Dynamic Modeling of Balance Of Plant of Nuclear Power Plant Using Neural Networks

• Main Authors: Chang,Shih-Chung, 張世忠
• Other Authors: Lin,Chaung
• Format: Others
• Language: zh-TW
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/84481582616403862632

碩士 === 國立清華大學 === 核子工程學系 === 83 === Generally, dynamic system's responses were described by the differential equations .Therefore, to model the system dynamics of a complicated system , it requires rather long time to set up the system governing equations. In addition, the computation effort is also very huge. In order to reduce the computing time, the calaulation of dynamic response of balance of plant is usually neglected in the large system codes. This research is to set up the dynamic model of balance of plant using the neural networks so that the computing time can be very short. In this research, the diagonal recurrent neural networks were adopted, which need rather few neurons and also achieve good performance. In addition, it converged very fast and then reduced the training time. The data used for training were generated by the compact simulator of Mannashan Nuclear Power Plant which was developed by Institute of Nuclear Energy Research and Institute for Information Industry. The whole system was divided into six subsystems:high pressure turbine, moisture seperator and reheater, low pressure turbine, condenser, low pressure feedwater heater, and high pressure feedwater heater. The neural network models were trained for such subsystems. When the training was finished, the neural networks of subsystem were connected to simulate the dynamic behavior of balance of plant.