基于人工智能控制器的多区域互联系统频率调节

R. Priyadarsini, Archana M. Nayak, Ajit Kumar Barisal
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摘要

在互联电力系统的任何一个区域,负荷的轻微脉冲变化都会引起所有区域的频率和功率波动。负荷频率控制(LFC)的主要目的是通过管理被控制区域内电网功率的变化来稳定电网的实际频率和期望输出功率(MW)。从本质上讲,LFC方案构成了一个适合于并网电力系统的控制系统。控制系统具有恢复本地频率的能力,在标准控制器的帮助下,负载移动后,可以将电源连接到初始设定点或非常接近它。在我的研究中,我使用了一种基于ai的控制器来动态分析三区互联火电发电系统的负荷频率控制。该思想利用PI、PID和模糊控制器等先进的控制方法,对三区互联水热发电系统进行控制。本文所构建的控制器参数是基于粒子群优化(PSO)技术,利用积分时间绝对误差(ITAE)作为目标函数来控制频率偏差。利用MATLAB2016b软件对控制器进行了性能仿真,并将所提出的基于模糊逻辑的解决方案与PI和PID在相同条件下进行了比较。通过对这些方法的比较,可以明显看出模糊控制器的性能优于其他两种方法。总结了仿真结果,并从峰值超调和稳定时间两方面对其性能进行了对比分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency regulation of multi area interconnected system by using artificial intelligence based controller
A slight impulsive load change in any zone of an interconnected power system will cause fluctuations in frequency and power in all zones. The main intention of load frequency control (LFC) is to stabilize the actual frequency and desired output power (MW) in the interlinked power system, by managing the variations of tie line power in controlled areas. Inherently, the LFC scheme constitutes a suitable control system for connected power systems. The control system has the ability to restore the local frequency and can connect the power to the initial setting point or very close to it after the load is moved by the help of standard controller. In my study, an AI-based controller has been used to analyze the load frequency control in a three-zone interconnected thermal hydro power generation system dynamically. The proposed idea makes use of advanced controlling methods using PI, PID and Fuzzy logic controllers for an interconnected hydrothermal heating power generation system in three areas system. The controller parameters which are made up here is based on the particle swarm optimization (PSO) technique, that uses an objective function called the integral time absolute error (ITAE) to control the deviation in frequency The performance simulation of the controller is done by using MATLAB2016b and by comparing the proposed fuzzy logic- based solution with PI and PID under the same conditions. By proper comparison among these methods, it is clearly noticeable that fuzzy logic controller performs better than other two approaches. The results of simulation are summarized and comparison analysis of the performance is done in terms of peak overshoot and settling time.
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