Robust modified GA based multi-stage fuzzy LFC

Shayeghi, Hossein; Jalili, Aref; Shayanfar, Heidar Ali

doi:10.1016/j.enconman.2006.11.010

Cited by 50 publications

(51 citation statements)

References 16 publications

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“…3. That consist of the uncertain plant matrix P and the diagonal compensation matrix G. These matrices are defined as (2).…”

Section: Controller Design Using Qftmentioning

confidence: 99%

“…A well designed and operated power system should cope with changes in the load and with system disturbances, and it should provide acceptable high level of power quality while maintaining both voltage and frequency within tolerable limits [1][2][3][4][5] . Several strategies for Load Frequency Control of power systems have been proposed by researchers over the past decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . This extensive research is due to fact that LFC constitutes an important function on power system operation where the main objective is to regulate the output power of each generator at prescribed levels while keeping the frequency fluctuations within pre-defined limits.…”

Section: Introductionmentioning

confidence: 99%

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Robust Decentralized Load Frequency Control Using Multi Variable QFT Method in Deregulated Power Systems

Taher¹,

Hematti²

2008

American J. of Applied Sciences

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Abstract:The Load Frequency Control problem has been a major subject in electrical power system design/operation and is becoming more significant recently with increasing size, changing structure and complexity in interconnected power systems. In practice LFC systems use simple proportionalintegral controllers. However, since the PI control parameters are usually tuned based on classical or trial-and-error approaches, they are incapable of obtaining good dynamic performance for a wide range of operation conditions and various load changes scenarios in multi-area power system. For this problem, in this paper quantitative feedback theory method is used for LFC control in multi-area power system with system parametric uncertainties. The system parametric uncertainties are obtained by changing parameters by 40% simultaneously from their typical values. A two-area power system example with a wide range of parametric uncertainties is given to illustrate proposed method. To show effectiveness of proposed method, a classical I type controller optimized by genetic algorithm is designed for LFC for comparison with QFT. The validity of the proposed method was confirmed by comparing it results with those of traditional methods (I controller optimized by genetic algorithm) has been confirmed.

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“…3. That consist of the uncertain plant matrix P and the diagonal compensation matrix G. These matrices are defined as (2).…”

Section: Controller Design Using Qftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Decentralized Load Frequency Control Using Multi Variable QFT Method in Deregulated Power Systems

Taher¹,

Hematti²

2008

American J. of Applied Sciences

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“…Since frequency control in a complex and competitive environment is a tedious process, the control technique must be more intelligent and adaptive for a changing environment. More research work has been carried on Frequency control in restructured power system [1]- [7]. Possible issues in Frequency control in energy market were discussed in [1].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System

Anitha¹,

Subburaj²

2016

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This paper discusses the implementation of Load Frequency Control (LFC) in restructured power system using Hybrid Fuzzy controller. The formulation of LFC in open energy market is much more challenging; hence it needs an intelligent controller to adapt the changes imposed by the dynamics of restructured bilateral contracts. Fuzzy Logic Control deals well with uncertainty and indistinctness while Particle Swarm Optimization (PSO) is a well-known optimization tool. Abovementioned techniques are combined and called as Hybrid Fuzzy to improve the dynamic performance of the system. Frequency control of restructured system has been achieved by automatic Membership Function (MF) tuned fuzzy logic controller. The parameters defining membership function has been tuned and updated from time to time using Particle Swarm Optimization (PSO). The robustness of the proposed hybrid fuzzy controller has been compared with conventional fuzzy logic controller using performance measures like overshoot and settling time following a step load perturbation. The motivation for using membership function tuning using PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes is tested on a two-area restructured power system.

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“…In order to improve the performance of the FLFC and to reduce fuzzy system effort cost and time consumption, fuzzy logic controllers combined with a genetic algorithm (GA) based on the hill climbing method and partial swarm optimization (PSO) algorithm were proposed in [12] and [13], respectively. In these strategies, the authors used a GA and PSO for the tuning of fuzzy MFs through multistage procedures.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, there has been growing interest in artificial intelligence techniques, such as fuzzy logic control (FLC) [7][8][9][10][11][12][13], artificial neural network (ANN) [14,15], and biologically inspired algorithms [16][17][18], to design a load frequency controller in a power system by research around the world. Among these techniques, since fuzzy logic controllers provide an effective means to model and control a complex and ill-defined plant, many control strategies based on the FLC approach have been suggested and applied successfully regarding the loadfrequency control of power systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules

Zamani

Bijami²,

Sheikholeslam³

2014

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, an auto-tuned fuzzy load frequency controller (FLFC)-based artificial bee colony (ABC) algorithm is developed to quench the deviations in the frequency and tie-line power due to load disturbances in an interconnected power system. Optimal tuning of membership functions (MFs) and fuzzy control rules is very important to improve the design performance and achieve a satisfactory level of robustness for a particular operation. In this work, to reduce the fuzzy system design effort and take large parametric uncertainties into account, a new systematic and simultaneous tuning method is developed for designing MFs and fuzzy rules. For this, the designing problem is restructured as an optimization problem and the ABC algorithm is employed to solve it. This newly developed method provides some advantages such as a flexible controller with a simple structure and easy algorithm. For the purpose of the proposed method's evaluation, the designed controller is applied to a 2-area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic proportional-integral controller.Simulation results show better operation and improved system parameters, such as the settling time and step response rise time, using the proposed approach, in the presence of system parameter variations.

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Robust modified GA based multi-stage fuzzy LFC

Cited by 50 publications

References 16 publications

Robust Decentralized Load Frequency Control Using Multi Variable QFT Method in Deregulated Power Systems

Robust Decentralized Load Frequency Control Using Multi Variable QFT Method in Deregulated Power Systems

Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System

Optimal fuzzy load frequency controller with simultaneous auto-tuned membership functions and fuzzy control rules

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