Optimal multiobjective design of robust power system stabilizers using genetic algorithms

Abdel-Magid, Y.L.; Abido, M. A.

doi:10.1109/tpwrs.2003.814848

Cited by 295 publications

(151 citation statements)

References 26 publications

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“…PSO consists of, at each step, changing the velocity of each particle toward its pbest an gbest according to equation (12). The velocity of particle i represented as Vi = (v i1 , v i2 , …,v iN ).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The PSS tuning problem is formulated as non-linear non differentiable optimization problem in [10] which is found to be very hard to solve using traditionally differentiable optimization algorithms. Several random exploration techniques like Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), and Evolutionary Programming (EP) has been successfully used [11][12][13][14][15] to optimize the PSS parameters. These techniques have gained acceptance because of their ability and effectiveness of searching an optimal solution in a problem space.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Ahmed¹,

Amin²

2012

IJCA

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In this paper, two evolutionary algorithms-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a derivative-free and flexible optimizer which is powered by the behavior of organism, such as bird flocking. Eigen-value based objective function is considered for the tuning of PSSs to enhance system damping of electromechanical mode. The performance of proposed IWO-based PSS and PSO-based PSS is tested and demonstrated under different disturbances for a four machine example power system. The Eigen value analysis and non-linear time domain simulation results shows that both IWO-based PSS and PSO-based design can successfully damp out the oscillations and thus improve the stability of the system. However, the abilities like faster convergence and greater shifting of critical modes to the left of s-plane keeps the choice of IWO based design in front of PSO based design for the system under consideration. General TermsPower system stability, Multi-machine system, Optimization.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Ahmed¹,

Amin²

2012

IJCA

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“…There are several research efforts that apply genetic algorithms (GAs) to optimize operational parameters in power systems [39][40][41][42]. [39][40][41] study parameter optimization for controllers (e.g., programmable logic controllers; PLC) in substations and control centers in order to, for example, stabilize the power current in circuit switching devices.…”

Section: Related Workmentioning

confidence: 99%

“…[39][40][41] study parameter optimization for controllers (e.g., programmable logic controllers; PLC) in substations and control centers in order to, for example, stabilize the power current in circuit switching devices. [42] leverages a GA to find the optimal size of equipment (e.g.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

2011

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This paper proposes and evaluates an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes QoS (quality of service) parameters in communication networks. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves the individuals via genetic operators such as crossover, mutation and selection for satisfying given QoS requirements. For evaluating EVOLT in real-world settings that have high-dimensional parameter and optimization objective spaces, this paper focuses on QoS optimization in safety-critical communication networks for electric power utilities. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs. Moreover, EVOLT visualizes obtained QoS parameters in a Self-Organizing Map in order to aid network administrators to intuitively understand the QoS parameters and the tradeoffs among optimization objectives.

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“…A power system stabilizer (PSS) has been widely used to solve the oscillatory instability oscillation and to improve small signal stability through the supplementary excitation control of a generator [1][2][3][4]. However, a PSS may cause great variations in the voltage profile, may result in leading power factor and may not be able to suppress oscillation resulting from severe disturbance [5].…”

Section: Introductionmentioning

confidence: 99%

Damping of Power System Oscillation using robust thyristor-controlled phase shifter controller

Han¹,

Jeong²,

Lee³

2017

International Journal of Innovative Research in Electrical, Ele

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Abstract:In this paper, a robust supplementary controller of thyristor-controlled phase shifter(TCPS) using  H control theory has been designed to damp power system oscillation and to enhance power system stability despite uncertainties of the power systems. Since an order of a designed controller using  H control theory become very high, a structure-specified low-order TCPS controller using genetic algorithm (GA) has been designed. In order to treat disturbance attenuation and robust stability specifications simultaneously in the stage of  H controller design using GA, two weighting transfer functions have been selected. The  H norm and damping ratio have been used as the objective function in GA. To verify control performance of proposed TCPS controller, the modal analysis and nonlinear simulations for one-machine infinite bus system have been performed. The simulation results under various disturbances for various operating conditions showthat the proposed TCPS controller provided better dynamic responses in comparison with the conventional PSS and TCPS controller.

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Optimal multiobjective design of robust power system stabilizers using genetic algorithms

Abstract: His research interests include power system control and operation, adaptive and robust control of power systems, and applications of AI techniques in power systems and FACTS.

Cited by 295 publications

References 26 publications

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

Damping of Power System Oscillation using robust thyristor-controlled phase shifter controller

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