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    Hybrid Islanding Detection in Microgrid with Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network

    Access Status
    Fulltext not available
    Authors
    Kermany, S.
    Joorabian, M.
    Deilami, Sara
    Masoum, Mohammad Sherkat
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Kermany, S. and Joorabian, M. and Deilami, S. and Masoum, M.S. 2017. Hybrid Islanding Detection in Microgrid with Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network. IEEE Transactions on Power Systems. 32 (4): pp. 2640-2651.
    Source Title
    IEEE Transactions on Power Systems
    DOI
    10.1109/TPWRS.2016.2617344
    ISSN
    0885-8950
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55483
    Collection
    • Curtin Research Publications
    Abstract

    © 1969-2012 IEEE. This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If {\text{PoI}}-{{\rm{ANN}}} is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it ({\text{PoI}}-{{\rm{FUZZY}}}) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to {\text{PoI}}-{{\rm{FUZZY}}}. Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method.

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