Transient identification in nuclear power plants: A review

Abstract A transient is defined as an event when a plant proceeds from a normal state to an abnormal state. In nuclear power plants (NPPs), recognizing the types of transients during early stages, for taking appropriate actions, is critical. Furthermore, classification of a novel transient as “don't know”, if it is not included within NPPs collected knowledge, is necessary. To fulfill these requirements, transient identification techniques as a method to recognize and to classify abnormal conditions are extensively used. The studies revealed that model-based methods are not suitable candidates for transient identification in NPPs. Hitherto, data-driven methods, especially artificial neural networks (ANN), and other soft computing techniques such as fuzzy logic, genetic algorithm (GA), particle swarm optimization (PSO), quantum evolutionary algorithm (QEA), expert systems are mostly investigated. Furthermore, other methods such as hidden Markov model (HMM), and support vector machines (SVM) are considered for transient identification in NPPs. By these modern techniques, NPPs safety, due to accidents recognition by symptoms rather than events, is improved. Transient identification is expected to become increasingly important as the next generation reactors being designed to operate for extended fuel cycles with less operators' oversight. In this paper, recent studies related to the advanced techniques for transient identification in NPPs are presented and their differences are illustrated.

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