• International Journal of Safety
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• v.6 no.2
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• pp.17-21
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• 2007

This paper aims at the statistical analysis of electrical fire and classification of electrical fire causes to collect electrical fires data efficiently. Electrical fire statistics are produced to monitor the number and characteristics of fires attended by fire fighters, including the causes and effects of fire so that action can be taken to reduce the human and financial cost of fire. Electrical fires make up the majority of fires in Korea(including nearly 30% of total fires according to recent figures), The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire fighters directly ticking the appropriate box on the fire report form or making an assessment of a text description. Therefore, it is highly recommended to develop electrical fire cause classification and electrical fire assessment on the fire statistics in order to categorize and assess electrical fires exactly. In this paper newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also fire statistics systems of foreign countries are introduced and compared.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.53-57
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• 2005

This paper presents development of a classification system for an electrical fire investigation. In order to reduce an electrical fires and establish detailed prevention plans, the collection of an electrical fire causes and base data are very important. Based on this data, a new classification system for an electrical fire investigation was developed and the direction to the classification system was suggested by fundamental analysis. All of the collected information is analyzed by bottom-up method. Criteria items which based on base data were categorized to classify items. The classification of items were found out as follows : basic condition fire scene condition, fire sign, fire cause. Particularly, the fire cause category is classified. A new developed classification system for an electrical fire investigation will be used to analyse electrical fires easily and efficiently.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.466-471
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• 2007

Electrical fires form over 30 percent of fires, but the study on the reliability of electrical fire statistics is not performed. Electrical roe occupancy was very high due to investigating and classifying fires, which is not directly continuous with electrical cause, as electrical fire because insufficiency of cause classification method or system, and the problems of the reliability of electrical fire statistics were presented. So, the reliability of electrical fire statistics must be guaranteed by improvement of the existing cause classification method of electrical fire. This paper analyzed the problems of electrical rue statistics by the existing cause classification method of electrical fire and presented the new method to classify causes of electrical fire.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.405-411
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper propolsed the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of Preventive Medicine and Public Health
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• v.1 no.1
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• pp.43-49
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• 1968

Epidemiological and statistical observations were made of fire hazards that occurred during the past 18 years, 1948 to 1965. Injury and mortality rates for all ages were computed chronologically. For the years of 1955, 1961 and 1965, all fire accidents were epidemiologically analysed to draw characteristic patterns in relation to the seasonal and 24 hour distribution, causes and sites of accidents etc.. Fire hazards observed herein are the categorys E 916 of the International Classification of Causes of Death, 1955, and includes all accidents caused by fire and explosion of combustible materials. The following conclusion was made: 1. The average number of annual deaths due to fire was 183 and the number of the in jured due to the same cause was 335. The mortality rate per 100,000 population was 0.8 and the ratio of injuries per death was 1.83. 2. The casually rate including both the dead and injured was 5.0 per 100,000 in Seoul, the highest among the provinces and followed by 3.4 in Cheju -Do, 2.1 in Kangwon-Do, 1.7 in Kyunggi-Do accordingly. The other provinces had a range of 0.6 to 1.2. 3. The monthly distribution of fro accidents showed that the winter months, December through February, had more frequent accidents, while the summer season, June through August had less. The 24 hour distribution of accidents showed more cases from 12:00 to 18:00 and less from 4:00 to 10:00 hours. 4. The per cent distribution of causes of accidents showed; 90.0% for careless, 10.0% for arson. The cause of carelessness was further breakdown into; 15.0% for kitchen fire places, 13.8% for fire playing, 9,4% for electrical heating and wires, 8.3% for fuels, 6.3% for matches, 5.2% for ash dumps and the remaining for others. 5. The accidents as classified by place revealed that 56.8% of the total occurred at the common dwelling houses, 11.3 at various industrial workshops, 9.3% at the street shops and the remaining at the miscellaneous places.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.366-368
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper proposes the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of the Korea Institute of Construction Safety
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• v.1 no.1
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• pp.31-39
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• 2018

It is reported that about 20 % of all fires in Korea are caused by the electric equipment and installations. In complex and large-scale buildings, the sizes of electric fires are becoming larger as property damage and casualties increase. Among the causes of various electric fires, fire by short circuit accounts for about 71.5% of overall fires, and in the classification by electric equipment and installation, fire caused by wiring and wiring equipment accounts for approximately 38.3% of overall fires. The purpose of this study is to propose methods to prevent electric fires due to short circuit by improving indoor wiring currently in use and to find the fundamental measures to prevent wiring equipment caused fires by improving the socket and plug which are commonly used in wiring equipment. It is expected that the electric fire prevention measures presented through this study can be used as a measure to protect many people and properties by eliminating the root cause of electric fire.