• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.211-218
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• 2002

We propose a intelligent controller for FACTS device to stabilize a power system. In order to identify the nonlinear characteristics of the power system and to estimate a control signal, an artificial neural network is utilized. Parameter and location of Unified Power Flow Controller(UPFC) on power system operating conditions are discussed. A UPFC is composed of an excitation transformer, a boosting, two three-phase GTO based voltage source converters, and a dc link capacitor. The proposed controller is applied to UPFC to verified the effectiveness of the proposed control system. The results show that the proposed nonlinear FACTS controller is able to enhance the transient stability of a three machine and nine bus system.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.710-713
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• 1995

Hopfield networks have been applied to the problem of linear system identification. In this paper, Hopfield network based parameter identification scheme of non-linear dynamic systems is proposed. Simulation results demonstrate that Hopfield network can be used effectively for the identification of non-linear systems assuming that the system states and their time derivatives are available. Therefore, the proposed scheme can be applied in fault detection and isolation(FDI) and adaptive control of non-linear systems where the Hopfield networks perform on-line identification of system parameters.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.235-235
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• 1999

화력 발전소의 드럼형 보일러 제어 프로세스에 있어 드럼 수위(Drum Level)의 정확한 측정은 매우 중요하다. 만약 드럼 수위가 불안정하게 되면 급수 유량 제어가 불안정하여 증기 온도 제어를 불안정하게 하고, 증기 온도의 변화는 보일러 출구 증기 압력을 변화시켜 연소 제어 계통을 불안정하게 한다. 결국 드럼 수위의 불안정은 발전소 전체 프로세스를 불안정하게 한다. 또한 드럼 수위의 오지시로 인해 수위가 과도하게 높아져 물이 터빈에 유입되면 터빈 날개의 파손을 가져오고, 반대로 수위가 너무 낮으면 과열로 인한 보일러 튜브의 파열을 초래하기도 한다. 특히, 보일러의 기동시 또는 과도상태일 때는 드럼 압력의 변화에 따른 water 및 steam의 밀도 변화로 인한 오차가 크며, 압력 대 밀도(비중)의 관계가 비선형 함수이므로 별도의 압력검출기에 의해 드럼 압력을 측정하여 압력 변화에 따른 오차를 보정해주어야 하는데 아날로그 시스템의 경우에는 이러한 압력 수위 보정을 기준 압력에 대해서만 하므로 기동시 또는 과도상태에서의 수위 제어에 많은 문제점이 있다. 본고에서는 이러한 보일러 드럼 수위 압력 보정의 유.무에 따라 드럼 수위 변화에 대해 시뮬레이션을 하여 압력 보정이 드럼 수위에 미치는 영향을 고찰하고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1319-1329
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• 2012

Globally stabilizing adaptive fuzzy state- and output-feedback controllers for the fully nonaffine pure-feedback nonlinear system are proposed in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controllers require no backstepping design procedures. Avoiding backstepping makes the controller structure and stability analysis to be considerably simplified. For the global stabilty of the clossed-loop system, the self-structuring fuzzy system whose memebership functions and fuzzy rules are automatically generated and tuned is adopted. The proposed controllers employ only one fuzzy logic system to approximate unknown nonlinear function, which highlights the simplicity of the proposed adaptive fuzzy controller. Moreover, the output-feedback controller of the considered system proposed in this paper have not been dealt with in any literature yet.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1821-1826
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• 2009

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2459-2461
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• 2005

EMTP 프로그램으로 뇌써지 발생시 가공송전선로 등가모델 구현을 위한 필수적인 요소들과 비선형 요소들을 시간영역에서 해석이 가능하다. 따라서 이번 연구에서는 EMTP를 이용하여 전력전송이 이루어지는 가공 송전 선로 등가모델을 구현하고 전송선로의 길이와 굵기, 뇌격전류의 크기를 변화시켜가면서 시뮬레이션을 통해 자료를 얻는다. 이 과정으로 얻어진 결과는 송전계통의 설계에 사용되어 안정적인 전력 전송에 적용될 수 있을 것이다.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.129-134
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• 1987

In this paper, "Revised two-level costate prediction method" is developed to optimize the quadratic performance of a class of nonlinear dynamic systems. To show the merit, of this algorithm, the proposed algorithm is compared With "The new prediction method" and "Two-level costate prediction method". Advantages of this algorithm are illustrated by applying it to three examples, turbine generator system, fermentation Process, power control system in nuclear reactor.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.356-358
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• 1994

In this paper, to alleviate the effect of approximation error and discontinuous variation of the controller parameters, the variable structure control scheme using neural networks is presented. In the proposed method, the variable structure control rules for each local linear models are designed to reject the effect of linearization error caused by linearization of the nonlinear system. And neural network infer approximate controller gains from combination of local linear control gains. The proposed control methods can be used to control nonlinear systems and it has robust characteristic against system parameter variations and external disturbances.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2081-2083
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• 2001

In this paper, without mathematical modeling dynamics, the plant parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.