• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.783-788
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• 2008

This paper presents the mechanism of the machine and the numerical result of attractive force in the Electro-Permanent Magnet Lifter (EPML) and an improved niching Genetic Algorithm (GA) applying the concept of auto-tuning and detecting traces. Population size and both (right and left) niche radii of each peak in an asymmetrical objective function can be determined automatically. The validity of the proposed method is verified by simulation results.

• Progress in Superconductivity and Cryogenics
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• v.7 no.4
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• pp.24-27
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• 2005

Development of a 600 kJ class Superconducting Magnetic Energy Storage (SMES) system is being in progress by Korea Electrotechnology Research Institute(KERI). High temperature superconducting (HTS) wires are going to be used for the windings for the SMES system is presented in this paper. We considered BSCCO-2223 wire for the HTS windings and the operating temperature of the winding was decided to be 20 K which will be accomplished by conduction cooling method using cyro-coolers. Auto-Tuning Niching Genetic Algorithm was adopted for an optimization method of the HTS magnets in the SMES system. The objective function of the optimal process was minimizing total amount of the HTS wire. As a result, we obtained output parameters for optimization design of 600 kJ class SMES under several constrained conditions. These HTS windings are going to be applied to the SMES system whose purpose is stabilization of the power grid.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.4
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• pp.211-216
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• 2004

Inductance characteristics, torque variations and ripple according to current, and position of multi-layer buried magnet synchronous machines with field-weakening operations are presented. The rotor structure optimal design of a buried magnet synchronous machine with high performance is investigated, and optimization results and comparison among design candidates are shown. For the fast and accurate search of multiple optima, the auto-tuning niching genetic algorithm is used and a final solution is selected considering various design factors.