Ship micro-grid reconfiguration based on multiobjective optimization algorithm

SU Li; WANG Xihuai; XIAO Jianmei

doi:10.19693/j.issn.1673-3185.01534

Volume 15 Issue 3

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Chinese Journal of Ship Research > 2020 > 15(3): 169-176. > DOI: 10.19693/j.issn.1673-3185.01534 CSTR: 32390.14.j.issn.1673-3185.01534

SU Li, WANG Xihuai, XIAO Jianmei. Ship micro-grid reconfiguration based on multiobjective optimization algorithm[J]. Chinese Journal of Ship Research, 2020, 15(3): 169-176. DOI: 10.19693/j.issn.1673-3185.01534

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Ship micro-grid reconfiguration based on multiobjective optimization algorithm

Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

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Received Date: February 26, 2019
Available Online: May 07, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Objectives In order to solve the problem of poor convergence and distribution of the existing constrained multiobjective optimization algorithms in solving the ship micro-grid reconfiguration, a constrained multiobjective optimization method based on two-stage differential evolution (TSDE) algorithm is proposed.
Methods Firstly, in the first stage, the two-population hybrid method(i.e. self-adaptive penalty function method and feasibility rule)was used to deal with the constraints. Secondly, in the second stage, the two populations generated in the first stage were merged into a single population, and the feasibility rule was adopted to solve the constrained optimization problem. Finally, different elitist selection strategies and improved non-parametric mutation operators were adopted in different stages to further optimize the differential evolution algorithm.
Results The simulation results show that the minimum load loss obtained by TSDE algorithm under the fault 1 and the fault 2 is 185 and 940 A lower than that of chaotic migration and parameterless mutation differential evolution (CMPMDE) and environment pareto dominated selection differential evolution(EPDSDE), respectively. The minimum switching operands obtained by the TSDE algorithm are 1 time more than that of CMPMDE algorithm under the fault 1, and are the same as that of EPDSDE algorithm. Under the fault 2, the minimum switching operands of the proposed algorithm are 1 time less than those of CMPMDE algorithm and EPDSDE algorithm.
Conclusions The set of optimal non-inferior solutions obtained by TSDE algorithm is closer to the real Pareto frontier and distributes more evenly, so the method can ensure that the ship is operated safely and steadily when the reconfiguration time is satisfied.

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Ship micro-grid reconfiguration based on multiobjective optimization algorithm

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