Robust on-line diagnosis tool for the early accident detection in nuclear power plants

Tolo, Silvia; Tian, Xinshou; Bausch, Nils; Becerra, Victor M.; Santhosh, T. V.; Vinod, Gopika; Patelli, Edoardo

doi:10.1016/j.ress.2019.02.015

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…AI-based process models such as the least square support vector machine (LSSVM) and artificial neural network (ANN) are widely used to model such ill-defined and complex problems using real operational data of the process [29][30][31][32][33][34]. An extensive process of data of high quality, the data visualization tests, and the validation of AI process models are essential for reliable AI utilization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

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Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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“…Content may change prior to final publication. [160] Colored Petri Nets [193], [197], [198] Tree Methods [50], [200] Unsupervised Learning [161], [181] Fuzzy Methods [159], [182]- [189], [192], [195], [201] Other Models/Algorithms Physical Plant-centered [39], [53], [86]- [88], [123], [127]- [129], [150], [175], [193] Cyber System Layer & Physical System Layer ANN [105], [160] Fuzzy Colored Petri Nets (FCPN) [106] Reinforcement Learning (RL) [107], [108], [134]- [137] Recurrent Neural Network (RNN) [95], [97], [138], [138], [139], [146] Convolutional Neural Network (CNN) [140]- [142] Deep Belief Network [90], [121] SVM [118], [119], [122] Tree Methods [124]-…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, as subspace clustering is deemed to be a promising technique, Gao et al [126] suggest using locality-preserving robust latent low-rank recovery (L2PLRR) in order to mine intrinsic structure from high dimensional and non-linear data where high-dimensional data are mapped into a latent lowdimensional space. Silvia et al [127] combines some ANN architectures and designs an on-line diagnosis tool which could early predict the possibility of coolant loss accidents with enhanced model response accuracy, leading to a better level of safety for NPPs. Another group of researchers also do research on issues concerning on the loss of coolant accidents [128], while they apply NARX (nonlinear autoregressive with exogenous inputs) neural network and successfully find a general solution for break size estimation in loss of coolant accidents, improving the reliability of FDD to some extent.…”

Section: ) Fault Detection and Diagnosismentioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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“…Areas where BNs have seen application, in addition to those discussed in Cai et al (2017), include the general case of industrial processes (Yu and Zhao, 2019), hydroelectric generation systems (Xu et al, 2019), and ground-source heat pumps . Lastly, as a method used in combination with ANNs, Bayesian statistics was used in Tolo et al (2019) as a means of connecting a set of neural network architectures for early accident detection in NPPs.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Robust on-line diagnosis tool for the early accident detection in nuclear power plants

Cited by 31 publications

References 32 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

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