• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.895-904
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• 2015

This paper presents a probabilistic approach of reliability evaluation and economic assessment for solving transmission network expansion planning problems. Three methods are proposed for TNEP, which are reorganizing the existing power system focused on the buses of interest, selecting candidates using modified system operating state method with healthy, marginal and at-risk states, and finally choosing the optimal alternative using cost-optimization method. TNEP candidates can be selected based on the state reliability such as sufficient and insufficient indices, as proposed in this paper. The process of economic assessment involves the costs of construction, maintenance and operation, congestion, and outage. The case studies are carried out with modified IEEE-24 bus system and Jeju island power system expansion plan in Korea, to verify the proposed methodology.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2034-2036
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• 2002

In this paper, we propose the modeling of a chaotic nonlinear system using wavelet neural networks. In our modeling, we used the parameter adjusting method as the training method of a wavelet neural network. The difference between the actual output of a nonlinear chaotic system and that of a wavelet neural network adjusts the parameters of a wavelet neural network using the gradient-descent method. To verify the efficiency of this paper, we perform the simulation using Duffing system, which is a representative continuous time chaotic nonlinear system.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1137-1140
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• 1997

This paper presents a fuzzy-neural network controller technique on the load frequency control of two-area power system. Firstly, Fuzzy controller a series of initial selected rules are improved by means of the proposed technique. Secondly, scale factors for error, change rate of error and control input are optimized by the given error back-pagation teaming algorithms. Finally, the related simulation results show that the proposed fuzzy neural network controller technique are more powerful than conventional ones.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.491-493
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• 1995

In this paper, the method of long-term load forecasting using a fuzzy neural network of which input is a fuzzy membership function value of a input variable like as GNP which is considered to affect demand of load. The proposed method was applicated in Korea Electric Power Corporation (KEPCO). The comparison with Error Back-Propagation Neural Network has been shown.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.587-590
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• 2005

In this paper, the PWM inverter-motor drive system including sensor is controlled through the network. The algorithm to compensate for the time delay of ac current and ac voltage sensors due to the network is proposed. The delay time of sensors is kept nearly constant, using the synchronous signal and timers. The error between the real and estimated ac signals can be reduced by using two slopes for estimating the value of at signals. The proposed algorithms are verified with the simulation studies and experiments.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2467-2469
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• 2000

In this paper, the DSP implementation of induction motor drive is presented on the viewpoint of the design and experiment. The speed estimation of control system for induction motor drive is designed on the base of neural network speed estimator. This neural network speed estimator is experimentally applied to the induction motor system. This system provides the satisfactory results.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2720-2722
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• 2000

This paper proposes a face detection and recognition method that combines the template matching method and the eigenface method with the neural network. In the face extraction step, the skin color information is used. Therefore, the search region is reduced. The global property of the face is achieved by the eigenface method. Face recognition is performed by a neural network that can learn the face property.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2482-2484
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• 2001

제어시스템이 복잡해지고 다변화 되어짐에 따라 네트워크의 중요성이 부각되어지고 있다. 특히, 원격지 제어시스템의 정보를 실시간으로 수집, 분석함은 물론 제어할 필요가 있다. ENA(Embedded Network Adapter) 카드는 원격지에 있는 대상 제어시스템의 정보를 수집 및 명령을 전달하기 위한 장치이다. 이 카드의 개발은 네트워크의 발달로 인한 다양한 인터페이스 요구를 만족시키기 위하여 개발되었으며, 기존의 장치에는 없는 기능인 실시간 운영체계를 탑재하였으며, 우선순위에 의한 방법과 Round-Robin에 의한 방법으로 각각의 TASK들을 관리하도록 하였다. TCmini라는 PLC 상용제품에 적용실험을 통하여 그 성능을 입증하였다.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.