Performance evaluation of neural network topologies for online state estimation and fault detection in pressurized water reactor.
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| Title: | Performance evaluation of neural network topologies for online state estimation and fault detection in pressurized water reactor. |
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| Authors: | Kumar, Swetha R.1 (AUTHOR) srk.ice@psgtech.ac.in, Devakumar, Jayaprasanth1 (AUTHOR) djp.ice@psgtech.ac.in |
| Source: | Annals of Nuclear Energy. Sep2022, Vol. 175, pN.PAG-N.PAG. 1p. |
| Subjects: | Recurrent neural networks, Delayed neutrons, Artificial neural networks, Nuclear reactor cores, Pressurized water reactors, Nuclear reactors, Kalman filtering |
| Abstract: | • To develop a Neural network model for estimating the internal variables of a pressurized water reactor process and to detect fault at early stage. • Data-driven Neural networks architectures like Feed-Forward Neural networks, Dynamic NARX Neural networks, and Recurrent Neural networks are designed to estimate the reactor core states. • The performance of the selected neural estimator is also compared with the model-based Unscented Kalman filter estimator. • Residuals are generated along with positive and negative threshold using the selected RNN model for fault detection. The nuclear reactor is a multi-rate nonlinear system in which the state variables progress with widely varying dynamics. It has state variables such as reactivity and delayed neutron precursor densities that cannot be measured directly via sensors. Reactivity signifies the criticality of the reactor core. Delayed neutron precursors are the source for delayed neutrons which plays a vital role in the change of neutron densities. Besides, the other states which are measured are also corrupted by measurement noise and are susceptible to sensor faults. Thus, estimation of these state variables becomes critical. As traditional estimators like EKF, UKF, and Particle filers require a close model of the process, Data-driven Neural networks architectures like Feed-Forward Neural networks, Dynamic NARX Neural networks, and Recurrent Neural networks are designed to estimate the reactor core states. The performance of the selected neural estimator is also compared with the Unscented Kalman estimator. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of Nuclear Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 157523934 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance evaluation of neural network topologies for online state estimation and fault detection in pressurized water reactor. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Swetha+R%2E%22">Kumar, Swetha R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> srk.ice@psgtech.ac.in</i><br /><searchLink fieldCode="AR" term="%22Devakumar%2C+Jayaprasanth%22">Devakumar, Jayaprasanth</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> djp.ice@psgtech.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Nuclear+Energy%22">Annals of Nuclear Energy</searchLink>. Sep2022, Vol. 175, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Delayed+neutrons%22">Delayed neutrons</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+reactor+cores%22">Nuclear reactor cores</searchLink><br /><searchLink fieldCode="DE" term="%22Pressurized+water+reactors%22">Pressurized water reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+reactors%22">Nuclear reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • To develop a Neural network model for estimating the internal variables of a pressurized water reactor process and to detect fault at early stage. • Data-driven Neural networks architectures like Feed-Forward Neural networks, Dynamic NARX Neural networks, and Recurrent Neural networks are designed to estimate the reactor core states. • The performance of the selected neural estimator is also compared with the model-based Unscented Kalman filter estimator. • Residuals are generated along with positive and negative threshold using the selected RNN model for fault detection. The nuclear reactor is a multi-rate nonlinear system in which the state variables progress with widely varying dynamics. It has state variables such as reactivity and delayed neutron precursor densities that cannot be measured directly via sensors. Reactivity signifies the criticality of the reactor core. Delayed neutron precursors are the source for delayed neutrons which plays a vital role in the change of neutron densities. Besides, the other states which are measured are also corrupted by measurement noise and are susceptible to sensor faults. Thus, estimation of these state variables becomes critical. As traditional estimators like EKF, UKF, and Particle filers require a close model of the process, Data-driven Neural networks architectures like Feed-Forward Neural networks, Dynamic NARX Neural networks, and Recurrent Neural networks are designed to estimate the reactor core states. The performance of the selected neural estimator is also compared with the Unscented Kalman estimator. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Nuclear Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.anucene.2022.109235 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Delayed neutrons Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Nuclear reactor cores Type: general – SubjectFull: Pressurized water reactors Type: general – SubjectFull: Nuclear reactors Type: general – SubjectFull: Kalman filtering Type: general Titles: – TitleFull: Performance evaluation of neural network topologies for online state estimation and fault detection in pressurized water reactor. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Swetha R. – PersonEntity: Name: NameFull: Devakumar, Jayaprasanth IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 03064549 Numbering: – Type: volume Value: 175 Titles: – TitleFull: Annals of Nuclear Energy Type: main |
| ResultId | 1 |
