电力系统潮流优化平衡优化器的开发与应用。

Essam H Houssein, Mohamed H Hassan, Mohamed A Mahdy, Salah Kamel
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引用次数: 20

摘要

本文提出了一种改进的均衡优化器(eoo),用于解决全局优化和最优潮流(OPF)问题。提出的EEO算法包含了一种新的性能增强策略和lsamvy飞行机制。该算法解决了原有均衡优化器(EO)的不足,旨在为全局优化问题,特别是OPF问题提供更好的解决方案。通过将CEC'20测试套件的十个功能与其他算法(包括CMA-ES、IMODE、AGSK和LSHADE_cnEpSin等高性能算法)的结果进行比较,证实了所提出的EEO效率。并通过Wilcoxon秩和检验验证了这些结果的统计学显著性。然后,将所提出的平等就业机会应用于解决OPF问题。将OPF描述为一个目标冲突且同时受等式和不等式约束的非线性优化问题。针对不同的目标,在标准的IEEE 30总线测试系统上对该技术的性能进行了研究和评价。将提出的EEO算法得到的结果与原始EO算法和文献中提到的其他技术得到的结果进行了比较。仿真结果表明,该算法比已有的20种方法和结果以及原有的EO算法提供了更好的优化解。通过六个不同的案例证明了EEO的优越性,这些案例涉及最小化不同的目标:燃料成本、带有阀点负载效应的燃料成本、排放、总有功功率损耗、电压偏差和电压不稳定。对比结果表明,EEO算法可以为不同的OPF问题提供鲁棒性、高质量的可行解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Development and application of equilibrium optimizer for optimal power flow calculation of power system.

This paper proposes an enhanced version of Equilibrium Optimizer (EO) called (EEO) for solving global optimization and the optimal power flow (OPF) problems. The proposed EEO algorithm includes a new performance reinforcement strategy with the Lévy Flight mechanism. The algorithm addresses the shortcomings of the original Equilibrium Optimizer (EO) and aims to provide better solutions (than those provided by EO) to global optimization problems, especially OPF problems. The proposed EEO efficiency was confirmed by comparing its results on the ten functions of the CEC'20 test suite, to those of other algorithms, including high-performance algorithms, i.e., CMA-ES, IMODE, AGSK and LSHADE_cnEpSin. Moreover, the statistical significance of these results was validated by the Wilcoxon's rank-sum test. After that, the proposed EEO was applied to solve the the OPF problem. The OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus test system for different objectives. The obtained results of the proposed EEO algorithm is compared to the original EO algorithm and those obtained using other techniques mentioned in the literature. These Simulation results revealed that the proposed algorithm provides better optimized solutions than 20 published methods and results as well as the original EO algorithm. The EEO superiority was demonstrated through six different cases, that involved the minimization of different objectives: fuel cost, fuel cost with valve-point loading effect, emission, total active power losses, voltage deviation, and voltage instability. Also, the comparison results indicate that EEO algorithm can provide a robust, high-quality feasible solutions for different OPF problems.

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