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Document Type

Original Article

Abstract

The paper discusses a technique for detecting electrical fires in residential buildings using the Gradient Boosting Machine (GBM) algorithm. The features of the algorithm processes data related to current, voltage, and total harmonic distortion from electrical systems, considering resistive loads, such as heating appliances, and inductive loads, like refrigerators and washing machines. The technique underscores the relationship between electrical characteristics and fire risks, demonstrating that the gradient boosting machine can accurately predict fire hazards under various fault conditions, including arc faults, overvoltage, and contact opening. Results from MATLAB simulations confirm the algorithm's efficacy, and high accuracy rates for heating systems and induction motors across different fault types that could lead to electrical fires in buildings. These results highlight the significance of effective feature selection in enhancing the algorithm's performance while addressing some imprecision, particularly regarding the two different load types. Ultimately, the Gradient Boosting Machine represents a promising approach to improving the safety of electrical systems and supporting fire detection strategies.

Receive Date

30/01/2025

Revise Date

20/04/2025

Accept Date

18/05/2025

Publication Date

6-16-2025

References

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