基于粒子群优化惯性权重和收缩因子混合算法的最优潮流研究——以150kv Sulbagsel热电发电系统为例

None Muhammad Natsir Rahman, None Andi Muhammad Ilyas
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摘要

越来越多的电力使用鼓励电学科学家创建或建立数学模型来提高电力质量。本研究使用IEEE 26总线系统数据来验证该方法,并使用来自南苏拉威西(Sulbagsel)系统的150 kV热发电机数据作为案例研究。使用的方法是PSOHIC。对150 kV Sulbagsel系统数据的仿真结果表明,PSOHIC在第8次迭代时收敛速度更快。标准PSO在第25次迭代时收敛。IPSO算法在第20次迭代时收敛。同时,MIPSO算法在第12次迭代时收敛。潮流仿真结果表明,采用PSOHIC后,16.48 MW的功率损耗比现有系统19.10 MW的功率损耗小,即功率损耗降低了0.1613%。PSOHIC的生产成本为281,860.91印尼盾/小时,比MIPSO、IPSO和PSO便宜。
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Optimal Power Flow Using Particle Swarm Optimization Hybrid Inertia Weight and Constriction Factor Algorithm (PSOHIC) Case Study: Thermal Generator System of 150 kV Sulbagsel
The increasing use of electricity encourages electricity scientists to create or build mathematical models to improve the quality of electric power. This study uses IEEE 26 bus system data to validate the method and 150 kV thermal generator data from the South Sulawesi (Sulbagsel) system as a case study. The method used is PSOHIC. The simulation results for the 150 kV Sulbagsel system data show that PSOHIC converges more quickly, namely in the 8th iteration. The standard PSO converges at the 25th iteration. The IPSO algorithm converges at the 20th iteration. At the same time, the MIPSO algorithm converges at the 12th iteration. The power flow simulation results show that with PSOHIC, the power loss of 16.48 MW is smaller than the current system of 19.10 MW, that is, the power loss is reduced by 0.1613%. The production cost with PSOHIC is IDR 281,860.91/hour, cheaper than MIPSO, IPSO and PSO.
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