Multi-Objective Optimal Design of a NEMA Design D Three-phase Induction Machine Utilizing Gaussian-MOPSO Algorithm

Ren, Ziyan; Koh, Chang Seop

doi:10.5370/jeet.2014.9.1.184

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…Thus, the multi-objective optimal design is an important research topic. Recently, several multi-objective evolutionary algorithms have been developed and successfully applied to the design of numerous engineering applications [1], [2]. Differential evolution (DE) has been reported to be a simple yet a powerful optimization algorithm and successfully applied to single and multi-objective optimization problems [3].…”

Section: Introductionmentioning

confidence: 99%

A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

et al. 2015

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A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given sampling data may be minimized. In the developed multi-objective optimization algorithm, the DTK provides predicted objective function values as an alternative to direct finite-element analysis. The effectiveness of the proposed DTK and multi-objective optimization strategy are verified through applications to analytic example and TEAM 22. Index Terms-Binary particle swarm optimization (B-PSO), dynamic Taylor Kriging (DTK), multi-objective differential evolution (MODE).

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Section: Introductionmentioning

confidence: 99%

A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

et al. 2015

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“…For these applications, induction motors with design D are preferred. Design D induction motors are characterized by high starting torque, low starting current and high operating slip [6].…”

Section: Introductionmentioning

confidence: 99%

Improving Sucker Rod Pump Efficiency Using Frequency Controlled Induction Motor

Текле¹,

Ziuzev²,

Костылев³

2022

IZVESTIYA

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Relevance. In sucker rod pump installations, the cost of the prime mover’s power use has substantial effect on the overall operational cost. Reduction in power consumption can lead to reduction in operating cost. Hence, as the sucker rod pump is dominant in the oil industry, any means which reduces the energy consumption can produce considerable economic benefit and help to meet the energy efficiency targets and standards. Due to the losses in the prime mover, surface transmission, and sucker rod string the power required to lift oil to the surface is always less than the power input to the prime mover. Induction motors, which are widely used as prime movers in sucker rod pump installations, operate at significantly lower efficiency and at a load lower than their rated capacity. Therefore, the demand for efficiency improvement is readily seen. This demand can be achieved by controlling motor losses through AC-DC-AC converter. The main aim of the research is to develop control strategy that helps to operate the sucker rod pump drive at optimal efficiency. Objects: electrical drive, sucker rod pump, oil producing well. Method: integrated simulation model consisting of the models of AC-DC-AC converter, induction motor including iron loss, sucker rod pump; vector control; generation of optimal magnetizing current trajectory for one cycle of pump operation. Result. The energy consumption of sucker rod pump unit for operation at rated flux and the one based on optimal magnetizing current trajectory were compared using an integrated simulation model. The simulation results indicate that about 1,6 % of the required energy can be saved when the sucker rod pump is operated based on the calculated optimal magnetizing current trajectory.

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Optimization of an induction motor for loss reduction considering manufacturing tolerances

Huang

et al. 2022

Struct Multidisc Optim

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Multi-Objective Optimal Design of a NEMA Design D Three-phase Induction Machine Utilizing Gaussian-MOPSO Algorithm

Cited by 9 publications

References 13 publications

A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

Improving Sucker Rod Pump Efficiency Using Frequency Controlled Induction Motor

Optimization of an induction motor for loss reduction considering manufacturing tolerances

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