| 引用本文: | 杨培宏,刘连光,刘春明,冯士伟,郑许朋.基于粒子群优化算法的电网GIC-Q多目标优化策略[J].电力自动化设备,2017,37(3): |
| YANG Peihong,LIU Lianguang,LIU Chunming,FENG Shiwei,ZHENG Xupeng.Multi-objective optimization strategy based on PSO algorithm for GIC-Q of power grid[J].Electric Power Automation Equipment,2017,37(3): |
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| 摘要: |
| 地磁感应电流(GIC)流经变压器绕组会产生直流偏磁现象，造成变压器无功损耗增加，破坏电网无功平衡，影响电网安全稳定运行。为了有效地抑制GIC对电网的不良影响，以无功补偿设备成本和电压偏移量最小为目标，提出一种基于粒子群优化算法的多目标无功优化策略，保证地磁场扰动下电网无功平衡。所提策略利用小生境共享机制不断更新粒子位置，并依据拥挤距离排序对Pareto最优解进行存档，保持解的多样性和均匀性；引入混沌变异避免陷入局部最优解，同时提高全局搜索能力。GIC标准算例的仿真结果验证了所提策略的准确性和有效性。 |
| 关键词: 地磁场扰动 地磁感应电流 无功优化 电压偏移 多目标优化 小生境 粒子群优化算法 混沌变异 Pareto最优 |
| DOI：10.16081/j.issn.1006-6047.2017.03.016 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(51577060)；国家高技术研究发展计划(863计划)资助项目(2012AA121005) |
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| Multi-objective optimization strategy based on PSO algorithm for GIC-Q of power grid |
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YANG Peihong1,2, LIU Lianguang1, LIU Chunming1, FENG Shiwei2, ZHENG Xupeng2
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1.State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2.School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
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| Abstract: |
| GIC(Geomagnetically Induced Current) may cause the DC magnetic bias when it flows through the transformer windings, resulting in the increase of transformer reactive-power loss, the imbalance of grid reactive-power and the impact on safe and stable grid operation. A multi-objective reactive-power optimization strategy based on the particle swarm optimization algorithm is proposed to restrain the influence of GIC and ensure the reactive-power balance of power grid under geomagnetic disturbance, which takes the minimum cost of reactive-power compensation equipments and the minimum voltage deviation as its objectives. The proposed strategy applies the niche sharing mechanism to update the particle locations and archives the Pareto optimal solution set according to the crowding distance for the diversity and uniformity of solutions. Chaotic mutation is introduced to avoid the locally optimal solutions and to improve the global searching ability. Simulative results of GIC-benchmark verify the accuracy and effectiveness of the proposed strategy. |
| Key words: geomagnetic disturbance geomagnetically induced current reactive power optimization voltage deviation multi-objective optimization niche particle swarm optimization algorithm chaotic mutation Pareto optimality |
