基于细菌觅食优化的多区域互联电力系统自动发电控制PI控制器设计
Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System using Bacterial Foraging Optimization
AI总结：
本文基于热电、风电和水电互联电力系统，利用细菌觅食优化算法优化PI控制器参数，减少频率偏差，对比三种优化方法的性能，证明BFO在抑制超调和欠调方面效果更优。
摘要：
本文设计了用于多区域互联电力系统自动发电控制的PI控制器，采用细菌觅食优化（BFO）和粒子群优化（PSO）等计算智能技术优化控制器增益（Kp和Ki）以及频率偏置（Bi）和速度调节参数（Ri）。还应用了基于梯度下降（GD）的传统方法优化参数。通过比较三种方法的频率响应，发现BFO技术显著降低了超调和欠调，尽管BFO计算时因大量数学方程导致 settling time 略有增加。所有方法均在MATLAB/SIMULINK环境中实现系统设计和参数调优。
英文摘要：
The system comprises of three interconnected power system networks based on thermal, wind and hydro power generation. The load variation in any one of the network results in frequency deviation in all the connected systems.The PI controllers have been connected separately with each system for the frequency control and the gains (Kp and Ki) of all the controllers have been optimized along with frequency bias (Bi) and speed regulation parameter (Ri). The computationally intelligent techniques like bacterial foraging optimization (BFO) and particle swarm optimization (PSO) have been applied for the tuning of controller gains along with variable parameters Bi and Ri. The gradient descent (GD) based conventional method has also been applied for optimizing the parameters Kp, Ki,Bi and Ri.The frequency responses are obtained with all the methods. The performance index chosen is the integral square error (ISE). The settling time, peak overshoot and peak undershoot of all the frequency responses on applying three optimization techniques have been compared. It has been observed that the peak overshoot and peak undershoot significantly reduce with BFO technique followed by the PSO and GD techniques. While obtaining such optimum response the settling time is increased marginally with bacterial foraging technique due to large number of mathematical equations used for the computation in BFO. The comparison of frequency response using three techniques show the superiority of BFO over the PSO and GD techniques. The designing of the system and tuning of the parameters with three techniques has been done in MATLAB/SIMULINK environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
