• 대한전기학회논문지:전력기술부문A
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• 제51권2호
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• pp.83-92
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• 2002

In this paper, we design a GA-fuzzy precompensator for enhancement of power system stability. Here, a fuzzy prerompensator is designed as a fuzzy logic-based precompensation approach for Power System Stabilizer(PSS). This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of Magnetics
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• 제16권4호
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• pp.379-385
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• 2011

This paper presents an optimal design of a direct-drive permanent magnet synchronous generator for a small-scale wind energy conversion system. An analytical model of a small-scale grid-connected wind energy conversion system is presented, and the effects of generator design parameters on the payback period of the system are investigated. An optimization procedure based on genetic algorithm method is then employed to optimize four design parameters of the generator for use in a region with relatively low wind-speed. The aim of optimization is minimizing the payback period of the initial investment on wind energy conversion systems for residential applications. This makes the use of these systems more economical and appealing. Finite element method is employed to evaluate the performance of the optimized generator. The results obtained from finite element analysis are close to those achieved by analytical model.

• Nuclear Engineering and Technology
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• 제49권8호
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• pp.1696-1710
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• 2017

An improved Reactor Regulating System (RRS) logic architecture, which is combined with genetic algorithm (GA), is implemented in this work. It is devised to provide an optimal solution to the current RRS. The current system works desirably and has contributed to safe and stable nuclear power plant operation. However, during the ascent and descent section of the reactor power, the RRS output reveals a relatively high steady-state error, and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this work proposes to apply GA to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse engineering is implemented to build a Simulink-based RRS model. Reengineering is followed to produce a newly configured RRS to generate an output that has a reduced steady-state error and diminished overshoot level. A full-scope APR1400 simulator is used to examine the dynamic behaviors of RRS and to build the RRS Simulink model.

• 한국시뮬레이션학회논문지
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• 제10권4호
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• pp.65-75
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• 2001

This study presents a heuristic algorithm for buffer allocation in FMS(Flexible Manufacturing System). The buffers, which are finite resources in FMS, are responsible for improvement of an overall system utilization. But, until now, the study for buffer allocation in FMS are rarely conducted because of the complexity in FMS. Most studies for buffer allocation had been addressed to the simple production line system. The presented algorithm uses a simulation for the description of system complexity and uses a genetic algorithm for finding better buffer allocation. Lastly, we compare performance of the presented algorithm with that of a simple heuristic, and analyze the experiment results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3013-3015
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions, At the same time, the fine tunings of their gain parameters are also troublesome, Thus, in this paper, an Evolving Neural Network ControlleY(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm (RVEGA) was presented for stabilization of an IP system with nonlinearity, This proposed ENNC was described by a simple genetic chromosome. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• Journal of international Conference on Electrical Machines and Systems
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• 제2권3호
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• pp.361-366
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• 2013

This paper presents the effect on application of the flywheel energy storage system (FESS) for load frequency control (LFC) of an interconnected 2 area power system. To do this, the control characteristics with the FESS were compared with that of the conventional governor controller. The controller for the FESS control and the governor control used a PID type controller. Both the FESS PID controller and the governor PID controller using genetic algorithm (GA) were designed to optimize the PID parameters. The frequency and generation output characteristics with the only FESS controller and with the only conventional governor controller were compared. To verify robust performance of the FESS controller, the computer simulations were performed under various disturbances. The simulation results showed that the FESS controller provided better dynamic responses in comparison with the conventional governor controller.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1327-1329
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• 2005

This paper presents a gam optimization method using genetic algorithm(GA) for a magnetic levitation(Maglev) controller. GA uses the integral of square error(ISE) as performance index. The plant dynamics are described and modelled by mathematical equations. Also, the system apparatus for the Maglev system are described. Using the derived model, to optimize the feedback gains of conventional state feedback controller(SFC), GA is simulated with SIMULINK model. finally, using the optimized feedback gains, SFC is applied to the Maglev system. From the results, we can see that GA can give a solution for the better control performance for the Maglev system.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.497-504
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system nay be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.