| 引用本文: | 蔡博,黄少锋.基于多目标粒子群算法的高维多目标无功优化[J].电力系统保护与控制,2017,45(15):77-84. |
| CAI Bo,HUANG Shaofeng.Multi-objective reactive power optimization based on the multi-objective particle swarm optimization algorithm[J].Power System Protection and Control,2017,45(15):77-84 |
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| 摘要: |
| 提出一种高维多目标电力系统无功优化模型。相比于传统的电力系统无功优化模型，该模型能够在无功优化中同时兼顾系统的有功损耗、电压水平、静态电压稳定性以及供电能力。针对已有的求解多目标无功优化模型的算法应用于求解所提模型时存在的局限性，进一步引入一种基于帕雷托熵的高维多目标粒子群优化算法并加以改进，使得该算法能够有效求解高维多目标优化问题。最后，利用IEEE-39节点系统验证了所提模型和求解算法的正确性和有效性。仿真结果表明，在传统的多目标无功优化模型中引入系统供电能力，能够在不恶化其他目标函数优化效果的情况下，使系统的供电能力得到提高。 |
| 关键词: 无功优化 高维多目标优化 供电能力 粒子群算法 |
| DOI：10.7667/PSPC170542 |
| 分类号: |
| 基金项目:国家自然科学基金项目(51677069) |
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| Multi-objective reactive power optimization based on the multi-objective particle swarm optimization algorithm |
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CAI Bo,HUANG Shaofeng
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(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source, North China Electric Power University, Beijing 102206, China)
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| Abstract: |
| The paper proposes a high-dimensional multi-objective reactive power optimization model of power system. Compared with the traditional power system reactive power optimization model, the proposed model can balance the active loss, voltage level, static voltage stability and power supply capacity in reactive optimization. Owing to the limitations of the existing algorithm for solving the multi-objective reactive power optimization model, a new high-dimensional multi-objective particle swarm optimization algorithm based on Pareto entropy is introduced and improved further in this paper to effectively solve the high-dimensional multi-objective optimization problem. Finally, the correctness and validity of the proposed model and the algorithm are verified by IEEE-39 node system. In addition, the simulation results show that the introduction of power supply capacity into traditional multi-objective reactive power optimization model can improve system’s power supply capacity with no deterioration in optimization of other objective functions. This work is supported by National Natural Science Foundation of China (No. 51677069). |
| Key words: reactive power optimization high-dimensional multi-objective optimization power supply capacity particle swarm optimization algorithm |
