Optimal AGC tuning with genetic algorithms

Abdel-Magid, Y.L.; Dawoud, M.M.

doi:10.1016/s0378-7796(96)01091-7

Cited by 114 publications

(50 citation statements)

References 4 publications

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“…Various conventional and intelligent techniques like ISE-based continuous-discrete control (Nanda et al 2006), Ziegler Nichols (ZN) method (Chandrakala et al 2013), variable structure control (Chandrakala et al 2013), neural networks (Chaturvedi et al 1999), genetic algorithm (GA) (Abdel-Magid and Dawoud 1996;Chandrakala et al 2013;Jeyalakshmi and Subburaj 2015), fuzzy control (Indulkar and Raj 1995;Chandrakala et al 2013;Jeyalakshmi and Subburaj 2015), bacterial forazing optimization algorithm (BFOA) (Ali and Abd-Elazim 2011), firefly algorithm (FA) (Padhan et al 2014), hybrid particle swarm optimizationpattern search (PSO-PS)-based fuzzy (Sahu et al 2015a), PSO (Gozde et al 2010;Abdel-Magid and Abido 2003;Panda et al 2013), PSO-based fuzzy (Jeyalakshmi and Subburaj 2015), hybrid BFOA-PSO (Panda et al 2013), artificial bee colony (ABC) (Gozde et al 2012), hybrid FA-PS (Sahu et al 2015b, c), hybrid differential evolution-PS (DE-PS)-based fuzzy ) and BFOA-based fuzzy ) are prevalent in the literature to solve AGC problem in traditionally interconnected power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Arya

Kumar

2016

Soft Comput

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In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA-PSObased PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA-PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA

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Section: Literature Reviewmentioning

confidence: 99%

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Arya

Kumar

2016

Soft Comput

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“…The optimum parameter values of the classical AGC have been obtained in the literature (using integral or proportional-plus-integral) by minimizing the popular integral of the squared error criterion (ISE) (Abdel-Magid and Dawoud, 1997). This criterion has been used because of the ease of computing the integral both analytically and experimentally.…”

Section: Fitness-objective Function and Performance Indices Under Conmentioning

confidence: 99%

“…Many investigations have been reported in the past pertaining to AGC of a large interconnected power system (i.e Abdel-Magid and Dawoud, 1997, Aditya and Das, 2003, Cohn, 1986. A net interchange tie-line bias control strategy has also been widely accepted by utilities.…”

Section: Introductionmentioning

confidence: 99%

performance evaluation of evolutionary designed conventional AGC controllers for interconnected electric power system studies in a deregulated market environment

Karnavas¹,

Dedousis²

2010

Int. J. Eng. Sci. Tech

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Electric power industry is currently in transition from vertically integrated utilities to an industry that will incorporate competitive companies. This increases the complexity of the load frequency issue and calls for more insight and research. In this context, the tuning of a multi-area automatic generation control (AGC) system after deregulation and furthermore, the effect of reheat turbines dynamics in the power system performance, are not yet discussed in depth and are studied in this work. The effect of bilateral contracts on the dynamics of the system is taken into account and the concept of DISCO participation matrix for these bilateral contracts is simulated. Genetic algorithms are adopted in order to obtain the optimal parameters of the loadfrequency controllers as well as of the frequency biases of thermal systems with reheat turbines. Also, since the optimum parameter values of the classical AGC have been obtained in the literature by minimizing the popular integral of the squared errors criterion (ISE) only, an effort is made in this study to show that this criterion does not give always the best system performance especially in a deregulated environment. In this work, we investigate the optimum adjustment of the load frequency controllers using a set of performance indices which are various functions of error and time. In this way, someone can observe the various performances that such a kind of power system might have when a different performance index is used. It should be noted that to the extent of the authors' knowledge, this kind of optimization has not been done yet in the literature. The performances of the tuned two-area AGC system are obtained using appropriate Matlab/Simulink models. Finally, it is envisaged that the synthesis procedure highlighted in this paper could be of practical significance for tuning conventional AGC controllers for an interconnected thermal-electric power system in a deregulated environment.

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“…Several studies have explored the cost of providing AGC and tertiary reserve incurred by the generating agents in order to develop bidding strategies under a deregulated framework [5][6][7][8][9][10]. In addition, the design and optimization of the AGC area regulator, which calculates generating unit setpoints has been widely addressed in order to maximize the profit obtained from providing the service [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…The main key characteristics of the reactive support ancillary service have been explored in ref. [11] and the structure and computation of the cost incurred to provide the service widely analyzed [12][13][14][15][16][17][18][19][20][21] for their inclusion in optimal power flow programs.…”

Section: Introductionmentioning

confidence: 99%