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

Identyfikatory

DOI

10.24425/aee.2021.136986

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Chebyshev chaotic mapping; distributed generation; distribution network reconfiguration; fuzzy decision method; Pareto optimal; pmdMOFOA; population Manhattan distance

PL

Chaotyczne mapowanie Czebyszewa; generacja rozproszona; rekonfiguracja sieci dystrybucyjnej; rozmyta metoda decyzji; Optymalne Pareto; populacja Odległość Manhattanu

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2021
• Tom: Vol. 70, nr 2
• Strony: 307--323
• Opis fizyczny: Bibliogr. 25 poz., rys., wz., tab.

Twórcy

autor

Tang Minan

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Zhang Kaiyue

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Wang Qianqian

• College of Electrical and Information Engineering, Lanzhou University of Technology Lanzhou, China

autor

Cheng Haipeng

• CRRC Qingdao Sifang Co., Ltd. Qingdao, China

autor

Yang Shangmei

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

autor

Du Hanxiao

• School of Automation and Electrical Engineering, Lanzhou Jiaotong University Lanzhou, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).