Application Of Output Distribution Control Theory And Performance Evaluation Method In Nuclear Power

With the development of technology,nuclear power generation is receiving more and more attention.Effective power control systems and performance evaluation systems are crucial for meeting grid load tracking and nuclear power plant safety.Based on Output Distribution Control(ODC),this thesis improves the optimization algorithm for ODC by using the Particle Swarm Optimization(PSO),which has the advantages of fast convergence,fewer parameters,and simple implementation.Applying this algorithm to ODC can avoid complex calculations and obtain globally optimal control inputs.Pressurized Water Reactors(PWRs)are widely used in power generation,and the key to their safe and efficient operation is the control of reactor power.Reactor power control has nonlinear characteristics,and model parameters vary greatly under different operating conditions.Using the dynamic equations,thermal-heat transfer models,and reactor reactivity models,this thesis simulates pressurized water reactors,considering stochastic disturbances in the delayed neutron precursor density measurement and reactor coolant inlet temperature.The improved ODC algorithm is used to control the core power of PWRs,which has insensitivity and robustness to non-Gaussian disturbances.In long-term operation,all control systems will gradually wear and even fail with the increasing operation time,which affects control performance.For nuclear power control systems,once the performance of the controller drops significantly,a chain reaction may occur,leading to a risk of system collapse.Based on ODC theory,an online control performance evaluation system is proposed to timely detect the control effect and sudden disturbances of the controller,avoiding the collapse of the entire system.To improve the accuracy and sensitivity of performance evaluation,a performance evaluation method based on the Bhattacharyya performance index is proposed,considering the probability distribution of real-time I/O data and historical I/O data.The sliding window is used to update the probability distribution of the output,greatly expanding the range of statistical data for the performance index.Simulation results show that the performance evaluation method based on the Bhattacharyya performance index can accurately judge the control effect fluctuation,and also eliminate non-Gaussian disturbances during the control process,avoiding the impact on performance evaluation.In conclusion,this thesis uses the particle swarm algorithm to improve the ODC algorithm,applies it to the core power control system of PWRs,and designs an online control performance evaluation system to ensure the performance and safety of core power control.