Neural Network Control for Steam Generator Level Stability
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Solution Overview
Problem
Nuclear power plant control systems face challenges in precisely regulating steam generator levels due to component degradation and transient events, leading to safety margins that may result in suboptimal power operation or premature shutdowns.
Innovation Solution
A neural network is trained using simulated nuclear power plant data to determine optimal control settings, enabling precise control of components like pumps and valves to achieve desired plant responses, such as steam generator levels, even in the presence of degradation or transient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control systems are used to regulate steam generator levels, then the system operates with safety margins, but power output is reduced or premature shutdowns occur
Solution Approach 1:
The patent replaces traditional mechanical control systems with an artificial neural network-based control system. The neural network processes sensor data and determines optimal control settings for steam generator levels, replacing conventional control mechanisms with an intelligent system that adapts to component degradation and transient conditions, thereby maintaining reliability while optimizing power output
Solution Approach 2:
The neural network dynamically adjusts control parameters based on real-time sensor data and learned patterns from training. By changing control settings adaptively rather than using fixed parameters, the system maintains steam generator level control reliability while avoiding unnecessary safety margins that reduce power output
2Reliability
If control system settings are adjusted to maintain desired response, then component degradation and transient events cause control failure, but increasing safety margins reduces power operation efficiency
Solution Approach 1:
The neural network is trained in advance using simulated nuclear power plant data that includes various component degradation scenarios and transient events. This preliminary training equips the network with knowledge to handle these conditions effectively when deployed, maintaining control reliability without requiring excessive safety margins that would reduce power operation efficiency
Solution Approach 2:
The control system continuously receives feedback from plant sensors and adjusts control settings based on neural network predictions. This closed-loop feedback mechanism enables the system to maintain desired steam generator levels despite component degradation or transient events, optimizing power operation efficiency by avoiding overly conservative safety margins
Data Source
AI summary
A method of controlling a nuclear power plant includes obtaining sensor data from one or more sensors of the nuclear power plant, providing the sensor data and a desired plant response to a neural network, wherein the neural network has been previously trained using a simulated nuclear power plant and is structured to determine at least one control system setting to achieve the desired plant response, determining at least one control system setting to achieve the desired plant response with the neural network, and setting or changing at least one control system setting of a control system of the nuclear power plant to the at least one control system setting determined by the neural network.