Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework

Abstract

With the improvement of computer performance and the emergence of cutting-edge artificial intelligence (AI) algorithms, an autonomous operation based on AI is being applied to many industries. An autonomous algorithm is a higher-level concept than conventional automatic operation in nuclear power plants (NPPs). In order to achieve autonomous operation, the autonomous algorithm needs to include superior functions to monitor, control and diagnose automated subsystems. This study suggests an autonomous operation algorithm for NPP safety systems using a function-based hierarchical framework (FHF) and a long short-term memory (LSTM). The FHF hierarchically models the safety goals, functions, systems, and components in the NPP. Then, the hierarchical structure is transformed into an LSTM network that is an evolutionary version of a recurrent neural network. This approach is applied to a reference NPP, a Westinghouse 930 MWe, three-loop pressurized water reactor. This LSTM network has been trained and validated using a compact nuclear simulator.