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Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller

机译:混合侵入性杂草优化和模式搜索调整的2-DOF PID控制器自动控制发电

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A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system.
机译:本文提出一种混合入侵杂草优化和模式搜索(hIWO-PS)技术,以设计用于互联电力系统自动发电控制(AGC)的2自由度比例积分-微分(2-DOF-PID)控制器。首先,在互联的两区域热电系统中对提出的方法进行了测试,并且通过将结果与最近发布的方法(如常规的齐格勒·尼科尔斯(ZN),差异进化(DE),细菌觅食)进行比较,确立了该方法的优势。优化算法(BFOA),遗传算法(GA),粒子群优化(PSO),混合BFOA-PSO,混合PSO-PS和基于非主导短路GA-II(NSGA-II)的控制器,用于相同的互联电源系统。此外,通过更改系统参数,运行负载条件,位置以及干扰大小来执行敏感性研究,以证明所提出方法的鲁棒性。此外,该方法还适用于具有适当发电率约束(GRC)的三区域水热互连系统。通过介绍自适应神经模糊推理系统(ANFIS),混合hBFOA-PSO以及基于混合hPSO-PS的相同系统控制器的比较结果,证明了所提出方法的优越性。

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