Bio-Inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA

Das, Tanmay; Venayagamoorthy, G.K.; Aliyu, Usman

doi:10.1109/tia.2008.2002171

Cited by 132 publications

(63 citation statements)

References 14 publications

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“…The proposed algorithm mimics the foraging behavior of E. coli present in our intestines. It is categorized into the following processes: chemotaxis, reproduction and elimination [8,15].…”

Section: Bfo Improvement For Ekf Training Algorithmmentioning

confidence: 99%

“…(1) Chemotaxis: This process simulates the movements of an E. coli cell through swimming and tumbling via flagella [8]. For N c number of chemotactic steps the direction of movement after a tumble is given by:…”

Section: Bfo Improvement For Ekf Training Algorithmmentioning

confidence: 99%

“…This behavior can be represented mathematically forage as a kind of swarm intelligence [3]. It has attracted significant attention from researchers, and has been used in several areas of control application such as design of multiple optimal power system stabilizers [8] and optimization of active power filters for load compensation [24]. It has been shown that BFO is able to find the optimum value and avoid being trapped in the local optima.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

Alanis

Arana‐Daniel

López-Franco

2015

Applied Mathematics and Computation

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“…The proposed algorithm mimics the foraging behavior of E. coli present in our intestines. It is categorized into the following processes: chemotaxis, reproduction and elimination [8,15].…”

Section: Bfo Improvement For Ekf Training Algorithmmentioning

confidence: 99%

Section: Bfo Improvement For Ekf Training Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

Alanis

Arana‐Daniel

López-Franco

2015

Applied Mathematics and Computation

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“…In a more explicit manner, multiobjective optimization problems are considered, where 2 or more performance criteria can be considered and handled separately [5][6][7][8][9]. As a result, multiple noncompetitive solutions are found instead of a unique and perfect solution.…”

Section: Introductionmentioning

confidence: 99%

A multiobjective tuning approach of power system stabilizers using particle swarm optimization

Labdelaoui

Boudjema²,

Boukhetala³

2016

Turk J Elec Eng & Comp Sci

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Abstract:This work presents an optimal tuning approach of power system stabilizers (PSSs) using multiobjective particle swarm optimization. Two types of PSSs are investigated, the conventional speed-based PSS type and a dual-input PSS type that uses the accelerating power as an additional input. The tuning problem of these PSSs is formulated as a minimization problem of a vector objective function characterizing the damping and the transient performance of the closed-loop system. A 3-machine 9-bus power system example is considered, and the speed-constrained multiobjective particle swarm optimization algorithm is used to solve the optimization problem. The results show that trade-offs exist between the 2 objective functions of the problem, and that the best trade-off is obtained with the dual-input PSS. The performance of the resulting PSSs is illustrated through numerical simulations considering different scenarios.

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“…The PSO method is becoming very popular due to its simplicity of implementation and ability to converge quickly to a reasonably good solution. PSO method has been successfully applied in many application areas such as task assignment [4], power systems [5,6] and biomedical image registration [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

Basu

2015

J. Inst. Eng. India Ser. B

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Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.

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Bio-Inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA

Cited by 132 publications

References 14 publications

Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

A multiobjective tuning approach of power system stabilizers using particle swarm optimization

Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

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