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dc.contributor.authorWang, Qilin
dc.contributor.authorPang, Chengzong
dc.contributor.authorAlnami, Hashim
dc.date.accessioned2021-06-01T03:35:04Z
dc.date.available2021-06-01T03:35:04Z
dc.date.issued2021-04-02
dc.identifier.citationWang, 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/TPEC51183.2021.9384918en_US
dc.identifier.isbn978-1-7281-8612-2
dc.identifier.isbn978-1-7281-8611-5
dc.identifier.isbn978-1-7281-7345-0
dc.identifier.urihttps://doi.org/10.1109/TPEC51183.2021.9384918
dc.identifier.urihttps://soar.wichita.edu/handle/10057/20070
dc.descriptionClick on the URL link to access this conference paper on the publisher’s website (may not be free.)en_US
dc.description.abstractWith 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.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Texas Power and Energy Conference, TPEC 2021;
dc.subjectSupport vector machinesen_US
dc.subjectSimulationen_US
dc.subjectPower system stabilityen_US
dc.subjectStability analysisen_US
dc.subjectTransient analysisen_US
dc.subjectTime-domain analysisen_US
dc.subjectGenetic algorithmsen_US
dc.titleTransient stability assessment of a power system using multi-layer SVM method.en_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2021, IEEEen_US


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