Fault detection of pressurized heavy water nuclear reactors with steady state and dynamic characteristics using data-driven techniques

Nuclear energy is a crucial source to bridge the deficit of energy demand as fossil fuel reserves are continuously depleting over time. However, nuclear reactors operation is highly safety-critical and any breach of safe operation would lead to catastrophic effects. Therefore, the operation of nuclear reactors have to monitored very carefully for any anomalous operational characteristics. However, a significant challenge in that front is the existence of various operational modes and dynamic transition in between them. Thus, a successful fault detection (FD) technique should clearly segregate the dynamic transitions and any anomalous faulty behaviour. This article presents a novel approach for data-driven FD of Pressurized Heavy Water Reactors. The proposed solution hinges on integrating Hidden Markov Models (HMM) with Probabilistic Principal Component Analysis (PPCA), and Dynamic Principal Component Analysis (DPCA). Initially an HMM is developed using a training data set which consist of multimode with transitions. Then, an HMM-based probability ratio strategy is employed for distinguishing transitional modes. After that, Viterbi algorithm is also used to separate different known modes, such as stable modes and transitional modes. Thus, HMM would help to identify the modal transitions and dynamic operation. Thereafter, PPCA models are used to deal with the steady-state operation while DPCA based models are used for detecting transitions and dynamic operation. Subsequently, statistical non-conformity to the developed models are used to flag faults. The superiority of the proposed framework is validated using a benchmark simulated data and industrial real time data representing the operation of PHWR.