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首页> 外文期刊>Applied Soft Computing >Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems
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Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems

机译：基于多目标维思萤火虫算法优化电力系统功率损耗，发射和成本的应用

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摘要

In this paper, a new multi-objective dimension-based firefly algorithm (MODFA) is proposed for solving the constrained multi-objective optimal power flow (MOOPF) problem with multiple and contradictory objectives in power systems. In our suggested MODFA algorithm, a constrained Pareto-dominant approach (CPA) is offered for guaranteeing zero violations of various inequality constraints on state variables in the constrained MOOPF problem. In addition to that, the CPA and the dimension-based technology (DT) are federated together to update the information of the non-dominant firefly to speed up the convergence of multiple target search. Crowding distance and non-dominated sorting based on the violation of constraints are also regarded as measures to sustain well-distributed Pareto optimal solution (POS) set. Furthermore, a fuzzy affiliation is utilized to pick the best compromise solution (BCS) from the obtained POS. The IEEE30-bus system, the IEEE57-bus system, and the IEEE118-bus system with nine cases are implemented to validate the performance of the proposed MODFA by considering the active power losses, the emission, and the total fuel cost. The numerous simulation results optimized by the MODFA, which are compared with frequently-used NSGA-III, NSGA-II, and MOPSO algorithm, show the capability of the MODFA for obtaining POS with uniform distribution and high quality. Additionally, three performance metrics are considered to evaluate approximation, distribution, and diversity of POS found by MODFA. (C) 2018 Elsevier B.V. All rights reserved.
机译：在本文中，提出了一种新的基于多目标维思算法（MODFA），用于解决电力系统中的多个和矛盾目标的受约束的多目标最佳功率流（MOOPF）问题。在我们建议的ModFA算法中，提供了一个受约束的Pareto - 主导方法（CPA），用于保证受约束MoOPF问题中的状态变量对状态变量的各种不等式约束的零违反。除此之外，CPA和基于维度的技术（DT）将联合在一起，以更新非主导萤火虫的信息，以加快多个目标搜索的融合。基于违规的约束的挤出距离和非主导排序也被视为维持分布式Pareto最佳解决方案（POS）集的措施。此外，利用模糊的隶属度来从所获得的POS中挑选最佳的折衷溶液（BCS）。 IEEE30-Bus系统，IEEE57总线系统和具有九种情况的IEEE118总线系统的实施方式通过考虑有效功率损耗，发射和总燃料成本来验证所提出的MODFA的性能。由MODFA优化的许多仿真结果，与常用的NSGA-III，NSGA-II和MOPSO算法进行比较，显示了MODFA的能力，以获得具有均匀分布和高质量的POS。此外，三种性能指标被认为是评估MODFA发现的POS的近似，分布和多样性。 （c）2018 Elsevier B.v.保留所有权利。

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