Autonomous Control of Nuclear Power Plants

A nuclear reactor is a complex system that requires highly sophisticated controllers to ensure that desired performance and safety can be achieved and maintained during its operations. Higher-demanding operational requirements such as reliability, lower environmental impacts, and improved performance under adverse conditions in nuclear power plants, coupled with the complexity and uncertainty of the models, necessitate the use of an increased level of autonomy in the control methods. In the opinion of many researchers, the tasks involved during nuclear reactor design and operation (e.g., design optimization, transient diagnosis, and core reload optimization) involve important human cognition and decisions that may be more easily achieved with intelligent methods such as expert systems, fuzzy logic, neural networks, and genetic algorithms. Many experts in the field of control systems share the idea that a higher degree of autonomy in control of complex systems such as nuclear plants is more easily achievable through the integration of conventional control systems and the intelligent components. Researchers have investigated the feasibility of the integration of fuzzy logic, neural networks, genetic algorithms, and expert systems with the conventional control methods to achieve higher degrees of autonomy in different aspects of reactor operations such as reactor startup, shutdown in emergency situations, fault detection and diagnosis, nuclear reactor alarm processing and diagnosis, and reactor load-following operations, to name a few. With the advancement of new technologies and computing power, it is feasible to automate most of the nuclear reactor control and operation, which will result in increased safety and economical benefits. This study surveys current status, practices, and recent advances made towards developing autonomous control systems for nuclear reactors