Improved methodology for generation of axial flux shapes in digital core protection systems

Abstract

An improved method of axial flux shape (AFS) generation for digital core protection systems of pressurized water reactors is presented in this paper using an artificial neural network (ANN) technique—a feedforward network trained by backpropagation. It generates 20-node axial power shapes based on the information from three ex-core detectors. In developing the method, a total of 7173 axial flux shapes are generated from ROCS code simulation for training and testing of the ANN. The ANN trained 200 data predicts the remaining data with the average root mean square error of about 3%. The developed method is also tested with the real plant data measured during normal operation of Yonggwang Unit 4. The RMS errors in the range of 0.9∼2.1% are about twice as accurate as the cubic spline approximation method currently used in the plant. The developed method would contribute to solve the drawback of the current method as it shows reasonable accuracy over wide range of core conditions.