Artificial-intelligence method for the derivation of generic aggregated dynamic equivalent models

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Kontis, Eleftherios O. and Papadopoulos, Theofilos A. and Syed, Mazheruddin H. and Guillo-Sansano, Efren and Burt, Graeme M. and Papagiannis, Grigoris K. (2019) Artificial-intelligence method for the derivation of generic aggregated dynamic equivalent models. IEEE Transactions on Power Systems, 34 (4). pp. 2947-2956. ISSN 0885-8950 (https://doi.org/10.1109/TPWRS.2019.2894185)

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Abstract

Aggregated equivalent models for the dynamic analysis of active distribution networks (ADNs) can be efficiently developed using dynamic responses recorded through field measurements. However, equivalent model parameters are highly affected from the time-varying composition of power system loads and the stochastic behavior of distributed generators. Thus, equivalent models, developed through in situ measurements, are valid only for the operating conditions from which they have been derived. To overcome this issue, in this paper, a new method is proposed for the derivation of generic aggregated dynamic equivalent models, i.e., for equivalent models that can be used for the dynamic analysis of a wide range of network conditions. The method incorporates clustering and artificial neural network techniques to derive robust sets of parameters for a variable-order dynamic equivalent model. The effectiveness of the proposed method is evaluated using measurements recorded on a laboratory-scale ADN, while its performance is compared with a conventional technique. The corresponding results reveal the applicability of the proposed approach for the analysis and simulation of a wide range of distinct network conditions.

ORCID iDs

Kontis, Eleftherios O., Papadopoulos, Theofilos A., Syed, Mazheruddin H. ORCID logoORCID: https://orcid.org/0000-0003-3147-0817, Guillo-Sansano, Efren ORCID logoORCID: https://orcid.org/0000-0002-2773-4157, Burt, Graeme M. ORCID logoORCID: https://orcid.org/0000-0002-0315-5919 and Papagiannis, Grigoris K.;
  • Item type:Article
    ID code:66583
    Dates:
    Date
    Event
    31 July 2019
    Published
    21 January 2019
    Published Online
    9 January 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:16 Jan 2019 10:04
    Last modified:28 Nov 2024 01:17
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/66583
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