Automatic Voltage Regulator System Tuning Using Swarm Intelligence Techniques

Automatic Voltage Regulator System Tuning Using Swarm Intelligence Techniques

Naglaa K. Bahgaat (Canadian International College (CIC), Egypt) and Mohamed Ahmed Moustafa Hassan (Cairo University, Egypt)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/978-1-5225-4077-9.ch008


The voltage regulator may be used to regulate one or more AC or DC voltages in power systems. Voltage regulator may be designed as a simple “feed-forward” or may include “negative feedback” control loops. It may use an electronic components or electromechanical mechanism on the design. AVR is keeping constant output voltage of the generator in a specified range. The PID controller can used to provide the control requirements. This chapter discusses some modern techniques to get the best possible tuning controller parameters for automatic voltage regulator techniques such as particle swarm optimization, adaptive weight particle swarm optimization, adaptive acceleration coefficients, adaptive acceleration coefficients. Also, it presents a new adjustment modified adaptive acceleration coefficients and a discussion of the results of the all methods used. Simulation for comparison between the proposed methods and the obtained results are promising.
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1. Introduction

Automatic Voltage Regulator is a very important part of the power system generation, the main aim of the Automatic Voltage Regulator (AVR) loop is to control the amount of the terminal voltage V of the generator in the power system. The Dc signal, being proportional to |V|, is compared with a dc reference \V\ref, the result of the comparison is the value of the “error voltage”, after amplification and signal shaping, serves as the input to the exciter which finally delivers the voltage Vf to the generator field winding (Naglaa, 2013; Tammam,2011; Ingemar, 1983). A simple AVR circuit consists of many parts: amplifier, exciter, generator and sensor. In order to maintain the normal operation of AVR in the power system to control the value of the generator output voltage, there are many types of controllers used. In Previous works on AVR system with self-tuning control was initiated in the years of 1990s. Sweden bank and coworkers carried out the classical self-tuning control techniques to the AVR system in 1999 (Bhati & Nitnawwre, 2012; Swidenbank et al., 1999). After this study, Fitch used a generalized projecting control technique as a self-tuning control algorithm in the same year (Fitch et al., 1999). Several nonlinear system approaches have been proposed for many practical applications such as optimal control, nonlinear feedback control, adaptive control, sliding mode control, nonlinear dynamics, chaos control, chaos synchronization control, fuzzy logic control, fuzzy adaptive control, fractional order control, and robust control and their integrations (Azar & Vaidyanathan, 2015a,b,c, 2016; Azar & Zhu, 2015; Meghni et al, 2017a,b,c; Boulkroune et al, 2016a,b; Ghoudelbourk et al., 2016; Azar & Serrano, 2015a,b,c,d, 2016a,b, 2017; Azar et al., 2017a,b,c,d; Azar 2010a,b, 2012; Mekki et al., 2015; Vaidyanathan & Azar, 2015a,b,c,d, 2016a,b,c,d,e,f,g, 2017a,b,c; Zhu & Azar, 2015; Grassi et al., 2017; Ouannas et al., 2016a,b, 2017a,b,c,d,e,f,g,h,I,j; Singh et al., 2017; Vaidyanathan et al, 2015a,b,c; Wang et al., 2017; Soliman et al., 2017; Tolba et al., 2017). The usage of artificial intelligence based self-tuning controllers was preferred by researchers from the beginning of 2000 (Azar & Zhu Q, 2015). In particular, self-tuning PID type controllers which were tuned with the optimization methods based on artificial intelligence have been initiated to carry out the AVR system since as described in (Azar & Vaidyanathan, 2015b; Azar & Vaidyanathan, 2015c), then Gaing suggested a PSO based self-tuning PID controller for AVR in 2006, Kim and colleagues developed the hybrid method which contains genetic algorithm and bacterial foraging optimization technique in order to improve the performance of self-tuning PID controller in AVR system (Kim & Cho, 2006). In 2007, Mukherjee and Goshen reported the Surgeon fuzzy logic self-tuning algorithm based on crazy-PSO for PID controller (RamaSudha, et al., 2010; Bevrani, 2009; Ismail, 2006).

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