Bayesian belief network-based human reliability analysis methodology for start-up and shutdown operations in nuclear power plants

Abstract

Human-induced unplanned reactor trips during start-up and shutdown operations can impair the safety condition of the plant or threaten important plant safety functions. Despite the need for efforts to reduce such human errors through human reliability analysis (HRA), most existing HRA focuses on emergency or full-power operations. To address this, the current study proposes an HRA methodology for start-up and shutdown operations in which a quantitative evaluation is performed using a Bayesian belief network (BBN) model based on a developed quantification framework for the start-up and shutdown operation tasks. Before the evaluation, general operating procedure (GOP) analysis, simulation-based screening analysis, and task analysis are performed. To demonstrate the process of proposed methodology and its effectiveness, a case study with a GOP is conducted. Then to verify the feasibility of the proposed methodology, two comparison cases are conducted with BBN model evaluation and actual events that occurred in Korean NPPs.