Load Frequency Control Based on Modern Techniques in Two Areas Power Systems

Load Frequency Control Based on Modern Techniques in Two Areas Power Systems

Naglaa Kamel Bahgaat (Canadian International College (CIC), Egypt), Mohamed Ibrahim (Al Azhar University, Egypt), Mohamed Ahmed Moustafa Hassan (Cairo University, Egypt) and Fahmy M. A. Bendary (Benha University, Egypt)
Copyright: © 2018 |Pages: 29
DOI: 10.4018/978-1-5225-4077-9.ch006


This chapter gives introduction to evolutionary techniques. Then it presents the problem formulation for load frequency control with evolutionary particle swarm optimization. It gives the application of particle swarm optimization (PSO) in load frequency control; also, it illustrates the use of an adaptive weight particle swarm optimization (AWPSO) and adaptive accelerated coefficients based PSO (AACPSO). Furthermore, it introduces a new modification for AACPSO technique (MAACPSO). The new technique is explained. A well-done comparison will be given in this chapter for these above-mentioned techniques. A reasonable discussion on the obtained results will be displayed. The obtained results are promising.
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1. Introduction

Frequency is an important factor to describe the stability criterion in power systems (Ismail & Hassan, 2012; Salami et al., 2006). To provide the stability of power system, the balance of power and steady frequency is required. In case of change occurs in active power demand or the generation in power systems, oscillations increase in both power and frequency. Frequency depends on active power balance so it cannot be hold in its rated value (Azar, 2010, 2012a and 2012b). Thus, system suffering from a serious of instability problem. Load Frequency Control (LFC) is an important issue in power system operation and control for supplying stable (Skogestad, 2003). The principle aspect of Automatic Load Frequency Control is to maintain the generator power output and frequency within the prescribed limits (Bahgaat et al., 2013).

There are many studies that have been conducted to reach the fastest and best control methods that achieve stability of the electrical power systems (Azar & Serrano, 2014; Azar & Serrano, 2015a,b,c,d; Zhu & Azar, 2015; Azar & Zhu, 2015; Boulkroune et al, 2016a,b; Ghoudelbourk et al., 2016; Mekki et al., 2015; Meghni et al, 2017a,b,c). Recently, many important developments related to the design of nonlinear systems for many practical applications have been proposed, 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 & Serrano, 2015a,b,c,d, 2016a,b, 2017; Azar et al., 2017a,b,c,d; Azar 2010, 2012; 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).

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