Publication:
Real-Time Transient Instability Identification in Power Systems using a PMU-Based EMS System

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dc.citedby0
dc.contributor.authorSarmin M.K.N.M.en_US
dc.contributor.authorSaadun N.en_US
dc.contributor.authorAzmi M.T.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorid56177713500en_US
dc.contributor.authorid55612145600en_US
dc.contributor.authorid56340771500en_US
dc.contributor.authorid35606640500en_US
dc.date.accessioned2024-10-14T03:19:20Z
dc.date.available2024-10-14T03:19:20Z
dc.date.issued2023
dc.description.abstractModern power systems are confronted with operational challenges that increase the risk of transient instability. Existing Dynamic Security Assessment (DSA) tools have limitations, necessitating accurate and timely assessment of transient stability. To address this, a novel approach for real-time identification of transient instability is introduced in this paper using a Thevenin equivalent network model. The proposed method leverages synchronized phasor measurements and incorporates PMU-based Energy Management System (EMS) with Linear State Estimation (LSE) alongside snapshots from existing EMS systems, cascading analysis application, and a performance index (PI) to rank cascading outages based on severity. A case study demonstrates the effectiveness of the proposed method to identify transient instabilities in a large interconnected power system through real-time hardware-in-the-loop (HIL) simulations. By offering enhanced accuracy and efficiency in real-time stability assessment, the method empowers grid operators to promptly act and prevent wide area outages during challenging operating conditions. Future research directions encompass integration with Wide-Area Monitoring, Protection, and Control (WAMPAC) system, incorporation of advanced machine learning techniques alongside data analytics, as well as scalability examination across diverse operating conditions and contingencies. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/I-PACT58649.2023.10434732
dc.identifier.scopus2-s2.0-85187012400
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85187012400&doi=10.1109%2fI-PACT58649.2023.10434732&partnerID=40&md5=7921c56779a421f00c9b36fdf0be6f9e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34370
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023
dc.subjectcascading analysis
dc.subjecthardware-in-the-loop simulation
dc.subjectlinear state estimation
dc.subjectperformance index
dc.subjectphasor measurement unit
dc.subjectTransient stability
dc.subjectData Analytics
dc.subjectElectric power system interconnection
dc.subjectElectric power system protection
dc.subjectEnergy management systems
dc.subjectHardware-in-the-loop simulation
dc.subjectLearning systems
dc.subjectReal time systems
dc.subjectState estimation
dc.subjectSynthetic apertures
dc.subjectSystem stability
dc.subjectWide area networks
dc.subjectCascading analyse
dc.subjectHardwarein-the-loop simulations (HIL)
dc.subjectLinear state estimation
dc.subjectOperating condition
dc.subjectOperational challenges
dc.subjectPerformance indices
dc.subjectPower
dc.subjectReal- time
dc.subjectTime transient
dc.subjectTransient instability
dc.subjectPhasor measurement units
dc.titleReal-Time Transient Instability Identification in Power Systems using a PMU-Based EMS Systemen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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