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DAMPING POWER SYSTEMS OSCILLATIONS USING FACTS COMBINATIONS

机译:事实结合的阻尼电力系统振荡

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摘要

The damping of electromechanical modes of oscillation in power systems, can be made by static VAR compensator, SVC, and / or Power system stabilizer, PSS. This paper presents a combinations of using static VAR compensator, SVC, controlled by a nonlinear dynamic controller based adaptive neural network with power system stabilizer, controlled by variable structure fuzzy logic control. The proposed system is used for both damping low frequency modes of oscillation of the power systems, and enhancing the system dynamic performance at post-fualt conditions. The proposed variable structure fuzzy logic controller implement the speed deviation, it's rate of change, internal machine angle deviation, and it's rate of change signals. The nonlinear dynamic controller based adaptive neural network implement the speed deviation signal. The proposed scheme is validated using sample single machine connected to infinite bus power system through a double transmission line circuit. The studied system is modeled and represented by a nonlinear differential equations,. The matlab softwar is used for solving the system equations. The time simulation indicates the superiority of using both SVC, and PSS over using any of them alone in the studied power system. The results shows that using the FACTS combinations provide very fast damping, with less overshoot, and reduce the amount of reactive compensation required for the static VAR compensator, and reduce the exciter gain.
机译:电力系统中机电振荡模式的阻尼可以通过静态无功补偿器SVC和/或电力系统稳定器PSS来实现。本文提出了一种组合使用静态无功补偿器(SVC),该补偿器由基于非线性动态控制器的自适应神经网络控制,该神经网络与电力系统稳定器一起由可变结构模糊逻辑控制来控制。所提出的系统既可用于阻尼电力系统的低频振荡模式,又可用于增强后极限条件下的系统动态性能。提出的变结构模糊逻辑控制器实现了速度偏差,变化率,内部机器角度偏差以及变化率信号。基于非线性动态控制器的自适应神经网络实现速度偏差信号。所提出的方案通过使用通过双传输线电路连接到无限母线电力系统的示例单机进行了验证。对所研究的系统进行建模并由非线性微分方程表示。 Matlab软件软件用于求解系统方程。时间仿真表明,在所研究的电力系统中,同时使用SVC和PSS优于单独使用它们。结果表明,使用FACTS组合可提供非常快的阻尼,并具有较小的过冲,并减少了静态VAR补偿器所需的无功补偿量,并降低了励磁机增益。

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