Safety analysis and dynamic risk assessment of community power distribution network using Bayesian network

Abstract

Safety analysis and risk assessment of Community Power Distribution Network (CPDN) are the key links of safe operation of distribution network system. Reliability indices based risk assessment is the standard method for power distribution network. However, reliability indices are not adequate for the CPDN's intrinsic safety, which is represented by the states of physical components and network's failure events. Meanwhile, the reliability indices is designed for long-term risk assessment and cannot refer to the operation scenarios of CPDN. Therefore, dynamic risk assessment by considering the intrinsic safety of CPDN is a significant and challenge issue. This study proposes a novel safety and dynamic risk assessment framework of CPDN using Bayesian Network (BN). First, based on the standard reliability indices, the failure rate of physical components in CPDN and the failure events are creatively introduced into risk assessment, then a novel risk indices hierarchy is obtained. This indices hierarchy can quantitatively present the dynamic risk of CPDN, with considering the reliability indices and the features of intrinsic safety. Second, a dynamic risk analysis method is proposed, which is based on Fault Tree (FT) and Bayesian Network (BN). A real CPDN is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. The result of the case study suggests that the proposed framework could adequately assess the risk of CPDN and the derived rational safety actions can reduce the risk effectively.