Statistical analysis of a time series relevant to passive systems of nuclear power plants

Chandrakar, Amit; Datta, Debabrata; Nayak, Arun K.; Vinod, Gopika

doi:10.1007/s13198-016-0570-8

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…For training the semisupervised model, new unlabelled data collected from the NPP was integrated with the generated labelled data. A. Chandrakar et al [22] focused on the uncertainty propagation and reliability estimation of passive safety systems in a nuclear power plant. The paper used an autoregressive integrated moving average (ARIMA) model to fit the atmospheric temperature, which is an independent process parameter in a passive safety system.…”

Section: Nuclear Power Plant Data Modellingmentioning

confidence: 99%

Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

et al. 2021

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Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

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Section: Nuclear Power Plant Data Modellingmentioning

confidence: 99%