• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.322-326
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• 1986

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.6
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• pp.306-314
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• 2005

In this paper, a new approach that is based on EEAC & only with network solutions for CS&S in the transient stability assessment is developed. The proposed CS&S algorithm in conjunction with EEAC to include the capability of performing on-line TSA without TDS is used to calculate the critical clearing time for stability index. In this algorithm, all generators are represented by classical models and all loads are represented by constant impedance load models. The accelerating & synchronizing power coefficient as an index is determined at its disturbance through solving network equation directly. As mentioned above, a new index for generator is generally used to determine the critical generators group. The generator rotor angle is fixed for non-critical generators group, but has equal angle increments for critical generators group. Finally, the critical clearing time is calculated from the power-angle relationship of equivalent OMIB system. The proposed CS&S algorithm currently being implemented is applied to the KEPCO system. The CS&S result was remarkably similar to TSAT program and SIME. Therefore, it was found to be suitable for a fast & highly efficient CS&S algorithm in TSA. The time of CS&S for the 139 contingencies using proposed CS&S algorithm takes less than 3 seconds on Pentium 4, 3GHz Desktop.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1658-1666
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• 2009

This paper proposes an approach of Mult_HPSO based on the PC cluster system to reduce or remove the stagnation on an early convergence effect of PSO, reduce an execution time and improve a search ability on an optimal solution. Hybrid PSO(HPSO) is combines the PSO(Particle Swarm Optimization) with the mutation of conventional GA(Genetic Algorithm). The conventional PSO has operated a search process in a single swarm. However, Multi_PSO operates a search process through multiple swarms, which increments diversity of expected solutions and reduces the execution time. Multiple Swarms are composed of unsynchronized PC clusters. We apply to SCED(security constrained economic dispatch) problem, a nonlinear optimization problem, which considers line flow constraints and N-1 line contingency constraints. To consider N-1 line contingency in power system, we have chosen critical line contingency through a process of Screening and Selection based on PI(performace Index). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed approaches.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1334-1341
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• 2008

This paper introduces an approach of Hybrid Particle Swarm Optimization(HPSO) for a security-constrained economic dispatch(SCED) with line flow constraints. To reduce a early convergence effect of PSO algorithm, we proposed HPSO algorithm considering a mutation characteristic of Genetic Algorithm(GA). In power system, for considering N-1 line contingency, we have chosen critical line contingency through a process of Screening and Selection based on PI(performance Index). To prove the ability of the proposed HPSO in solving nonlinear optimization problems, SCED problems with nonconvex solution spaces are considered and solved with three different approach(Conventional GA, PSO, HPSO). We have applied to IEEE 118 bus system for verifying a usefulness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.10
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• pp.360-366
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• 1983

In this paper, operation cost of the system is calculated by the probabilistic simulation method. And it is proved that only 20 iterative simulations are enough to get the result obtain by the Monte Carlo simulation method which requires more than 1000 iterative simulations. In the probabilistic simulation method we use the ranking of line contingency which is derived from the line countingency selection algorithm proposed in (2). In using this method the nature of the sparsity of the power system is used.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.118-121
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• 1987

This paper presents a rule-based approach for automatically selecting critical contingencies in electric power systems. The rules required to perform the task are derived from inspection about results of simulation and expertise of operators. And inherent information of system, for example, topology of system configuration, and flow direction in a line by compensation theorem. etc., which are independent of operating point of system, is stored in the database using the off-line calculation. The approach was investigated using the study of a sample test system. Since it is based on the knowledge engineering technique, efficiency of selection can be improved by updating and adding the rules.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.167-176
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• 2008

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.698-701
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• 2004

In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.627-641
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• 2012

We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.441-448
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• 2005

On-line dynamic security assessment is becoming more and more important for the stable operation of power systems as load level increases. The necessity is getting apparent under Electricity Market environments, as operation of power system is exposed to more various operating conditions. For on-line dynamic security assessment, fast transient stability analysis tool is required for contingency selection. The TEF(Transient Energy Function) method is a good candidate for this purpose. The clustering of critical generators is crucial for the precise and fast calculation of energy margin. In this paper, we propose a new method for fast decision of mode of instability by using stability indices. Case study shows very promising results.