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Wyszukujesz frazę "Pareto optimal" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration
Autorzy:
Tang, Minan
Zhang, Kaiyue
Wang, Qianqian
Cheng, Haipeng
Yang, Shangmei
Du, Hanxiao
Powiązania:
https://bibliotekanauki.pl/articles/1841286.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Chebyshev chaotic mapping
distributed generation
distribution network reconfiguration
fuzzy decision method
Pareto optimal
pmdMOFOA
population Manhattan distance
Opis:
In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 307-323
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improved Differential Evolution Algorithm to solve multi-objective of optimal power flow problem
Autorzy:
Al-Kaabi, Murtadha
Hasheme, Jaleel Al
Al-Bahrani, Layth
Powiązania:
https://bibliotekanauki.pl/articles/2135728.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Multi-objective Improved Differential Evolution Algorithm
MOIDEA
optimal power flow
OPF
set of Pareto front solutions
multi-objective function problems
fuel costs considering emissions
fuel costs considering real power losses
fuel costs considering voltage deviation
Opis:
This article presents a new efficient optimization technique namely the Multi- Objective Improved Differential Evolution Algorithm (MOIDEA) to solve the multiobjective optimal power flow problem in power systems. The main features of the Differential Evolution (DE) algorithm are simple, easy, and efficient, but sometimes, it is prone to stagnation in the local optima. This paper has proposed many improvements, in the exploration and exploitation processes, to enhance the performance of DE for solving optimal power flow (OPF) problems. The main contributions of the DE algorithm are i) the crossover rate will be changing randomly and continuously for each iteration, ii) all probabilities that have been ignored in the crossover process have been taken, and iii) in selection operation, the mathematical calculations of the mutation process have been taken. Four conflicting objective functions simultaneously have been applied to select the Pareto optimal front for the multi-objective OPF. Fuzzy set theory has been used to extract the best compromise solution. These objective functions that have been considered for setting control variables of the power system are total fuel cost (TFC), total emission (TE), real power losses (RPL), and voltage profile (VP) improvement. The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software. Finally, to demonstrate the effectiveness and capability of the MOIDEA, the results obtained by this method will be compared with other recent methods.
Źródło:
Archives of Electrical Engineering; 2022, 71, 3; 641--657
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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