• 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.

• Fire Science and Engineering
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• v.29 no.2
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• pp.1-7
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• 2015

Tracking, which is one of main reasons of the electric fire, progresses gradually, and therefore, the possibility of fire caused by tracking can be predicted by analyzing the stage of its progress. This paper is conducted in order to predict possibility of the electric fire caused by the tracking in the simulated electric equipment with load. Non-inductive resistance is used as the load. The tracking is happened in a Polyvinyl-chloride-sheathed flat cord, which is a part of the simulated electric equipment by means of dropping of electrolyte droplet. In order to predict the possibility of electric fire caused by tracking, we detect the whole current waveforms of the simulated electric equipment. The time-energy analysis and probability distribution are used for analysis of the tracking progress from the whole current waveforms. In accordance with the results is used for input date of Neural networks, the neural networks can be predict possibility of the electric fire in the electric equipment by 4 stages.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.85-90
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• 2013

Currently, the electric fire prediction system uses PIC(Peripheral Interface Controller) for controller microprocessor. PIC has a slower computing speed than DSP does, so its real-time computing ability is inadequate. So with the basic characteristics waveform during arc generation as the standard reference, the comparison to this reference is used to predict and alarm electric fire from arc. While such alarm can be detected and taken care of from a remote central server, that prediction error rate is high and remote control in mobile environment is not available. In this article, the arc detection of time domain and frequency domain and wavelet-based adaptation algorithm executing the adaptation algorithm in conversion domain were applied to develop an electric fire prediction system loaded with new real-time arc detection algorithm using DSP. Also, remote control was made available through iPhone environment-based app development which enabled remote monitoring for arc's electric signal and power quality, and its utility was verified.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• 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%.

• 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 Institute of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.41-48
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• 2010

In this paper, a new type of integrated AFCI is designed and implemented by combining individual circuit breaker characteristics, which can effectively detect arc fault signals in real-time. The proposed integrated AFCI satisfies the UL1699, the USA certification standard, and the Korean circuit breaker standards such as KS C4613 and KS C8321. Data signals are transferred to the management server via ZigBee network to analyze dangerous factors and to prevent unwanted trip. It is also shown by experiments that arc fault signals are detected and analyzed by using the integrated AFCI with ZigBee networks.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.189-193
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• 2013

In this paper, in order to predict and prevent electrical fires that occur in the power system, we measured the informations of electric power, and then proposed a system to predict the electrical fire using these informations. To this end, we analyzed the correlations for over-current, overload and overheating. These states are caused by the grounding current and the leakage current, and are the main causes of an electrical fire. Use these correlations to derive the derivative of the fuzzy rules for membership function. The designed algorithm was simulated by utilizing the informations of the actual power of the switchgear-panel.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.73-74
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• 2023

본 논문에서는 음향센서를 기반으로 한 지하시설물 화재 위험감지 방법을 제안하였다. 음향센서는 진동이나 광센서처럼 접촉식이 아니기 때문에 결로가 발생하고 있는 취약구간에 설치하여 보다 효율적으로 활용이 가능하고 지하시설물 내부에 설치된 기기나 장비들과 상호작용하거나 간섭하지 않기 때문에 안전하게 관리가 가능하다. 이러한 특징으로 지하 시설물에서 내 통행이 불편하여 관리하기 힘든 구간이나 결로가 많아 화재안전에 주의가 필요한 곳에 설치하여 전기스파크 발생 감지를통해 재난이 발생하기 이전 화재위험을 감지하는 방법론 중 하나가 될 수 있다. 제안하는 방법은 음향 센서를 통해 지하공동구 안에서 발생하는 소리들을 수집하고 일정한 길이의 시간 단위 프레임들로 분할한 후 분석하여 전기스파크의 특징 벡터를 도출한다. 전기스파크 감지 모델로는 전기스파크 신호의 지역적 특성을 포착할 수 있도록 2D-CNN 구조를 사용하며 모델에서 출력된 전기 스파크 발생 예측확률을 분할된 단위 프레임 따라 계산하여 융합한다. 이로 인해 높은 정확도의 전기스파크 감지 정밀도를 얻을 수 있으며, 이는 전기 스파크에 의한 화재 이벤트 감지 있어서 효과적인 센싱 기술임을 알 수 있다.