| 摘 要: 针对现有电压暂降治理方案聚焦特定场景、难以适配多类型用户个性化需求定制需求的问题,提出一种基于多目标粒子群(Multi-Objective Particle Swarm Optimization, MOPSO)和逼近理想解排序法(Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS)的电压暂降治理设备优化配置方法。从成本、净现值、治理效果三方面构建多目标优化配置模型,采用MOPSO算法求解模型得到帕累托最优解集,结合主客观组合赋权的TOPSIS法筛选出满足用户需求的最优解；最后以某食品制造企业为案例开展分析,与典型配置方案对比,增加了搜索范围,可实现技术与经济性的均衡,更适配用户个性化需求。与其他优化算法对比,具有良好的收敛速度。 |
| 关键词: 电压暂降 多目标粒子群 逼近理想解排序法 优化配置 组合赋权 |
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中图分类号: TP23 TM714.2
文献标识码:
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| 基金项目: 智能电网重大专项(2030)资助(2024ZD0800603);国家重点研发计划(2022YFE0205300) |
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| Optimal Configuration Method of Voltage Sag Mitigation Equipment Based on MOPSO-TOPSIS |
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HE Jinyu1, ZHANG Song2, CAI Yongxiang1, CHEN Xiangping2, ZHANG Mei1
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1.College of Electrical Engineering, Guizhou University;2.Institute of Electric Power Science, Guizhou Power Grid Co, Ltd
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| Abstract: Aiming at the problem that existing voltage sag mitigation schemes focus on specific scenarios and are difficult to adapt to the customized personalized demands of various types of users, an optimal configuration method for voltage sag mitigation equipment based on Multi-Objective Particle Swarm Optimization (MOPSO) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed. A multi-objective optimal configuration model is established from three aspects: cost, net present value and mitigation effect. The MOPSO algorithm is adopted to solve the model and obtain the Pareto optimal solution set, and the TOPSIS method combined with subjective and objective combination weighting is used to screen out the optimal solution meeting users" demands. Finally, a case study is carried out on a food manufacturing enterprise. Compared with typical configuration schemes, the proposed method expands the search range, achieves a balance between technical performance and economic efficiency, and is more adaptable to users" personalized demands. In comparison with other optimization algorithms, it exhibits a favorable convergence rate. |
| Keywords: Voltage sag multi-objective particle swarm optimization technique for order preference by similarity to an ideal solution optimal allocation combination weighting |
