• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.779-783
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• 2006

A The estimation of electrical power consumption is becoming more important to supply stabilized electrical power recently. In this paper, we propose a supplied forecasting system of electrical power using Fuzzy Compensative Algorithm to estimate electrical load accurately than the previous. We evaluate a time series of supplied electrical power have the chaotic character using quantitative and qualitative analysis, compose a forecasting system by the maximum change $rate(\alpha)$ of Fuzzy Algorithm and compensative parameter. Simulating it for obtained time series, we can obtain more accurate results than the previous proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.211-214
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• 1997

This paper applies fuzzy self-tuning PID controller in DS/CDMA cellular system. Power control is essential in DS/CDMA to compensate for the differing received powers due to both the slowly varying long-term and fast varying short-term fading processes and co-channel interference. The controller proposed is adaptable for the variations of the system dynamics and especially for the variable time delay which exists in mobile radio systems. Accordingly the results is the smaller power control error, that is, the smaller average transmitting power of mobile compared with the conventional control schemes. Because interferences to the other mobiles are reduced, the capacity of CDMA can be increased.

• Journal of Industrial Technology
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• v.15
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• pp.187-193
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• 1995

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.553-555
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• 1995

This paper presents the optimal short-term operation scheduling by using on cogeneration systems connected with auxiliary equipments. Simulation is performed in case of the bottomming cycle which generates the demand heat preferentially. Heat storage tank, electricity charger, auxiliary boiler and independent generator are considered as auxiliary equipments connected to cogeneration systems. The results of simulation show the auxiliary equipments can be effeciently operated in case of the bottomming cycle by modeling proposed in this paper. The effectiveness of operation scheduling gained by application of fuzzy theory is evaluated by detailed comparison and investigation of the simulation results.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.159-162
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• 1993

A new approach using fuzzy dynamic programming is proposed for the flexible long-term generation mix under uncertain circumstances. A characteristic feature of the presented approach is that not only fuzziness in fuel and construction cost. load growth and reliability but also many constraints of generation mix can easily be taken into account by using fuzzy dynamic programming. The method can accommodate arbitrary shape of membership function as well as the operation of pump-generator. And so more realistic solution can be obtained. The effectiveness of the proposed approach is demonstrated by the best generation mix problem of KEPCO-system which contains nuclear, coal, LNG, oil and pump-generator hydro plant in multi-years.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.48-57
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• 2001

In a fuzzy connectionist expert system(FCES), the knowledge base can be constructed of neural logic networks to represent fuzzy rules and their relationship, We call it fuzzy rule inference network. To find out the belief value of a conclusion, the traditional inference strategy in a FCES will back-propagate from a rule term of the conclusion and follow through the entire network sequentially This sequential search strategy is very inefficient. In this paper, to improve the above search strategy, we proposed fuzzy rule inference rule used in a FCES was modified. The proposed adaptive search strategy in fuzzy rule inference network searches the network according to the search priorities.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.487-492
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• 2007

This paper presents the development of digital robust fuzzy controller for uncertain nonlinear systems. The proposed approach is based on the intelligent digital redesign(IDR) method with considering the relaxed stability condition of fuzzy control system. The term IDR in the concerned system is to convert an existing analog robust control into an equivalent digital counterpart in the sense of the state-matching. We shows that the IDR problem can be reduced to find the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital robust control systems. Its constructive conditions are expressed as the linear matrix inequalities(LMIs) and thereby easily tractable by the convex optimization techniques. Based on the nonquadratic Lyapunov function, the robust stabilization conditions are given for the sampled-data fuzzy system, and hence less conservative. A numerical example, chaotic Lorentz system, is demonstrated to visualize the feasibility of the proposed methodology.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.