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    Improvement Of Micro-Grid Islanding Detection Using Smart Methods Based On Multi-Resolution Analysis And Data Whitening 

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    CIRED 2018 Ljubljana WS - 0576 - 21057.pdf (456.3Kb)
    Paper number
    0576
    Conference name
    CIRED 2018 Ljubljana Workshop
    Conference date
    7 - 8 June 2018
    Conference location
    Ljubljana, Slovenia
    Peer-reviewed
    Yes
    Metadata
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    Authors
    karimipoor, negar, Charmahal & Bakhtiari Distribution company, Islamic Republic Of Iran
    asgari, saleh, Niroo research Institute of Iran, Islamic Republic Of Iran
    Abstract
    These Islanding of Distributed Generations (DGs) in Micro-grids cause serious problems in operation and management of network. Therefore, proper detection of this event is of great importance. This paper presents a proper, fast and accurate method for detecting Islanding mode in unbalanced micro-grids. The proposed method uses multi-resolution analysis for pre-processing of measured data and using whitening for extracting of proper features. As a result, the accuracy of detection in micro-grid relation to measured noise and calculation window will increase. First, the voltage, current and frequency of DG’s terminal is measured and sampled. This data is pre-processed by multi-resolution analysis of relays and then main features are extracted by whitening. Then, the featured are considered by categorizing part whether these features indicate Islanding mode or not. In order to select the best method the classification methods like decision tree, neural network and support vector machine are compared. The proposed method is tested on an unbalanced micro-grid with DGs, wind and photovoltaic sources. Simulated results verify the accuracy of the method.
    Publisher
    AIM
    Date
    2018-06-07
    Published in
    • CIRED 2018 Ljubljana Workshop on Microgrids and Local Energy Communities
    Permanent link to this record
    https://www.cired-repository.org/handle/20.500.12455/1187
    http://dx.doi.org/10.34890/286
    ISSN
    2032-9628
    ISBN
    978-2-9602415-1-8

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