Reliability Estimation of Low-Voltage Power Distribution Systems

Abstract

Many accidents always happen in the electricity distribution sector, which requires the use of control strategies in this field. Therefore, this study presented a risk assessment method based on a fuzzy Bayesian network (FBN) and William Fine method in low-voltage power distribution systems to reduce the structural uncertainties of the studies and examined the reliability of these systems to achieve proper performance in emergencies. The first step is to develop a fault tree (FT) and event tree (ET) in bow tie (BT) format. In this study, fuzzy theory and expert judgment were used to qualitatively analyze the root events affecting the system failure and determine the relevant probabilities. Then, deductive and inductive reasoning was done after determining the likelihood of basic events (BEs) and establishing the Bayesian network (BN). Then, the final risk was estimated using William Fine’s method and fuzzy logic. The results of a case study in a low-voltage power distribution system showed that there were 36 BEs and 20 intermediate events (IEs) for system failure, and the reliability of the system was obtained as 0.96654476 and 0.96654476 based on the FT and FBN, respectively. According to the results of BE25 (no installation of the earth) and BE18 (contact of metal objects with the live wire), the most critical events were based on FBN. The results showed that FBN simulation and its combination with static methods such as BT and William Fine provide a suitable method for the accurate identification of distribution systems. Therefore, the approach of the present study can help the decision makers and analysts of the electricity industry to prevent possible failures due to system changes.

Providing a comprehensive approach to oil well blowout risk assessment

“…In the Bayesian method, incident precursor data is used as a probability function from the Bayes theorem to update prior beliefs about event probability [21][22][23][24]. BN is a method renowned for its versatility through visual representation coupled with robust logic-driven decision-making processes regarding process equipment safety evaluations [25].…”

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network

“…Mohammadi [ 13 ] used an integrated approach including BT, fuzzy Bayesian networks, and consequence modeling to estimate risk in tank storage. Jabbari [ 14 ] presented a risk assessment method based on a fuzzy Bayesian network (FBN) and the William Fine method in low-voltage power distribution systems. However, the above research methods all consider risk variables in a static way, without considering dynamic risk factors such as equipment aging, human error, and seasonal change.…”