Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system

被引:6
|
作者
Micev, Mihailo [1 ]
Calasan, Martin [1 ]
Radulovic, Milovan [1 ]
机构
[1] Dzordza Vasingtona Bb, Fac Elect Engn, Podgorica 81000, Montenegro
关键词
Automatic voltage regulation system; Disturbance rejection; Optimal tuning; PI controller; PID CONTROLLER; ALGORITHM; DESIGN;
D O I
10.1016/j.heliyon.2023.e18707
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper deals with the optimal tuning of the controller for the real automatic voltage regulation (AVR) system of the synchronous generator (SG). For this purpose, firstly, a novel proportional-integral controller with two degrees of freedom and anti-windup protection for application in the AVR system is proposed. Secondly, in order to determine the optimal parameters of such a controller, the objective function which takes into account transient response characteristics, disturbance, and measurement noise rejection capabilities of the AVR system is presented. Furthermore, the adaptive modification of the existing metaheuristic African vultures optimization algorithm (AVOA) is introduced for controller parameters design. Finally, unlike the many existing papers in the available literature which use a simplified model of the AVR system, in this work the simulation model of the AVR system is realized by observing the technical documentation of the excitation system of the 40 MVA SG from a hydropower plant Perucica in Montenegro. The results obtained in this work have proven that the proposed AVR controller has superior performances compared with other frequently used controllers in real power plants, in terms of providing transient response quality of the SG terminal voltage, disturbance rejection, and measurement noise mitigation abilities. Additionally, increased convergence speed and improved criterion function value demonstrated that the proposed adaptive modification of the AVOA algorithm outperforms some of the most popular metaheuristic algorithms. & COPY; 2017 Elsevier Inc. All rights reserved.
引用
收藏
页数:17
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