基于混合粒子群优化的多区域系统负荷频率控制

S. Meena, S. Chanana
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引用次数: 9

摘要

自动发电控制(AGC)的本质是在负荷需求变化引起电力系统波动时,通过控制发电功率,使系统频率和联络线交换保持在限定范围内。本文提出了几种在线智能控制技术,以了解多区域系统的负载频率控制。发展智能控制技术的主要原因是通过减少多区域系统的超调、欠调和稳定时间来改善电力系统的暂态响应。本文提出的方法是将细菌觅食算法(BFA)和粒子群算法(PSO)相结合,我们称之为混合粒子群算法(H-PSO)。在MATLAB/Simulink环境下进行了多区域电力系统的建模和控制器的整定。结果分析表明,与BFA和PSO相比,H-PSO效果更好。
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Load Frequency Control of multi area system using Hybrid Particle Swarm Optimization
The essential point of the Automatic Generation Control (AGC) is to keep up the system frequency and tie-line power interchange within the limits by controlling the electrical power generated, whenever the fluctuations in the power system occur due to change in load demand. This paper proposes few online smart control techniques to understand the Load Frequency Control (LFC) of multi area system. The main reason to develop smart control techniques to improve the transient response of the power system by reducing the overshoot, undershoot and settling time of the multi area system. The proposed method in this paper is combination of Bacterial Foraging Algorithm (BFA) and Particle Swarm Optimization (PSO) we call it as Hybrid-PSO (H-PSO). The modelling of multi area power system and tuning of the controller are carried out in MATLAB/Simulink environment. After the analysis of results it is observed that H-PSO is better as compare to BFA and PSO.
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