• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.923-925
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• 1998

Electric arc furnaces (EAFs) are a main cause of voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this paper, a chaotic approach to such modeling is described and justified. At the same time, a DLL (Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.

• Journal of Power Electronics
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• 제16권2호
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.