BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires

Abstract

Cable overload is one of the most critical contributors to early cable fires. This study proposes a hybrid Bayesian network (BN)-based fire risk analysis model, to investigate the evolution of overload-induced early cable fire risks. In particular, the fire risk transmission paths caused by cable overload are reported, considering the critical factors that likely lead to fires. A BN with a specific structure was considered using the fire risk transmission paths. Later, given the risk index system, a hybrid fire risk assessment model caused by cable overload was developed based on the entropy weight method. Subsequently, the corresponding risk levels were evaluated based on the evolution of the fire risk, using numerical simulations. Finally, a case study was conducted to validate the proposed methods, and the results indicated that the proposed methods can effectively evaluate the state of the cable and explain the causes of fire risk, which can be used for early fire warnings.

A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method

“…Upon pinpointing the fundamental risk factors and referring to relevant research [16,46], we have illustrated the cable fire risk evolution mechanism within the power distribution room, as depicted in Figure 5. This mechanism comprises two stages: early risk transmission and fire formation.…”

“…The CPT in a DBN can generally be determined using parameter learning or expert knowledge, and parameter learning is based on a large amount of statistical data. Due to a lack of available data, the CPTs of the nodes were primarily determined by expert knowledge and referred to the CPT data from Chen, Huang [16]. For example, the CPT of leaf node Z is listed in Table 3.…”

“…Fire risk assessment stands as a fundamental decision-making tool within the realm of risk control and safety management, providing potential risk information before the onset of a fire event. Presently, research endeavors in the field of cable fire risk assessment predominantly revolve around static analytical methodologies, including the weighted fuzzy Petri [14], the survey scoring method [15], and BN [16]. These methods integrate historical monitoring data and expert assessment data relative to risk factors to realize quantitative risk assessments based on multi-source information fusion [14,17].…”

Cable current-carrying status analysis and early fault warning method based on temperature information

“…It can effectively assess the cables' condition and explain the fire risk causes. And can be used for fire warnings [14]. In 2022, Li et al proposed a cable infrared image state evaluation method based on deep learning according to the temperature, using 4000 infrared images of a 10 kV cable and its accessories for training.…”