Application of WOA Tuned Type-2 FLC for LFC of Two Area Power System With RFB and Solar Park Considering TCPS in Interline

控制理论(社会学) 电力系统 控制器(灌溉) 计算机科学 混合动力 自动频率控制 最大功率点跟踪 可再生能源 功率(物理) 工程类 电气工程 逆变器 电压 电信 控制(管理) 物理 生物 人工智能 量子力学 农学
作者
Ahmed Mohammed Attiya Soliman,Mostafa Bahaa Eldin,Mohammed Mehanna
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:10: 112007-112018 被引量:17
标识
DOI:10.1109/access.2022.3215530
摘要

In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).
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