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    A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms

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    CP165_KAUST_ML_ForcedOscillations.pdf
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    1.249Mb
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Ayachi, Mohammed Ilies
    Vanfretti, Luigi
    Ahmed, Shehab
    KAUST Department
    Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/662642
    
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    Abstract
    Today’s evolving power system contains an increasing amount of power electronic interfaced energy sources and loads that require a paradigm shift in utility operations. Subsynchronous oscillations at frequencies around 13-15 Hz, for instance, have been reported by utilities due to wind farm controller interactions with the grid. Dynamics at such frequencies are unobservable by most SCADA tools due to low sampling frequencies and lack of synchronization. Real-time or off-line frequency domain analysis of phasor measurement unit (PMU) data has become a valuable method to identify such phenomena, at the expense of costly power system data and communication infrastructure. This article proposes an alternative machine learning (ML) based application for sub-synchronous oscillation detection in wind farm applications. The application is targeted for real-time implementation at the ‘edge’, resulting in significant savings in terms of data and communication requirements. Validation is performed using data from a North American wind farm operator.
    Citation
    Mohammed-Ilies Ayachi, Luigi Vanfretti, Shehab Ahmed, “A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms”, Saudi Smart Grid Conference 2019, 2019.
    Publisher
    SASG
    Conference/Event name
    Saudi Smart Grid Conference 2019
    Additional Links
    https://ecse.rpi.edu/~vanfrl/documents/publications/conference/2019/CP165_KAUST_ML_ForcedOscillations.pdf
    https://sasg2019.com/en/
    Collections
    Conference Papers; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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