• 한국지진공학회논문집
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• 제9권1호통권41호
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• pp.61-70
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• 2005

본 연구에서는 하이브리드 면진장치가 설치된 단자유도 구조물에 대하여 진동대 실험을 수행하였다. 본 연구에서 사용된 하이브리드 면진장치는 네 개의 FPS와 한 개의 MR 감쇠기로 구성하였다. 다양한 크기 및 특성을 가진 지진하중을 하이브리드 면진장치가 설치된 구조물에 가하여 진동제어 성능을 평가하였다. 본 연구에는 준능동 MR 감쇠기의 저항력을 효과적으로 조절하기 위하여 퍼지제어기를 사용하였고 구조물에 부착된 계측기를 통하여 변위 및 가속도를 피드백으로 이용한다. 수동 및 준능동 제어기법을 사용하여 얻은 구조물의 응답을 서로 비교하였고 그 결과 FPS와 MR 감쇠기의 조합으로 다양한 특성의 하중을 받는 구조물의 진동제어를 효과적으로 수행할 수 있음을 알 수 있다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.207-216
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• 2017

Wind energy conversion systems (WECS) which consist of wind turbines with permanent magnet synchronous generator (PMSG) and full-power converters have become widespread in the field of renewable power systems. Generally, conventional diode bridge rectifiers have used to obtain a constant DC bus voltage from output of PMSG based wind generator. In recent years, together advanced power electronics technology, Pulse Width Modulation (PWM) rectifiers have used in WECS. PWM rectifiers are used in many applications thanks to their characteristics such as high power factor and low harmonic distortion. In general, L, LC and LCL-type filter configurations are used in these rectifiers. These filter configurations are not exactly compensate current and voltage harmonics. This study proposes a hybrid passive filter configuration for PWM rectifiers instead of existing filters. The performance of hybrid passive filter was tested via MATLAB/Simulink environment under various operational conditions and was compared with LCL filter structure. In addition, neuro-fuzzy controller (NFC) was preferred to increase the performance of PWM rectifier in DC bus voltage control against disturbances because of its robust and nonlinear structure. The study demonstrates that the hybrid passive filter configuration proposed in this study successfully compensates current and voltage harmonics, and improves total harmonic distortion and true power factor.

• 제어로봇시스템학회논문지
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• 제7권10호
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.