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Titolo:
AN INTERIOR-POINT NONLINEAR-PROGRAMMING FOR OPTIMAL POWER-FLOW PROBLEMS WITH A NOVEL DATA STRUCTURE
Autore:
WEI H; SASAKI H; KUBOKAWA J; YOKOYAMA R;
Indirizzi:
HIROSHIMA UNIV,DEPT ELECT ENGN,1-4-1 KAGAMIYAMA HIGASHIHIROSHIMA 739 JAPAN TOKYO METROPOLITAN UNIV,DEPT ELECT ENGN TOKYO 159 JAPAN
Titolo Testata:
IEEE transactions on power systems
fascicolo: 3, volume: 13, anno: 1998,
pagine: 870 - 877
SICI:
0885-8950(1998)13:3<870:AINFOP>2.0.ZU;2-A
Fonte:
ISI
Lingua:
ENG
Soggetto:
SYSTEM OPTIMIZATION PROBLEMS; ALGORITHM;
Keywords:
OPTIMAL POWER FLOW; INTERIOR POINT NONLINEAR PROGRAMMING; PERTURBED KKT CONDITIONS; CENTERING DIRECTION; APPROXIMATE OFF;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
19
Recensione:
Indirizzi per estratti:
Citazione:
H. Wei et al., "AN INTERIOR-POINT NONLINEAR-PROGRAMMING FOR OPTIMAL POWER-FLOW PROBLEMS WITH A NOVEL DATA STRUCTURE", IEEE transactions on power systems, 13(3), 1998, pp. 870-877

Abstract

This paper presents a new interior point nonlinear programming algorithm for optimal power flow problems (OPF) based on the perturbed KKT conditions of the primal problem. Through the concept of the centering direction, we extend this algorithm to classical power flow (PF) and approximate OFF problems. For the latter, CPU time can be reduced substantially. To efficiently handle functional inequality constraints, a reduced correction equation is derived, the size of which depends on that of equality constraints. A novel data structure is proposed which has been realized by rearranging the correction equation, Compared withthe conventional data structure of Newton OFF, the number of fill-insof the proposed scheme is roughly halved and CPU time is reduced by about 15% for large scale systems. The proposed algorithm includes fourkinds of objective functions and two different data structures. Extensive numerical simulations on test systems that range in size from 14 to 1047 buses, have shown that the proposed method is very promising for large scale application due to its robustness and fast execution time.

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Documento generato il 10/07/20 alle ore 12:44:31