• The Journal of Korean Academy of Prosthodontics
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• v.29 no.1
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• pp.233-248
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• 1991

The purpose of this study was to qunatatively analyze the stress patterns induced in the abutment, superstructure, supporting bone and to determine the deflection of abutment and superstructure by appling occlusal force to natural teeth supported fixed prostheses and implant-supported fixed prostheses. The analysis has been conducted by using the two dimensional finite element method. The implant and natural tooth-supported bridge has a first molar pontic supported by mandibular second bicuspid and implant posterior retainer, which were rigidly(Model A) or flexible(Model B). The natural teeth-supported bridge has a first molar pontic supported by mandibular second bicuspid and second molar, which were rigidly splinted together(Model C). 63.5kg(Load P1) of localized load on central fossa of first molar pontic and 24kg(Load P2) of distributed load on each occlusal surface were applied respectively. 1. The coronal portion of premolar pontic and posterior abutment in fixed partial denture deflected inferiorly in order of Model B, Model C and Model A under Load P1 and Load P2. 2. Mesial displacement of the coronal portion of premolar showed in Model A, Model B and Model C under Load P1, but mesial displacement of that in Model B and distal displacement of that in Model A and Model C showed under Load P2. 3. Mesial displacement of the coronal portion of the pontic and distal displacement of the coronal portion of posterior abutment showed in Model A, Model B and Model C under Load P1 and Load P2. Displacement in the case of Model B was greater than that of Model A and Model C. 4. In the case Model A under Load P1 and Load P2, high stress apically was concentrated in the mesiocervical portion of the posterior abutment than in the disto-cervical portion of the premolar. 5. In the case of Model B under Load P1 and Load P2 high stress was concentrated in the case of the premolar than in that of posterior abutment and high stress especially was concentrated in the connected portion of pontic and posterior abutment. 6. In the case of Model C under Load P1 and Load P2, high stress was concentrated in the distal area of the cornal portion of premolar and the mesial area of the coronal portion of posterior abutment, and stress pattern was anteroposterially symmetric around the pontic. 7. Load P1 and Load P2 compared, stress magnitude was different but stress pattern was similar in Model A, Model B and Model C. 8. Under Load P1 and P2, stress magnitude in the mesial distal portion and the portion of root apex of the posterior abutment was in order of Model B, Model A and Model C.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.845-851
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• 2012

Power system loads have a significant impact on a system. Although it is difficult to precisely describe loads in a mathematical model, accurately modeling them is important for a system analysis. The traditional load modeling method is based on the load components of a bus. Recently, the load modeling method based on measurements from a system has been introduced and developed by researchers. The two major components of a load modeling problem are determining the mathematical model for the target system and estimating the parameters of the determined model. We use the composite load model, which has both static and dynamic load characteristics. The ZIP model and the induction motor model are used for the static and dynamic load models, respectively. In this work, we propose the measurement-based parameter estimation method for the composite load model. The test system and related measurements are obtained using transient security assessment tool(TSAT) simulation program and PSS/E. The parameter estimation is then verified using these measurements. Cases are tested and verified using the sample system and its related measurements.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.17-24
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• 2019

In this paper, we analyzed the effect of power system load model on the short-term voltage stability analysis results. First, we introduced common load models. We also confirmed that some load models can not represent actual system phenomena even if the model parameters are optimized. Also, we studied about the influences of load parameters and regional characteristics of load model on the sort-term voltage stability of KEPCO power system considering the contingency. The results showed that the importance of selecting a load model was confirmed again. And we recognized about it can be understood that it should reflect the load characteristics of the area near the assumed contingency more accurately.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.111-117
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• 2008

This paper presents a new continuation-based quasi-steady-state(CQSS) time-domain simulation algorithm incorporating a multiplicative aggregated load model for power systems. The authors' previous paper introduced a CQSS algorithm, which has the robust convergent characteristic near the singularity point due to the application of a continuation method. The previous CQSS algorithm implemented the load restoration in power systems using the exponent-based load recovery model that is derived from the additive dynamic load model. However, the reformulated exponent-based model causes the inappropriate variation of short-term load characteristics when switching actions occur, during time-domain simulation. This paper depicts how to incorporate a multiplicative load restoration model, which does not have the problem of deforming short-term load characteristics, into the time simulation algorithm, and shows an illustrative example with a 39-bus test system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.399-399
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.309-405
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• KIEE International Transactions on Power Engineering
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• v.2A no.4
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• pp.136-144
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• 2002

Load models are very important for improving the accuracy of stability analysis and load flow studies. Various loads are connected to a power bus and their characteristics of power consumption change with voltage and frequency. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this work, artificial neural networks-ANNs- were used to construct the component load models for more accurate modeling. A typical residential load was selected and subjected to a test under variable voltage/frequency conditions. Acquired data were used to construct component models by ANNs. The aggregation process of separately determined load models is also presented in the paper. Furthermore, this paper proposes a method to transform a single load model constructed by the aggregation method into a mathematical load model that can be used in traditional power system analysis software.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.12
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• pp.1088-1094
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• 2003

Case study was peformed to analyze energy load for department stores and develop energy load model to be applied to a cogeneration system. Energy loads of 14 departments were analyzed based on energy load sheets written by operators and energy load of one department store was measured through modem communication for a year. Energy load of department stores shows various variations depending on when they are opened or closed and, hours, days and months. In this paper, the measurement was compared with data in energy load sheets and resolved, and energy load model for a department store was built. It is important to use an accurate energy load model for an accurate feasibility study applying a cogeneration system to buildings.