• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.848-859
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• 2016

System failures in thermal power plants (TPPs) can lead to serious losses because the equipment is operated under very high pressure and temperature. Therefore, it is indispensable for alarm systems to inform field workers in advance of any abnormal operating conditions in the equipment. In this paper, we propose a clustering-based fault detection method for steam boiler tubes in TPPs. For data clustering, k-means algorithm is employed and the number of clusters are systematically determined by slope statistic. In the clustering-based method, it is assumed that normal data samples are close to the centers of clusters and those of abnormal are far from the centers. After partitioning training samples collected from normal target systems, fault scores (FSs) are assigned to unseen samples according to the distances between the samples and their closest cluster centroids. Alarm signals are generated if the FSs exceed predefined threshold values. The validity of exponentially weighted moving average to reduce false alarms is also investigated. To verify the performance, the proposed method is applied to failure cases due to boiler tube leakage. The experiment results show that the proposed method can detect the abnormal conditions of the target system successfully.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.55-60
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• 2019

Corrosion failure in the convection part of peak load boiler (PLB) of the district heating system led to water leakage. Herein, Internal Rotary Inspection System (IRIS) inspection was employed to examine wall thinning and the cause of leakage in the flue tube. The corrosive products of the turbulator and tube were investigated using scanning electron microscope combined with energy dispersive spectroscopy, X-ray diffraction, and inductively coupled plasma (ICP). Majority of the serious corrosion damage was observed near the turbulator located in the upper flue tube. ICP analysis of the boiler water revealed oxide formation of sodium chloride in the lower end part of the flue tube. A cross-sectional view of the turbulator revealed the presence of double-layers of the oxide film, indicating environmental change during operation associated with water leakage. The outer surface of the turbulator consisted of the acid oxides such as $NO_x$ and $SO_x$ along with sodium and chloride ions. Dew-point corrosion is hypothesized as the main cause for the formation of acid oxides in the region of contact of the flue tube and the turbulator.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.172-175
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• 2008

In the last five years, 45 people died and 104 were wounded because of carbon monoxide poisoning accident. CO poisoning accident is higher than any other gas accident in the rate of deaths/incidents. Most of these CO poisoning accidents were caused by defective exhaust tube in the old gas boiler and multi-use facility. In this study, the spread of CO gas released from leakage hole of exhaust tube was analyzed by computational flow modeling and concentration measuring test. CO gas leaked form exhaust tube in a building was highest concentrated near the ceiling and formed the circular currents along the walls. Through these experiments and simulation, the reasonable installation location of CO alarm was made certain and suggested.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2868-2873
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• 2008

Carbon Monoxide(CO) poisoning accident is higher than any other gas accident in the rate of deaths/incidents. In the last five years, 36 people died and 104 were wounded because of carbon monoxide poisoning accident. Most of these CO poisoning accidents were caused by defective exhaust tube in the old gas boiler and multi-use facility. In this study, the spread of incomplete combustion gas(CO) released from leakage hole of exhaust tube was analyzed by computational flow modeling and concentration measuring test. CO gas leaked form exhaust tube in a building was highest concentrated near the ceiling and formed the circular currents along the walls. Through these experiments and simulation, the reasonable installation location of carbon monoxide alarm was made certain and suggested.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.181-187
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• 2016

A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (ⵁ2, ⵁ5, ⵁ10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⵁ2 mm and ⵁ5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.259-264
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• 2009

Most of the fossil power plants firing lower grade coals are challenged with maintaining good combustion conditions while maximizing generation and minimizing emissions. In many cases significant derate, availability losses and increase in unburned carbon levels can be attributed to poor combustion conditions as a result of poorly controlled local fuel and air distribution within the boiler furnace. The poor combustion conditions are directly related to the gas flow deviation in upper furnace and convection tube-bank but a less reported issue related to in large-scale oppose wall fired boilers. In order to develop a on-line combustion monitoring system and suggest an alternative heat flux estimation method at tube bank, which is very useful information for boiler design tool and blower optimizing system, field test was conducted at operating power boiler. During the field test the exhaust gases' temperature and tube metal temperature were monitored by using a spatially distributed sensors grid which located in the boiler's high temperature vestibule region. At these locations. the flue gas flow is still significantly stratified, and air in-leakage is minimal which enables tracing of poor combustion zones to specific burners and over-fire air ports. Test results showed that the flue gas monitoring method is more proper than metal temperature distribution monitoring for real time combustion monitoring because tube metal temp. distribution monitoring method is related to so many variables such as flue gas, internal flow unbalance, spray etc., Heat flux estimation at the tube bank with flue gas temp. and metal temp. data can be alternative method when tube drilling type sensor can't able to use.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.55-56
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• 2018

화력발전소의 중요 구성품인 보일러 튜브의 예기치 못한 누설 사고로 인해 수억원에 해당하는 손실이 발생하고 있다. 본 논문에서는 보일러 튜브의 누설 감지를 위해 유전 알고리즘을 이용하여 추출 가능한 특징들 중 누수 감지에 유용한 특징들을 선택하고, 선택된 특징으로 서포트 벡터 머신을 이용하여 보일러 튜브의 누설 감지하는 방법을 제안한다. 이는 뛰어난 성능을 보였으며, 향후 본 기술을 이용하면 발전소의 손실 예방에 크게 도움이 될 것으로 기대된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.