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Business continuity of energy systems : a quantitative framework for dynamic assessment and optimization

Abstract : Business continuity management is a comprehensive framework to prevent the disruptive events from impacting the business operations, quickly recovering business and reducing the corresponding potential damages for energy system, such as nuclear power plants (NPPs). This dissertation provides discussions on the following aspects: developing appropriate risk assessment methods in order to integrate condition monitoring data and inspection data for a robust and real-time risk profile updating and prognostics. To account for the uncertainty of condition monitoring data, a hidden Markov gaussian mixture model is developed to model the condition monitoring data. A Bayesian network is applied to integrate the two data sources. For improving applicability of business continuity in practice, time-variant variables regard business continuity index, e.g. component degradation, time-dependent revenue, etc are taken into consideration in the business continuity modelling process. Based on the proposed business continuity index, a joint optimization method considering all the safety measures in event evolvement process including prevention stage, mitigation stage, emergency stage and recovery stage is developed to enhance system business continuity under limited resources. The proposed methodologies are applied to NPP against disruptive event.
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Submitted on : Saturday, January 2, 2021 - 1:24:26 AM
Last modification on : Sunday, January 3, 2021 - 3:29:30 AM

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  • HAL Id : tel-03092293, version 1

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Jinduo Xing. Business continuity of energy systems : a quantitative framework for dynamic assessment and optimization. Chemical and Process Engineering. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLC087⟩. ⟨tel-03092293⟩

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