The following master thesis presents Probabilistic Safety Assessment (PSA) of nuclear power plants during low power and shutdown operating states. Purpose of Probabilistic Safety Assessment is assessment and improvement of safety of complex systems, such as nuclear power plant. Analysis reveals possible undesired events that could occur in the system, likelihood of their occurrence, the way of their development and their consequences. Analysis also reveals importance of individual components of the system, which serves as a base for planning maintenance and upgrades in the system.
In the beginning, the basics of probability theory are presented together with general description of Probabilistic Safety Assessment and description of implementation of PSA. Event tree and fault tree approach is used for model development. Connections between different components and systems are modelled with fault trees, while in event trees development of undesired initial events is presented.
This is followed by description of Low Power and Shutdown PSA of nuclear power plants. It is supported with description of a computer model for performing such analyses. Model was developed with RiskSpectrum PSA, a powerful Probabilistic Safety Assessment computer program. Model is based on data from the US nuclear power plant Surry and existing model for nuclear power plant safety analysis during full power operation. Model consists of 15 sub models for 15 different plant operating states from lowering power through hot standby, hot shutdown, cold shutdown, refuelling and then back towards full power again. Core damage frequency is the main risk measure.
Later on some results are presented. Core damage frequencies (CDF) are presented in both numerical and graphical manner. Firstly time weighted CDF are shown, than contribution of different plant operating states (POS) to overall CDF and in the end conditional CDF of each POS is graphically presented.
In the end, there are some comments of results and model presented together with its limitations and possible future upgrades. Developed model covers only internal initiating events of nuclear power plants without external ones (e.g. earthquakes, airplane crash). Results show higher risk during mid-loop states and lowest risk during refuelling, when reactor cavity is flooded. Model could be further enhanced by additional analysis of the most important risk contributors (Human factor, time constants).
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