Abstract
With the increasing demand for electricity transmission and distribution, single-phase grounding accidents, which cause great economic losses and casualties, have occurred frequently. In this study, a Bayesian network (BN)-based risk assessment model for representing single-phase grounding accidents is proposed to examine accident evolution from causes to potential consequences. The Bayesian network of single-phase grounding accidents includes 21 nodes that take into account the influential factors of environment, management, equipment and human error. The Bow-tie method was employed to build the accident evolution path and then converted to a BN. The BN conditional probability tables are determined with reference to historical accident data and expert opinion obtained by the Delphi method. The probability of a single-phase grounding accident and its potential consequences in normal conditions and three typical accident scenarios are analyzed. We found that “Storm” is the most critical hazard of single-phase grounding, followed by “Aging” and “Icing”. This study could quantitatively evaluate the single-phase grounding accident in multi-hazard coupling scenarios and provide technical support for occupational health and safety management of power transmission lines.
Highlights
With the rapid urbanization and industrialization of China, the demand for electricity is continually increasing
Based on the previous accident statistics and expert judgment methods, the prior probability of the parent node was obtained, and the probability of the occurrence of each child node was scored by experts using the Delphi method, and the data obtained was input into Netica software (Norsys Software Corp, Vancouver, Canada) to obtain a complete single-phase grounded Bayesian network
A Bayesian network for representing single-phase grounding accidents of dynamic power grids is proposed based on the combination with the Bow-tie method
Summary
With the rapid urbanization and industrialization of China, the demand for electricity is continually increasing. Wang et al [22] used a Mahal distance discriminant theory-based scheme to improve the stator winding single-phase grounding faults They chose four factors, including magnitude and direction of leakage current, and magnitude and direction of 0-sequence current in the power transformer terminal, to estimate and evaluate the performance of the discriminant model. Previous studies have been dedicated to single-phase fault detection, characteristic identification and performance evaluation It seems that we still need a systematic quantitative risk assessment of single-phase grounding accidents of transmission lines that includes the whole process of the accident, the integrated risks and the various losses. The Bayesian network was employed to analyze single-phase grounding accidents of power transmission lines. This study could provide practical support for the occupational safety and sustainable management of power transmission lines
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