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Fast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Data

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dc.citedby3
dc.contributor.authorShahriyari M.en_US
dc.contributor.authorKhoshkhoo H.en_US
dc.contributor.authorPouryekta A.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorid57211193618en_US
dc.contributor.authorid24824311000en_US
dc.contributor.authorid56119220300en_US
dc.contributor.authorid6602912020en_US
dc.date.accessioned2023-05-29T07:25:17Z
dc.date.available2023-05-29T07:25:17Z
dc.date.issued2019
dc.descriptionAutomation; Forecasting; Intelligent systems; Phase measurement; Process control; Software testing; Stability; Support vector machines; System stability; Transients; Angle stability; Balanced faults; Different operating conditions; Fault clearance; Fault durations; Phasor measurement unit (PMUs); Stability of power system; Wide- area measurement systems (WAMS); Phasor measurement unitsen_US
dc.description.abstractThis paper deals with the prediction of the transient stability of power systems using only pre-fault and fault duration data measured by Wide Area Measurement System (WAMS). In the proposed method, the time-synchronized values of voltage and current generated by synchronous generators (SGs) are measured by Phasor Measurement Units (PMUs) installed at generator buses, and given as input to the proposed algorithm in order to extract a proper feature set. Then, the proposed feature set is applied to Support Vector Machine (SVM) classifier to predict the transient stability status after fault occurrence and before fault clearance. The robustness and accuracy of the proposed method has been extensively examined under both unbalanced and balanced fault conditions as well as under different operating conditions. The results of simulation performed on an IEEE 14-bus test system using DIgSILENT PowerFactory software show that the proposed method can accurately predict the transient stability status against different contingencies using only pre-disturbance and fault duration data. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8825052
dc.identifier.doi10.1109/I2CACIS.2019.8825052
dc.identifier.epage263
dc.identifier.scopus2-s2.0-85072940030
dc.identifier.spage258
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072940030&doi=10.1109%2fI2CACIS.2019.8825052&partnerID=40&md5=973176adb9c42206c70caa0766d1eef6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24627
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019 - Proceedings
dc.titleFast Prediction of Angle Stability Using Support Vector Machine and Fault Duration Dataen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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