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ANFIS and Fuzzy Tuning of PID Controller for STATCOM to Enhance Power Quality in Multi-machine System Under Large Disturbance

机译:STATCOM PID控制器的ANFIS和模糊调谐,在大扰动下提高多机系统电能质量

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STATCOM is one of the FACTS devices that are used in power systems. The algorithms used to control the STATCOM often use PID controller. However, there are a lot of elements in the network and have complex configurations and their dynamic model is highly non-linear, and convention PID controller are not robust for their stability control. In this paper, we propose the intelligent controllers for STATCOM based on dynamic model of the system and two control schemes have been developed: (ⅰ) Fuzzy-PID self-tuning controller (Hybrid F-PID); (ⅱ) Adaptive neuro-fuzzy inference system - PID (ANFIS-PID) controller. The operating performance of the studied system is using the popular benchmark three-machine nine-bus system. The two-axis four-order model of synchronous generator (SG) is used. Time-domain scheme based on a nonlinear system model subject to a three-phase short-circuit fault at the load connected bus is utilized to examine the effectiveness of the proposed control schemes. It can be concluded from the simulation results that ANFIS has provide the best results for controlling STATCOM to enhance power quality in power system as compared to the conventional control strategies under large disturbance.
机译:Statcom是电力系统中使用的事实设备之一。用于控制Statcom的算法通常使用PID控制器。然而,网络中有很多元素,并且具有复杂的配置,并且它们的动态模型是高度非线性的,并且常规PID控制器对其稳定性控制并不稳健。在本文中,我们提出了基于系统动态模型的Statcom智能控制器,并开发了两个控制方案:(Ⅰ)模糊PID自调整控制器(混合F-PID); (Ⅱ)自适应神经模糊推理系统 - PID(ANFIS-PID)控制器。学习系统的操作性能正在采用流行的基准三机九母线系统。使用同步发电机(SG)的双轴四阶模型。利用基于负载连接总线的三相短路故障的非线性系统模型的时域方案用于检查所提出的控制方案的有效性。可以从模拟结果中得出结论,即ANFIS提供了控制STATCOM的最佳效果,以提高电力系统的电力质量,与在大扰动下的传统控制策略相比。

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