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Optimal proportional integral derivative controller for automatic voltage regulator system using particle swarm optimization algorithm

机译:基于粒子群算法的自动调压系统最优比例积分微分控制器

摘要

Voltage control is very important in electrical power system to ensure safe operating condition for equipment. The purpose of this project is to investigate the application of Particle Swarm Optimization (PSO) algorithm in fine-tuning the Proportional, Integral and Derivative (PID) parameter of PID controller for Power System’s Automatic Voltage Regulator (AVR). AVR system is employed widely in exciter control system, mainly used in areas where the supply voltage is not stable and fluctuation of load occurs. The generator excitation system maintains generator terminal voltage and controls the reactive power flow using an AVR. A practical high order and linearized Power System AVR is modelled with four main components namely Amplifier, Exciter, Generator and Sensor. The proposed PSO algorithm had superior computational efficiency, stable convergence characteristic, and easy implementation. Fast tuning of optimum PID controller parameters yields high quality solution. To estimate the performance of optimal PID controller, a fitness function that use frequency-domain and time-domain were defined. A conventional technique, Ziegler-Nichols (ZN) tuning technique was used as the comparison for proposed algorithm. Simulation result shows that PID tuned by ZN technique improved the transient response of AVR but due to high order of AVR system, it is difficult to achieve optimal PID of AVR using ZN technique which is achievable using PSO algorithm.
机译:电压控制在电力系统中对于确保设备的安全运行状况非常重要。该项目的目的是研究粒子群优化(PSO)算法在微调电力系统自动电压调节器(AVR)的PID控制器的比例,积分和微分(PID)参数中的应用。 AVR系统广泛应用于励磁机控制系统中,主要用于电源电压不稳定,负载波动较大的区域。发电机励磁系统维持发电机端电压并使用AVR控制无功功率流。实用的高阶线性化电源系统AVR具有四个主要组件,即放大器,激励器,发电机和传感器。提出的PSO算法具有较高的计算效率,稳定的收敛特性,易于实现。快速调整最佳PID控制器参数可产生高质量的解决方案。为了估计最佳PID控制器的性能,定义了使用频域和时域的适应度函数。传统技术,Ziegler-Nichols(ZN)调整技术被用作所提出算法的比较。仿真结果表明,采用ZN技术调整的PID改善了AVR的瞬态响应,但由于AVR系统的高阶性,使用ZN技术难以获得AVR的最佳PID,而PN算法可以实现该PID。

著录项

  • 作者

    Wan Hassan Wan Zakaria;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

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