Parameter estimation of an induction machine using advanced particle swarm optimisation algorithms

Huynh, Duy C.; Dunnigan, Matthew W.

doi:10.1049/iet-epa.2009.0296

Cited by 83 publications

(44 citation statements)

References 9 publications

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“…; n: the maximum iteration number; i: the i th particle of the swarm, [11]. pbest i (k): the best position found by the i th particle (personal best).…”

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

“…With large cognitive and small social parameters at the beginning, particles are allowed to move around a wider search space instead of moving towards a population best. Additionally, with small cognitive and large social parameters, particles are allowed to converge to the global optima in the latter part of optimization [11]. r 1 and r 2 : the two independent random sequences which are used to effect the stochastic nature of the algorithm, r 1 and r 2 U(0, 1) [11].…”

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

“…Additionally, with small cognitive and large social parameters, particles are allowed to converge to the global optima in the latter part of optimization [11]. r 1 and r 2 : the two independent random sequences which are used to effect the stochastic nature of the algorithm, r 1 and r 2 U(0, 1) [11]. w: the inertia weight [11].…”

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

“…r 1 and r 2 : the two independent random sequences which are used to effect the stochastic nature of the algorithm, r 1 and r 2 U(0, 1) [11]. w: the inertia weight [11]. This value was set to 1 in the original PSO algorithm [10].…”

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

See 3 more Smart Citations

Optimal Generation Rescheduling of Power Systems with Renewable Energy Sources Using a Dynamic PSO Algorithm

Huynh¹,

Minh²

2016

International Advanced Research Journal in Science, Engineering

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This paper proposes a dynamic particle swarm optimization (PSO) algorithm for optimal generation rescheduling of a power system including renewable energy sources such as the solar and wind energy sources. The algorithm is to minimize total operating costs of this hybrid power system. The proposed dynamic PSO algorithm is one of the standard PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO algorithm to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The dynamic PSO algorithm based optimal generation rescheduling of the power system with and without solar and wind powers is considered on the standard IEEE 30-bus 6-generator 41-transmission line test power system. The numerical results demonstrate the capabilities of the proposed algorithm to generate optimal solutions of the power system considering the renewable energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to reschedule an optimal generation of the power system including the solar and wind energy sources.

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“…; n: the maximum iteration number; i: the i th particle of the swarm, [11]. pbest i (k): the best position found by the i th particle (personal best).…”

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

Section: Dynamic Pso Algorithm Based Optimal Generation Reschedumentioning

confidence: 99%

See 2 more Smart Citations

Optimal Generation Rescheduling of Power Systems with Renewable Energy Sources Using a Dynamic PSO Algorithm

Huynh¹,

Minh²

2016

International Advanced Research Journal in Science, Engineering

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“…Zero final value is a special kind of adaptive method [1][2][3][4][5][6][7][8][9] that seldom is discussed. As the demand for control performance increases, the problem of time-variance parameters of controlled system is more and more important.…”

Section: Introductionmentioning

confidence: 99%

Research On A Kind Of Improved Integral Adaptive Control Method With Zero Final Value

Liang¹,

Wu²,

Lei³

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

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A Novel Fault Location Technique for Transmission Lines of a Power System Based on a Language of an Optimization Problem

Hương

Huynh

Truong

et al. 2020

Advances in Intelligent Systems and Computing

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Parameter estimation of an induction machine using advanced particle swarm optimisation algorithms

Cited by 83 publications

References 9 publications

Optimal Generation Rescheduling of Power Systems with Renewable Energy Sources Using a Dynamic PSO Algorithm

Optimal Generation Rescheduling of Power Systems with Renewable Energy Sources Using a Dynamic PSO Algorithm

Research On A Kind Of Improved Integral Adaptive Control Method With Zero Final Value

A Novel Fault Location Technique for Transmission Lines of a Power System Based on a Language of an Optimization Problem

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