• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.263-268
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• 2021

This paper statistically analyzes the time required for each failure cause and describes a diagnostic method for 159 reports of failure analysis of MV cables that occurred in the distribution system of KEPCO over the past 18 years. In addition, the manufacturer's failure rate compared to 100C-km was calculated using 381 cases of MV cable deterioration failure between 2008 and 2020. It is hoped that this paper will help those in charge of maintaining underground facilities at the business office to use the failure rate to prioritize facility diagnosis.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.301-307
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• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.309-316
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• 2021

For the detection of the state of charge in VRFB-ESS, the analyses of UV-Visible spectrometry and the measurements of potential between the anolyte and catholyte were used in parallel. This paper includes the production of 4-valant ion from VOSO4 powder, 3- and 5-valant ions from electrochemical charge of 4-valant ion and 2-valant ion from 3-valant ion. It also includes the analyses of these valance ions and unknown electrolyte at any time using UV-Visible spectrometry. Through the analyses of the valance ions in samples, the SOCs of the samples at any charge-discharge times were verified.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.49-53
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• 2022

The distribution live-line work method is an operation method of working in a state in which electricity flows through overhead distribution lines to minimize inconvenience to electric customers due to power failure. In June 2016, to strengthen the safety of electrical workers, Korea Electric Power Corporation announced that it would in principle abolish the rubber glove method, in which workers wore protective equipment such as rubber gloves and performed their maintenance work. In addition, KEPCO announced that it would develop a short-range live working method using smart sticks and an advanced live-line maintenance robot system where workers work without touching wires directly. This paper is a preliminary study for the development of the live-line maintenance robot system, and deals with the results of analyzing the technical feasibility of whether the live works performed by workers can be replaced by robots or not.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.97-101
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• 2022

In this study we investigated the gas to solid heat transfer of bubbling fluidized bed bottom ash cooler installed at the Donghae power plant in South Korea. Several different analyses are done through 1-D calculations and 3-D CFD simulation to predict the bottom ash exit temperatures when it exits the ash cooler. Three different cases are set up to have consideration of unburnt carbon in the bottom ash. Sensible heat comparison and heat transfer calculation between the fluidization air and the bottom ash are conducted and 3-D CFD analysis is done on three cases. We have obtained the results that the bottom ash with unburnt carbon is exiting the ash cooler, exceeding the targeted temperature from both 1-D calculation and 3-D CFD simulation.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.93-96
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• 2022

The boiler tubes of thermal power plants are exposed to harsh environment of high temperature and high pressure, and the deterioration state of materials rapidly increases. In particular, parent material and welds of the materials used are subjected to a temperature change and various constraints, resulting in deformation and its growth, resulting in frequent leakage accidents caused by tube failure. The power plant checks the integrity of boiler tubes through non-destructive testing as it may act as huge costs loss and limitation of power supply during power station shutdown period due to boiler tube leakage. However, the current non-destructive testing is extremely limited in the field to detect micro cracks. In this study, the ability of metal magnetic memory technique to detect flaws of size that are difficult to inspect by the visual or general non-destructive methods was verified in the early stage of their occurrence.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.87-92
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• 2022

Recently, there has been an increasing demand for electric equipment installed on the ground to be installed in an underground space. Accordingly, U-CDS (Underground-Compact Distribution Station) installed in the underground is supplied, and to improve its weak ventilation performance, an Airdam-type structure was applied and the effect was analyzed. As a result, the temperature around the transformer was reduced by up to 9.5 degrees, and the air flow increased by up to 1.17 m/s. Airdam structure can be supplied in the form of various sculptures because it is possible to design freely while maintaining its principle.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.61-68
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• 2022

In this paper, the change history of various types of MV (Medium Voltage) cables was investigated. In addition, the statistical life expectancy of each type was calculated by using the operation data and the failure data. For cut-off year, 10 years was applied, and realistically applicable statistical life expectancy was calculated by correcting the cause of failure entered by mistake. The life expectancy of FR-CNCO-W was calculated as 51.2 years, CNCV-W 38.1 years, and CNCV 31.4 years and the overall average is 33.8 years. Currently, the life expectancy of TR CNCV-W is 29.4 years, but it is estimated that the lifespan will be extended if failure data is accumulated. As a result, it is expected that life expectancy results can be applied to Asset Management System (AMS) in the future.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.137-141
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• 2022

To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.