• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.413-419
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• 2004

This paper describes a technique that can predict electric fires by detection of discharge voltage signals caused by the use of electric facilities. In the experiment, various discharge modes, a flashover or a surface discharge through insulation paper and a line to line short, were simulated to acquire electrical information for predicting electrical fire as discharge modes. From the experimental results, it is hewn that electorial discharges which are ranked as majority causes of electric fires generate characterized signals distinguished from power frequency. Finally. We designed a prototype discharge detector based on the experimental results, and the detector is applied to a power lines. This study showed that the prediction of electric fires is possible by monitoring discharge voltage signals in electric power lines.

• Smart Media Journal
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• v.9 no.1
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• pp.38-44
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• 2020

Due to the high dependence on electric energy, electric fires make up a significant portion of fires in Korea. Electric arcs by short circuits or poor contact cause three of four electrical fires. An electric arc is a discharge phenomenon of electrical current between the insulators, which instantaneously produces high temperature. In order to reduce the fire due to electric arc, this study aims to predict the electric arc risk. We collected arc data from the arc detectors and converted into graphs based on temporal arc data. We used machine learning for training converted graph with different number of temporal arc data. To measure the performance of the learning model, we use the test data. In the results, when the number of temporal arc data was 20, the prediction rate was high as 86%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.371-374
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• 2001

This paper describes a technique that can predict electric fires by detecting a spark signal generated from operation of electric facilities. An electric fire lead a loss of life as well as huge property, therefore it is very Important to predict an electric fire and eliminate the causes of it. Electrical spark which is ranked as majority causes of electric fires has a characterized frequency bandwidthdistinguishedfrompowerfrequenry. In the experiment, various spark signals are simulated in a condition such as short circuit, flashover and surface discharge. The results showed that the monitoring of spark signals can predict electric fires.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.223-229
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• 2022

During the winter season, when the weather gets colder every year, electricity consumption increases rapidly. The occurrence of fires is increasing due to a short circuit in electrical facilities of buildings such as markets, bathrooms, and apartments with high population density while using a lot of electricity. The cause of these short circuit fires is mostly due to the aging of the wires, the usage increases, and the excessive load cannot be endured, and the wire sheath is melted and caused by nearby ignition materials. In this paper, the load and overheat generated in the electric wire are measured through a complex sensor composed of an overload sensor, a VoC sensor, and an overheat sensor. Based on this, big data analysis is carried out to develop a platform capable of predicting, alerting, and blocking electric fires in real time, and a simulator capable of simulated fire experiments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.749-755
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• 2022

Electricity is essential energy in modern society, such as being used in various industries. However, the rate of fires occurring on electric wiring to deal with it is very high. In this work, we implemented a system to predict the temperature change of an electric circuit through analysis using various regression models. To do so, we collected the temperature data of 27 types of magnetic switches which control electric circuits as well as trained the regression models by using the collected temperature data. In our experiments, we confirmed that the regression models can be trained at a sufficiently usable level since the difference between the actual temperature and predicted temperature is about 4℃. The results of our work will be useful to predict the temperature of electric circuits and preventing fires on them.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.15-16
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• 2015

According to 2014 fire statistical yearbook in the National Fire Data System, a main cause of fire is electrical fire except carelessness fire. Joint/contact badness is the one of the main cause of electrical fire. Furthermore, power relays which are used in electric panel board, motor control center and automation controller, are main element of automation system in the industry field. Overload, voltage unbalance and open-phase due to joint/contact badness of terminal make electric accidents or electrical fires. In order to prevent joint/contact badness of terminal, this paper proposes a sensing circuit of chattering, tracking, arc current, voltage unbalance and open-phase etc. Some experimental tests of the proposed apparatus confirm practicality and validity of the theoretical results.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.36-41
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• 2012

Electrical Connections often cause fires due to poor contact. Occurrence rate of these fires tends to increase annually. The reason why poor contacts occur is often because it is the low mechanical pressure at the contact points. A typical connection method using mechanical pressure is a screw terminal type. This study reviewed these poor contact cases in the screw terminals. In order to get reproduction of such cases, two types of experiments were conducted. the first one was conducted under the normal contact condition, and the other one was conducted under the poor contact condition that screw terminal of breaker was loosen and did not meet the requirements of toque value. In both types of experiments, compulsory vibration was adopted as a variable to aggravate poor contacts. Each of various current values(4.5A, 9.0A, 13.5A) is input. In these experiments, relationships of a contact, electrical signal such as current and electric pulse by ZCT and thermal characteristics according to vibration effect are analyzed. The suggested data and results in this study provide the useful resources helping to investigate fires due to poor contact, and they develop the detector for poor contact and finally reduce the electrical fire occurrence rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.255-257
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• 2021

During the winter season, when electricity usage increases rapidly every year, fires are frequent due to short circuits in aging electrical facilities in multi-use facilities such as traditional markets and jjimjilbangs, apartments, and multi-family houses. Most of the causes of such fires are caused by excessive loads applied to aging wires, causing the wire covering to melt and being transferred to surrounding ignition materials. In this study, we implement a system that measures the overload and overheating of the wire through a composite sensor, detects the toxic gas generated there, and logs it to the server through the gateway. Based on this, we will develop a platform that can predict, alarm and block electric fires in real time through big data analysis, and a simulator that can simulate fire occurrence experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.603-610
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• 2018

The development of industry and the increase in the use of fossil fuels have accelerated the process of global warming and climate change, resulting in more frequent and intense natural disasters than ever before. Since electricity facilities are often installed outdoors, they are heavily influenced by natural disasters and the number of related accidents is increasing. In this paper, we analyzed the statistical status of domestic electrical fires, electric shock accidents, and electrical equipment accidents and hence analyzed the risk associated with climate change. Through the analysis of the electrical accidental data in connection with the various regional (metropolitan) climatic conditions (temperature, humidity), the risk rating and charts for each region and each equipment were produced. Based on this analysis, a basic electric risk prediction model is presented and a method of displaying an electric hazard prediction map for each region and each type of electric facilities through a website or smart phone app was developed using the proposed analysis data. In addition, efforts should be made to increase the durability of the electrical equipment and improve the resistance standards to prevent future disasters.

• Journal of the Korean Wood Science and Technology
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• v.48 no.6
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• pp.917-935
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• 2020

In this study, an automated sensor to measure forest fire surface fuel moistures was developed to predict changes in the moisture content and risk of forest fire surface fuel, which was indicators of forest fire occurrence and spread risk. This measurement sensor was a method of automatically calculating the moisture content of forest fire surface fuel by electric resistance. The proxy of forest fire surface fuel used in this sensor is pine (50 cm long, 1.5 cm in diameter), and the relationship between moisture content and electrical resistance, R(R:Electrical resistance)=2E(E:Exponent of 10)+13X(X:Moisture content)-9.705(R²=0.947) was developed. In addition, using this, the software and case of the automated measurement sensor for forest fire surface fuel moisture were designed to produce a prototype, and the suitability (R²=0.824) was confirmed by performing field monitoring verification in the forest. The results of this study would contribute to develop technologies that can predict the occurrence, spread and intensity of forest fires, and are expected to be used as basic data for advanced forest fire risk forecasting technologies.