Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization

By Mohammad Ali Abido. IEEE Power Engineering Review, 2002

553 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2d1116815f028b6d3d10ee2ba66bb065502ea30f

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In Control and Systems Engineering

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Citations in the corpus

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “1) Controller Tuning: Specifically, PSO has been proposed in the literature as an efficient algorithm to tune the gains in classical PID controllers, as well as in nonlinear controllers [195]–[201].”
A Survey of Particle Swarm Optimization Applications in Electric Power Systems (2009, methodology): “In [37] and [38], PSO was employed to find the optimal settings of power system stabilizer parameters.”
On the computation of all global minimizers through particle swarm optimization (2004, background): “Among other applications, it has been applied to tackle multiobjective problems [14]–[19], minimax problems [20], [21], integer programming problems [22], noisy and continuously changing environments [23]–[26], errors-in-variables problems [27], existence of function zeros [28], and numerous enginee”
Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch (2008, methodology): “This method is increasingly gaining acceptance for solving economic dispatch [14]‐[18] and a variety of power system problems [ 19 ]‐[22], due to its simplicity, superior convergence characteristics and high solution quality.”
Particle Swarm Optimization in Structural Design (2007, methodology): “Recently, the PSO has been proven useful on diverse engineering design applications such as logic circuit design (e.g. Coello & Luna, 2003), control design (e.g. Zheng et al., 2003) and power systems design (e.g. Abido, 2002) among others.”
Analysis of the publications on the applications of particle swarm optimisation (2008, background): “6 Journal of Artificial Evolution and Applications cooker design, VLSI design, RF circuit synthesis [22], worst case electronic design, filter design [23], antenna design [24], CMOS wideband amplifier design, motor design, logic circuits design, power systems [25], transmission lines, mechanical des”
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation (2005, background): “Unfortunately, recent research has identified some deficiencies in GA performance [ 8 ].”
Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives (2008, unknown): “It has been applied to evolve weights and structure of neural networks [38–40], analyze human tremor [41], register 3Dto-3D biomedical image [42], play games [43], control reactive power and voltage [44, 45], pattern recognition [46], etc.”
Optimization of Governor Parameters to Prevent Frequency Oscillations in Power Systems (2018, methodology): “The particle swarm optimization (PSO) algorithm is used to solve the above optimization problem [16], [17].”
A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems (2007, methodology): “It has been used for many power system problems such as optimal design of power system stabilizers [ 23 ], distribution state estimation [24], and optimal reactive power dispatch [25], [26] apart from ED. After the introduction of PSO, many variations have been proposed for the basic PSO by various ”
A particle swarm optimization for economic dispatch with nonsmooth cost functions (2005, methodology): “Recently, PSO have been successfully applied to various fields of power system optimization such as power system stabilizer design [ 5 ], reactive power and voltage control [3], and dynamic security border identification [6].”
Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization (2009, background): “Another important advantage of PSO is that it has the possibility to control the balance between the global and local exploration of the search space [Engelbrecht, 2006; Abido, 2002 ].”