• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.321-334
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• 2016

In this paper, we propose power t distribution based on t distribution. We also study the properties of and inferences for power t model in order to solve the problem of real data showing both skewness and heavy tails. The comparison of skew t and power t distributions is based on density plots, skewness and kurtosis. Note that, at the given degree of freedom, the kurtosis's range of the power t model surpasses that of the skew t model at all times. We draw inferences for two parameters of the power t distribution and four parameters of the location-scale extension of power t distribution via maximum likelihood. The Fisher information matrix derived is nonsingular on the whole parametric space; in addition we obtain the profile log-likelihood functions on two parameters. The response plots for different sample sizes provide strong evidence for the estimators' existence and unicity. An application of the power t distribution suggests that the model can be very useful for real data.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.241-245
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• 2011

In this paper, when photovoltaic (PV) systems are connected to power distribution system, most effective capacity and connected-point of PV system are presented considering power distribution system reliability. The reliability model of PV system is presented considering the duration of sunshine. Also the model of time-varying load and reliability test system bus2 model are used. To simulate the effects of PV system, various cases are selected; (1) base case which is no connection of PV system to power distribution system when faults are occurred, (2) 3MW case which is 3[MW] connection of PV system (3) 4[MW] case, and (4) 20[MW] case which is 20[MW] connection of PV system to the bus of power distribution system. The capacity limit of connected PV system is settled to 14[MW] for all cases except case 4. The reliability for residential, general, industrial, and educational customer is evaluated.

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.159-170
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• 2005

This paper presents the design and development of a scaled down physical model for power Distribution Automation (DA) system simulation. The developed DA system simulator is useful in providing hands-on experience to utility engineers / managers to familiarize with the DA system and gain confidence in managing the power distribution system from the computer aided distribution control center. The distribution automation system simulator can be effectively used to carry out further research work in this area. This also helps the undergraduate and graduate students to understands the power distribution automation technology in the laboratory environment. The developed DA simulator has become an integral part of a distribution automation lab in the Electrical Engineering Department at Indian Institute of Technology Kanpur in India.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.540-549
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• 2018

This paper proposes the method of active distribution network expansion planning considering distributed generation integration and distribution network reconfiguration. The distribution network reconfiguration is taken as the expansion planning alternative with zero investment cost of the branches. During the process of the reconfiguration in expansion planning, all the branches are taken as the alternative branches. The objective is to minimize the total costs of the distribution network in the planning period. The expansion alternatives such as active management, new lines, new substations, substation expansion and Distributed Generation (DG) installation are considered. Distribution network reconfiguration is a complex mixed-integer nonlinear programming problem, with integration of DGs and active managements, the active distribution network expansion planning considering distribution network reconfiguration becomes much more complex. This paper converts the dual-level expansion model to Second-Order Cone Programming (SOCP) model, which can be solved with commercial solver GUROBI. The proposed model and method are tested on the modified IEEE 33-bus system and Portugal 54-bus system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1270-1275
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• 2009

The structure of a power distribution system will change in a loop configuration such as in the case of a smart grid. If power distribution system changes radial to loop form, the structure may have to be changed significantly. Therefore, we analyzed the reliability indices and calculated a CIC(Customer Interruption Cost) for the loop power distribution system. The power distribution system reliability depends on the protection scheme. This study is applied to the current protection scheme method and is compared with each model. When the CIC was evaluated, most studies performed calculations only for sustained interruptions. However, in actuality, momentary interruption frequencies occurred more than sustain interruptions. Thus, it is occurred the CIC additively. Therefore, we evaluated a CIC including momentary interruption, for each model, and then compared with MAIFI(Momentary Average Interruption Frequency Index)

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.21-29
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• 2022

Power generated from renewable energy has continuously increased recently. As the distributed generation begins to interconnect in the distribution system, an accurate generation forecasting has become important in efficient distribution planning. This paper explained method and current state of distributed power generation forecasting models. This paper presented selecting input and output variables for the forecasting model. In addition, this paper analyzed input variables and forecasting models that can use as mid-to long-term distributed power generation forecasting.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.