Stator inter turn fault and voltage unbalance detection and discrimination approach for an reactor coolant pump

Abstract

Nuclear power industries have increasing interest in using fault detection and diagnosis (FDD) techniques to improve availability, reliability, and safety of nuclear power plants (NPP). In this paper, a procedure for stator inter turn short circuit fault and unbalanced supply voltage fault detection and severity evaluation on reactor coolant pump (RCP) driven by induction motor is presented. Fault detection system is performed using unsupervised artificial neural networks: the so-called Self-Organizing Maps (SOM). Induction motor stator currents are measured, recorded, and used for feature extraction using Park transform, Zero crossing times signal, and the envelope, then statistical features are calculated from each signal which serves for feeding the neural network, in order to perform the fault diagnosis, the min-redundancy max-relevancy (mRMR) feature selection technique is used to select more accurate features. The network is trained and tested on experimental data gathered from a three-phase squirrel-cage induction motor. It is demonstrated that the strategy is able to correctly discriminate between the stator fault case, unbalanced voltage and the safe case. The system is also able to estimate the extent of the faults.