Artificial-Intelligence Method for the Derivation of Generic Aggregated Dynamic Equivalent Models
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...
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| Veröffentlicht in: | IEEE transactions on power systems 2019-07, Vol.34 (4), p.2947-2956 |
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| container_title | IEEE transactions on power systems |
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| creator | Kontis, Eleftherios O. Papadopoulos, Theofilos A. Syed, Mazheruddin H. Guillo-Sansano, Efren Burt, Graeme M. Papagiannis, Grigoris K. |
| description | 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. |
| doi_str_mv | 10.1109/TPWRS.2019.2894185 |
| format | Article |
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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. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-10d6db486cd0266b915a9963815217104e04bf324bd4fdc8ed7ffc6951a95e883</citedby><cites>FETCH-LOGICAL-c339t-10d6db486cd0266b915a9963815217104e04bf324bd4fdc8ed7ffc6951a95e883</cites><orcidid>0000-0003-3147-0817 ; 0000-0002-9470-8573 ; 0000-0002-2773-4157 ; 0000-0001-7595-9038 ; 0000-0002-0315-5919 ; 0000-0001-6384-1964</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8620372$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8620372$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kontis, Eleftherios O.</creatorcontrib><creatorcontrib>Papadopoulos, Theofilos A.</creatorcontrib><creatorcontrib>Syed, Mazheruddin H.</creatorcontrib><creatorcontrib>Guillo-Sansano, Efren</creatorcontrib><creatorcontrib>Burt, Graeme M.</creatorcontrib><creatorcontrib>Papagiannis, Grigoris K.</creatorcontrib><title>Artificial-Intelligence Method for the Derivation of Generic Aggregated Dynamic Equivalent Models</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>Aggregated equivalent models for the dynamic analysis of active distribution networks (ADNs) can be efficiently developed using dynamic responses recorded through field measurements. 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| subjects | Analytical models Artificial intelligence Artificial neural networks black-box modeling Clustering Computational modeling Computer simulation Derivation Distributed generation Dynamic equivalents dynamic modeling Equivalence Generators In situ measurement Load modeling Mathematical models measurement-based approach Order parameters Parameter robustness Power system dynamics Reactive power |
| title | Artificial-Intelligence Method for the Derivation of Generic Aggregated Dynamic Equivalent Models |
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