论文标题
通过组合基于分类的多目标进化算法和集成决策制定，多目标的最佳反应电源调度电力系统通过
Multi-objective Optimal Reactive Power Dispatch of Power Systems by Combining Classification Based Multi-objective Evolutionary Algorithm and Integrated Decision Making
论文作者
论文摘要
为了解决多目标最佳反应能力调度（MORPD）问题，本文提出了两步方法。首先，为了确保电力系统的经济和安全性，制定了旨在最大程度地降低电力损失和电压偏差的MORPD模型。然后提出了将决策整合到优化中的两步方法来解决模型。具体而言，第一步旨在通过使用名为分类的多目标优化（MOO）算法来寻求具有良好分布的Pareto最佳解决方案（POSS），并基于帕累托支配性的多目标进化算法（CPSMOEA）。此外，生成参考帕累托（Pareto）最佳前端，以验证使用CPSMOEA获得的帕累托前沿；在第二步中，通过将模糊c均值算法（FCM）与灰色关系投影方法（GRP）相结合的综合决策旨在提取最佳的折衷解决方案，以反映从POSS中的决策者的偏好。基于IEEE 30 BUS和IEEE 118-BUS测试系统的测试结果，证明了所提出的方法不仅可以解决MORPD问题，而且还优于其他常用的MOO算法，包括多目标粒子群（MOPSO），包括prefercement-inspired Coopired coletolution Algorith and piceagrith and PICEARINTION（MOPSO）和第三次活（GDE3）。
For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (CPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using CPSMOEA; in the second step, integrated decision-making by combining fuzzy c-means algorithm (FCM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PICEAg) and the third evolution step of generalized differential evolution (GDE3).
