Invisible threats: An investigation of electrical hazards and safety practices among residential electricity consumers

Kulor, Frank; Apprey, Michael W.; Agbevanu, Kafui T.; Gasper, Gabriel K.; Akorta, Judith A.

doi:10.1016/j.heliyon.2024.e34470

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e34470

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Invisible threats: An investigation of electrical hazards and safety practices among residential electricity consumers

Frank Kulor,

Michael W. Apprey,

Kafui T. Agbevanu

et al.

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2024

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Cited by 3 publications

References 14 publications

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Electrical Home Fire Injuries Analysis

Taylor,

Fielding,

O’Boyle

2024

Fire

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Domestic electrical fires can occur for a variety of reasons, including faulty wiring and plugs, overloaded circuits, and malfunctioning electrical appliances. In this article, the circumstances of domestic electrical fire injuries between 2011 and 2022 in the county of Merseyside in Northwestern England were examined in order to inform fire prevention activities. Householder carelessness, such as cooking, or smoking, appeared to be less of a factor in electrical fire injury compared to other types of fire injury. Faulty electricity supplies were the main cause of electrical fire injuries. Male residents were slightly more likely to sustain injury in an electrical fire in comparison to females (1.25 to 1). Those aged 75+ appeared to be more at risk of electrical fire injuries compared to other age groups.

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Electrical Home Fire Injuries Analysis

Taylor,

Fielding,

O’Boyle

2024

Fire

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A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model

Qi,

Kawaguchi,

Hashimoto

2024

Processes

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In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention.

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Electrical Installation Safety Assessment of Buildings in Kumasi, Ghana

Mante,

Sekyere,

Asabere

et al. 2024

JPEE

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Invisible threats: An investigation of electrical hazards and safety practices among residential electricity consumers

Cited by 3 publications

References 14 publications

Electrical Home Fire Injuries Analysis

Electrical Home Fire Injuries Analysis

A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model

Electrical Installation Safety Assessment of Buildings in Kumasi, Ghana

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