Dual Proportional Integral Controller of Two-Area Load Frequency Control Based Gravitational Search Algorithm

Elsisi, Mahmoud; Soliman, M.; Aboelela, Magdy A.S.; Mansour, W.

doi:10.11591/telkomnika.v15i1.8093

Cited by 10 publications

(12 citation statements)

References 20 publications

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“…In [12] multiple virtual voltage vectors are constructed that borrowed from discrete voltage space vector modulation (DSVM) idea. Although the effect of tracking control is improved obviously, but the number of MPC rolling optimization is increased, which has brought a very serious challenge to the actual digital system implementation [4][5][6]. …”

Section: Introductionmentioning

confidence: 99%

Model Predictive Optimization Control Strategy for Three-level AFE Converter

2016

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IntroductionWith the advantages of low voltage harmonic, high power factor and bidirectional energy flow, multi-level active front end (AFE) converter has been widely used in active power filter, reactive power compensation, and new energy grid generation and so on [1]. The control targets of the multi-level converter are not only to adjust the current of the AC side rapidly, suppress the harmonics, and adjust power factor, but also the problems of neutral point potential fluctuation and power device loss should be considered. For above purposes, domestic and foreign scholars have conducted in-depth research on its control strategy, which mainly include voltage oriented control (VOC) and direct power control (DPC) [2]. However, due to the time-variability and nonlinearity, it is difficult to achieve the best control effect. In recent years, with the rapid development of digital processors, some complex and new control strategies, such as adaptive control, sliding mode control, fuzzy control, predictive control etc, are well implemented [7,8].For easy to understand, flexible control, good dynamic performance and strong robustness，finite control set model predictive control (FCS-MPC) has been widely used in power electronics and power transmission applications, and also it can be easily and effectively integrated into a variety of constraints which can achieve multi-objectives optimization control [3]. Different from the traditional linear control strategy, FCS-MPC algorithm is based on accurate mathematical model of object system to predict the future state. At the same time, with taking into multi-control objectives, the optimal switching state can be determined by the cost function for global rolling optimization. However, numerous switching vectors, time-cost computation and complex cost functions which limit the application of FCS-MPC. In [13] a simplified model predictive algorithm is presented which can effectively reduce the operation time and improve the efficiency. But part of the control performance is affected. In [9] a satisfactory predictive control strategy is used to realize the multiple target satisfaction control among the NPC three-evel rectifier, neutral point potential balance, current tracking and low switching frequency operation through fuzzy decision. In [12] multiple virtual voltage vectors are constructed that borrowed from discrete voltage space vector modulation (DSVM) idea. Although the effect of tracking control is improved obviously, but the number of MPC rolling optimization is increased, which has brought a very serious challenge to the actual digital system implementation [4][5][6].

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Section: Introductionmentioning

confidence: 99%

Model Predictive Optimization Control Strategy for Three-level AFE Converter

2016

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“…LFC is accomplished by two different control actions of the primary speed control and supplementary speed control in an interconnected power system. Several approaches such as optimal, genetic algorithm (GAs), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), bacterial foraging optimization (BFO), etc., for the design and optimization of the LFC system, have been reported in the literature [4][5][6][7][8][9][10][11][12][13][14][15]. Modern optimal control concept for AGC designs of interconnected power system was firstly presented by Elgerd and Fosha [4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [13][14] with and without GRC. Application of Gravitational Search Algorithm (GSA) to optimize several important parameters in AGC of an interconnected power systems, has been reported in [15]. Recently ABC has emerged as powerful optimization technique because colonies of bees have instinct ability known as swarm intelligence [16].…”

Section: Introductionmentioning

confidence: 99%

ABC Based Design of PID Controller for Two Area Load Frequency Control with Nonlinearities

Elsisi

Soliman

Aboelela

et al. 2015

TIJEE

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This paper presents an application of the Artificial Bee Colony (ABC) to optimize the parameters of Proportional-Integral-Derivative controller (PID) of nonlinear Load Frequency Controller (LFC) for a power system. A two area non reheat thermal system is equipped with PID controller. ABC is employed to search for optimal controller parameters to minimize the time domain objective function. The performance of the proposed technique has been evaluated with the performance of the conventional Ziegler Nichols (ZN) , Genetic Algorithm (GA) and Bacterial Foraging Optimization Algorithm (BFOA) in order to demonstrate the superior efficiency of the proposed ABC in tuning PID controller. By comparison with the conventional technique, GA and BFOA, the effectiveness of the proposed ABC is validated over different operating conditions, and system parameters variations.

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“…Several approaches such as optimal, genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), etc., for the design and optimization of the LFC system, have been reported in the literature [4][5][6][7][8][9][10][11][12][13][14][15]. Modern optimal control concept for AGC designs of interconnected power system was firstly presented by Elgerd and Fosha [4][5].…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [13][14] have proposed bacterial foraging optimization algorithm (BFOA) for designing PI and PID-based load frequency controller for two-area power system with and without GRC. In [15] a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the algorithm, the searcher Model Predictive Control (MPC) is improved considerably in the last decades in field of control.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Two-Area Load Frequency Control Based Imperialist Competitive Algorithm

Elsisi

Aboelela

Soliman

et al. 2015

TIJEE

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Imperialist Competitive Algorithm (ICA) has recently been explored to develop a novel algorithm for distributed optimization and control. This paper proposes a Model Predictive Control (MPC) of Load Frequency Control (LFC) based ICA to enhance the damping of oscillations in a two-area power system. A two-area non-reheat thermal system is considered to be equipped with Model Predictive Control (MPC). ICA is utilized to search for optimal controller parameters by minimizing a time-domain based objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller, and PI controller tuned by ICA in order to demonstrate the superior efficiency of the proposed MPC tuned by ICA. Simulation results emphasis on the better performance of the optimized MPC based on ICA in compare to optimized PI controller based on ICA and conventional one over wide range of operating conditions, and system parameters variations.

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Dual Proportional Integral Controller of Two-Area Load Frequency Control Based Gravitational Search Algorithm

Cited by 10 publications

References 20 publications

Model Predictive Optimization Control Strategy for Three-level AFE Converter

Model Predictive Optimization Control Strategy for Three-level AFE Converter

ABC Based Design of PID Controller for Two Area Load Frequency Control with Nonlinearities

Model Predictive Control of Two-Area Load Frequency Control Based Imperialist Competitive Algorithm

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