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

TY - JOUR

T1 - Artificial-intelligence method for the derivation of generic aggregated dynamic equivalent models

AU - Kontis, Eleftherios O.

AU - Papadopoulos, Theofilos A.

AU - Syed, Mazheruddin H.

AU - Guillo-Sansano, Efren

AU - Burt, Graeme M.

AU - Papagiannis, Grigoris K.

N1 - © 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.

PY - 2019/1/21

Y1 - 2019/1/21

N2 - 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 which 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.

AB - 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 which 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.

KW - artificial neural networks

KW - black-box modeling

KW - clustering

KW - dynamic modeling

KW - measurement-based approach

UR - https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59

U2 - 10.1109/TPWRS.2019.2894185

DO - 10.1109/TPWRS.2019.2894185

M3 - Article

SP - 1

EP - 9

JO - IEEE Transactions on Power Systems

T2 - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

ER -