• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.1
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• pp.103-110
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• 1998

The modeling of load characteristics is a difficult problem because of uncertainty of load. This research uses artificial neural networks which can approximate nonlinear problem to represent load characteristics. After the selection of typical load, active and reactive power for the variation of voltage and frequency is obtained from experiments. We constructed and learned ANN based on these data for component load identification. The learned ANN identified load characteristics for other voltage and/or frequency variation. In addition, the results of component load identification are presented to demonstrate the potentiality of the proposed method.method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.508-516
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• 1999

This paper presents a method for constructing composite power system effective load duration curves(CMELDC) at load points by Monte Carlo method. The concept of effective load duration curves(ELDC) in power system planning is useful and important in both HLII. CMELDC can be obtained from convolution integral processing of the probability function of unsupplied power and the load duration curve at each load point. This concept is analogy to the ELEC in HLI. And, the reliability indices (LOLP, EDNS) for composite power system are evaluated using CMELDC. Differences in reliability levels between HLI and HLII come from considering with the uncertainty associated with the outages of the transmission system. It is expected that the CMELDC can be applied usefully to areas such as reliability evaluation, probabilistic production cost simulation and analytical outage cost assessment, etc. in HLII, DC load flow and Monte Carlo method are used for this study. The characteristics and effectiveness of thes methodology are illustrated by a case study of the IEEE RTS.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• Structural Engineering and Mechanics
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• v.34 no.4
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1055-1063
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• 1999

This paper present a conceptual possibilistic approach using fuzzy set theory to manage the uncertainties in the given reliability input date of the practical power system. In this paper, an algorithm is introduced to calculate the possibilstic reliability indices according to the degree of uncertainty in the given data. The probability distribution function can be transformed into an appropriate possibilstic representation using the probability-Possibility Consistency principle(PPCP) algorithm. In this the algorithm, the transformation is performation by making a compromise between the transformation consistency and the human updating experience. Fuzzy classifcation theory is applied to reduced the number of load data. The fuzzy classification method determines the closeness of load data points by assigning them to various clusters and then determening the distance between the clusters. The IEEE-RTS with 32-generating units is used to demonstrate the capability of the proposed algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1088-1095
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• 2015

In most cases, the use of electric power is associated with the economic scale of a nation closely. Thus, the electric power load forecasting plays an important role for the national economic plan. This paper deals with the design method for the electric power load forecasting system. In this paper, RCR-MA data processing, which can make the complex properties of the original data form simple, is proposed. Next, IT2TSK FLS, which can reflect the uncertainty of data more than T1TSK FLS, is applied. Consequently, the structural advantage of the proposed system can improve the forecasting accuracy, and is verified by using two types of electric power data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.479-486
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• 2004

The purpose of this study is to practical use with increase safety, usablility and economical. In this study, the property of fatigue behavior was tested by comparing reinforced concrete and steel fiber reinforced concrete. The basic test, the static test and fatigue test were used as the research methods. Basic on the test, the material compressive strength test and split tensile strength test ware conducted 7 days and 28 days after the concrete was poured. In the static test, there ware four types of experimental variables of the steel fiber mixing ratio : 0.00%, 0.75%, 1.00%, and 1.25%. The ultimate load initial diagonal tension crack, and initial load of flexural cracking were all observed by static test. A methodology for the probabilistic assement of steel fiber reinforced concrete(SFRC) which takes into account material variability, confinement model uncertainty and the uncertainty in local and globa failure criteria is applied for the derivation of vulnerability curves for the serviceability and ultimate limit states, the reliability of SFRC using the proposed practical linear limit state model is evaluated by using the AFOSM(Advanced First Order Second Moment) method and MCS(monte-Calrosimulation) method.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.53-63
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• 2017

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.1
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• pp.89-93
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• 2000

Robust control for DC motor is needed according to the highest precision of industrial automation. However, when a motor control system has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. Generally, it is known that sliding mode controller has robustness. But, after it is assumed that we known the disturbance uncertainty, sliding mode controller is designed. Thereafter, if we are not known th disturbance uncertainty then controller design is difficult. As a method solving this problem, in this paper, Expert sliding mode control method for motor control system is presented.The proposed controller can eliminate load disturbance effectively. The effectiveness of the control scheme is verified by simulation results.