As a nuclear power plant system configuration risk assessment tool, Risk Monitor (RM) can periodically update Living-PSA risk monitoring model to calculate risk information. It not only provides operating personnel with the real-time risk information, but also reduces human error in test and maintenance work, in order to ensure that the high-risk configurations are identified and controlled.

Traditionally, the high-risk identification of planning activities is mainly based on technical specification (TS) and operating experience, etc. It is lack of quantitative risk assessment to support decision-making, especially when multiple systems or components are out of service or being restored.

Planning risk assessment in existing risk monitors is time-consuming and error-prone, because they typically depend on manually screening and confirming risk-related planned events. Besides, online time-dependent characteristics of NPP systems are ignored. The reliability parameters are taken as constant values providing that components and systems always work under the predefined conditions. Thus, the risk level during planning period is not affected by the current configuration, or the operation history of equipment.

With the widespread application of digital instrument control systems (I&C) in nuclear power plants, the technology of Real-time Online Risk Monitoring (RORM) was put forward. It improves the acquisition technique of current equipment status, modeling and updating technique of Living-PSA Model and provides more accurate, realistic prediction of risk level for planning configurations.

This paper briefly introduces the design of the system structure, database, interfaces and functions of Real-time Online risk monitoring (RORM). It is characterized by the following features: online acquisition of the initial configuration before planning period, time-dependent risk monitoring modeling and updating in time. And it focuses how the real-time online risk monitoring technology of nuclear power plants help the nuclear power plant minimize the risk level of maintenance plan and optimize the maintenance schedule. Also, the calculation method of risk monitoring measures is improved based on the concept of “online, time-dependent”. Finally, the risk management method for optimization of planning activities is proposed.

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