recently. Compared with traditional SVM, this paper presents an advanced TSA system using Multi-layer Support Vector Machine (ML-SVM) method. Basically, a Genetic Algorithm (GA) is used in ML-SVM to identify the valued feature subsets with varying numbers of features which makes full use of the input information. Transient stabilities of the system are determined based on the generator relative rotor angles obtained from the time-domain simulation. Data from the time-domain simulation are used as the inputs for ML-SVM training and testing. Then these trained SVMs are integrated to assess the transient stability of the power system. The simulation results show that the proposed method can reduce the possibility of misclassification of the system. Case study of IEEE 9-bus system on PowerWorld Simulator illustrates the effectiveness of the proposed approach.
    Description
    Click on the URL link to access this conference paper on the publisher’s website (may not be free.)
    URI
    https://doi.org/10.1109/TPEC51183.2021.9384918
    https://soar.wichita.edu/handle/10057/20070
    Collections
    • EECS Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2022  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV