• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.3 s.246
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• pp.193-200
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• 2006

The purpose of this study is to compare the part-load performance of a SOFC/GT hybrid power system with three different kinds of load-following operation modes. The primary mode for the part load operation of a hybrid power system is the reduction of supplied fuel (e.g., fuel control mode) to the hybrid system. The other two options, i.e., variable speed and VIGV controls, are related to the reduction of supplied air simultaneously with the reduction of supplied fuel to the system. With the performance analysis of a SOFC/GT hybrid power system, it is concluded that the variable speed con佐ol mode Provides the best performance for the part-load operations. It is also found that the VIGV control mode, with its better performance behavior than the fuel control mode, can be used as an important option for the part-load operation especially in case that the variable speed control mode can not be adopted.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.171-173
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• 1997

This study proposes a method for evaluating composite power system effective load duration curve(CMELDC) al load points. The concept of ELDC in power system planning is very useful and important in HLI as well as HLII. Effective load duration curve of composite power system can be obtained from convolution integral processing or both probability function of unarrival power and load duration curve at load points. The characteristics and effectiveness of this methodology arc illustrated by case studyings of simple system.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.83-88
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• 2016

This paper describes the test results of plant minimum stable load (Pmin) for Ilijan Power Plant. The test was conducted on May 13 through 14, 2015 to investigate the plant operating and equipment condition in accordance with "Ilijan Plant Performance Test Procedure on Plant Minimum Stable Load" [1]. This paper also contains the assessment of the impact of Pmin to plant operating parameters and possible technical operating issues when operating at lower loads and to recommend the safe minimum load operation of Ilijan per block. In addition, this paper describes the performance calculation results of efficiency and heat rate depending on the load level.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.34-37
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• 2003

The frequency of power system will change when the load-generation equilibrium is disturbed. Insufficiency of generation from the imbalance between load and generation decreases the power system frequency. In case of the severe emergency, the under frequency load shedding scheme is applied for the power system defense plan. In this paper, we analyzed the dynamic characteristics of under frequency load shedding using new Transient Security Assessment Tool ; TSAT. We applied the actual UFLS scheme to these studies and considered the possible contingency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.253-256
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• 2016

In this paper, the power system with electric vehicles is analyzed considering the mobility and diffusion rate of electric vehicles in the smart grid environment. In the previous studies, load modeling and load composition rates have been researched and the results are applied to develop a new load model to explain the mobility of electric vehicles which could affect on the power system status such as power flow and stability. The results would be utilized to research and develop power system analysis methods considering movable charging characteristics of electric vehicles including movable discharging characteristics which could be affected by the diffusion progress of electric vehicles.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.227-228
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• 2013

The paper is related to smart maximum power system based on program logic. Especially, this system compares the total demand power with the target power by using the signal from the digital kilo watt meter. Based on the power information by the maximum power control equipment the consumed future power is anticipated. In addition, through consumed future power the controllable target power is set, and it applies on the maximum power control equipment. User or manager would control the load efficiently through the simple programming which could control load based on the control sequence and relay. So this system could control load more efficient and stable. Also the conventional load control circuit is not needed. Therefore, it is possible to improve the simple system configuration, which is resulted in cost effective and time saving. So this system is anticipated on time and coast.

• Journal of Power Electronics
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• v.16 no.2
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• pp.695-703
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• 2016

A fuel cell power conditioning system for online diagnosis and load leveling under the condition of varying load is developed in this study. The proposed system comprises a unidirectional boost converter and a bidirectional buck-boost converter with a battery. The system operates in two different modes. In normal mode, the bidirectional converter is utilized for load leveling; in diagnostic mode, it is utilized to control load voltage while the boost converter generates perturbation current to implement the online diagnosis function through in-situ electrochemical impedance spectroscopy (EIS). The proposed method can perform EIS for a fuel cell under varying-load conditions with no influence on the load. The validity and feasibility of the proposed system are verified by experiments, and the design procedure of the proposed system is detailed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.96-101
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• 2008

The field of load modeling has attracted the attention since it plays an important role for improving the accuracy of stability analysis and power flow estimation. Also, load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, precision load modeling technique and reasonable application method is needed for more accurate power system analysis. In this paper, we develop an intelligent load modeling method based. on neural network and application techniques for power system. The proposed method makes it possible to effectively estimate the load model for nonlinear models as well as linear models. Reasonable application method is also proposed for stability analysis. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.