Prediction of Reactor Power under Accident Conditions of Nuclear Power Plant Using  ν-Support Vector Machine

Aiming at the characteristics of core power change under accident conditions and the problems of artificial neural network (ANNs) such as easy to trap minimum and slow convergence speed, a prediction method of core power under accident conditions based on  support vector regression (ν-SVR) was proposed. This method used a k-CV to optimize the parameters of ν-SVR, and then two different ν-SVR predictors were designed. These two predictors were applied to the prediction of core power of rod ejection accident (REA) and rod drop accident (RDA). The results have shown that this method has higher prediction accuracy and shorter response time than the ANNs.