• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.2
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• pp.51-60
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• 2005

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented by adding a fuzzy precompensator to an existing TCSC-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. Nonlinear simulation results show that the proposed control technique is superior to conventional TCSC-PSS in dynamic responses over the wide range of operating conditions and in convinced robust and reliable in view of structure.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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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.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.137-139
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• 2001

In this paper, we design a GA-fuzzy precompensator for enhancement of power system stability. Here, a fuzzy precompensator 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, name1y, 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 the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.84-90
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• 2004

Many control techniques have been proposed in order to improve the control performance in the control system. In the feedback control system the output of controller is generally used as the input of a plant But the undesired noise is included in the output of a controller. Therefore, there is a need to use a precompensator for rejecting the undesired noise and improving the response characteristic of a system. In this paper, the design method of a precompensator is proposed for the model following control in the I-PD control system. The proposed precompensator is implemented with a neural network. The games of a precompensator are adjusted automatically to obtain a desired response of a system when the response characteristic of a system is changed under a condition.

• Proceedings of the KIEE Conference
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• summer
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• pp.292-294
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• 2004

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing TCSC-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 Auction 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.185-188
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• 1997

In this paper, a fuzzy precompensated PI controller for inverter-type air-conditioner is presented. The presented control scheme is composed of a fuzzy logic precompensator and PI controller, in which two control schemes are serially connected. The rules of the fuzzy precompensator is designed to improve the performance by considering the nonlinear characteristics of a temperature dynamics. The experimental results show the effectiveness of the proposed controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.619-625
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• 2000

A many control techniques have been proposed in order to improve the control performance of the discrete-time domain control system. In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore there is a need to used a precompensator for rejecting the undesired noise. In this paper, The expanded I-PD control system with a precompensator is constructed. The precompensator and I-PD controller are designed by a neural network and these coefficients are changed automatically to be a desired response of system when the response characteristic of system is changed under a condition.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.218-220
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• 2000

In this paper, the problem of the design of an intelligent type precompensator is discussed for the performance improvement of a power system stabilizer(PSS). An advantage of the scheme is that an existing PSS can be easily modified in our control structure simply by adding an adaptive neurofuzzy-based precompensator. The overall system has been tested on a simulation model in different operation conditions. Case studies show the proposed scheme can provide the good damping of the power system over the wide range of operating conditions and improve the dynamic performance of the system.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.88-94
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• 2001

In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore, there is a need to use a precompensator for rejecting the undesired noise. In this paper, the expanded PID controller with a precompensator is constructed. The precompensator and PID controller are designed by a neural network with two-hidden layer and these coefficients are changed automatically to be a desired response of system when the response characteristic is changed under a condition.