Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification

Vineet Vajpayee; Victor Becerra; Nils Bausch; Jiamei  Deng

doi:10.1007/978-3-030-85318-1_40

Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification

Vineet Vajpayee^*, Victor Becerra, Nils Bausch, Jiamei Deng

^*Corresponding author for this work

School of Energy & Electronic Engineering

Leeds Beckett University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This work presents multi-scale model predictive control design scheme employing wavelet basis function. The proposed scheme is established upon multi-scale subspace identification technique. It is aimed to utilize the proficiency of wavelets in multi-scale data projection and the robustness of subspace identification during estimation in a model predictive control set-up. The multi-scale state-space models estimated at different scales are used for output prediction and for designing predictive control strategy. The competence of the proposed approach is established for constrained load-following problem of a pressurized water-type nuclear reactor. In addition, the fault-tolerant capability of the control algorithm is also tested.

Original language	English
Title of host publication	15th European Workshop on Advanced Control and Diagnosis, ACD 2019
Editors	E. Zattoni, S. Simani, G. Conte
Publisher	Springer
Pages	679–693
Number of pages	15
ISBN (Electronic)	9783030853181
ISBN (Print)	9783030853174
DOIs	https://doi.org/10.1007/978-3-030-85318-1_40
Publication status	Published - 14 Jun 2022
Event	15th European Workshop on Advanced Control and Diagnosis - Bologna, Italy Duration: 21 Nov 2019 → 22 Nov 2019

Publication series

Name	Lecture Notes in Control and Information Sciences
Publisher	Springer
ISSN (Print)	0170-8643
ISSN (Electronic)	2522-5383

Workshop

Workshop	15th European Workshop on Advanced Control and Diagnosis
Abbreviated title	ACD 2019
Country/Territory	Italy
City	Bologna
Period	21/11/19 → 22/11/19

Keywords

MPC
multi-resolution
nuclear reactor
PWR
subspace identification
wavelet

Access to Document

10.1007/978-3-030-85318-1_40

Embargoed Document

VV_ACD_2019
Accepted author manuscript (Post-print), 1.1 MB
Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 14/06/23

Fault Tolerant Control for Increased Safety and Security of Nuclear Power Plants
Becerra, V. & Bausch, N.
Engineering and Physical Sciences Research Council
1/12/18 → 28/02/22
Project: Research

Cite this

Vajpayee, V., Becerra, V., Bausch, N., & Deng, J. (2022). Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification. In E. Zattoni, S. Simani, & G. Conte (Eds.), 15th European Workshop on Advanced Control and Diagnosis, ACD 2019 (pp. 679–693). (Lecture Notes in Control and Information Sciences). Springer. https://doi.org/10.1007/978-3-030-85318-1_40

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abstract = "This work presents multi-scale model predictive control design scheme employing wavelet basis function. The proposed scheme is established upon multi-scale subspace identification technique. It is aimed to utilize the proficiency of wavelets in multi-scale data projection and the robustness of subspace identification during estimation in a model predictive control set-up. The multi-scale state-space models estimated at different scales are used for output prediction and for designing predictive control strategy. The competence of the proposed approach is established for constrained load-following problem of a pressurized water-type nuclear reactor. In addition, the fault-tolerant capability of the control algorithm is also tested.",

keywords = "MPC, multi-resolution, nuclear reactor, PWR, subspace identification, wavelet",

author = "Vineet Vajpayee and Victor Becerra and Nils Bausch and Jiamei Deng",

year = "2022",

month = jun,

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doi = "10.1007/978-3-030-85318-1_40",

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series = "Lecture Notes in Control and Information Sciences",

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Vajpayee, V, Becerra, V , Bausch, N & Deng, J 2022, Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification. in E Zattoni, S Simani & G Conte (eds), 15th European Workshop on Advanced Control and Diagnosis, ACD 2019. Lecture Notes in Control and Information Sciences, Springer, pp. 679–693, 15th European Workshop on Advanced Control and Diagnosis, Bologna, Italy, 21/11/19. https://doi.org/10.1007/978-3-030-85318-1_40

Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification. / Vajpayee, Vineet; Becerra, Victor ; Bausch, Nils et al.

15th European Workshop on Advanced Control and Diagnosis, ACD 2019. ed. / E. Zattoni; S. Simani; G. Conte. Springer, 2022. p. 679–693 (Lecture Notes in Control and Information Sciences).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification

AU - Vajpayee, Vineet

AU - Becerra, Victor

AU - Bausch, Nils

AU - Deng, Jiamei

PY - 2022/6/14

Y1 - 2022/6/14

N2 - This work presents multi-scale model predictive control design scheme employing wavelet basis function. The proposed scheme is established upon multi-scale subspace identification technique. It is aimed to utilize the proficiency of wavelets in multi-scale data projection and the robustness of subspace identification during estimation in a model predictive control set-up. The multi-scale state-space models estimated at different scales are used for output prediction and for designing predictive control strategy. The competence of the proposed approach is established for constrained load-following problem of a pressurized water-type nuclear reactor. In addition, the fault-tolerant capability of the control algorithm is also tested.

AB - This work presents multi-scale model predictive control design scheme employing wavelet basis function. The proposed scheme is established upon multi-scale subspace identification technique. It is aimed to utilize the proficiency of wavelets in multi-scale data projection and the robustness of subspace identification during estimation in a model predictive control set-up. The multi-scale state-space models estimated at different scales are used for output prediction and for designing predictive control strategy. The competence of the proposed approach is established for constrained load-following problem of a pressurized water-type nuclear reactor. In addition, the fault-tolerant capability of the control algorithm is also tested.

KW - MPC

KW - multi-resolution

KW - nuclear reactor

KW - PWR

KW - subspace identification

KW - wavelet

UR - https://www.springer.com/series/642

U2 - 10.1007/978-3-030-85318-1_40

DO - 10.1007/978-3-030-85318-1_40

M3 - Conference contribution

SN - 9783030853174

T3 - Lecture Notes in Control and Information Sciences

SP - 679

EP - 693

BT - 15th European Workshop on Advanced Control and Diagnosis, ACD 2019

A2 - Zattoni, E.

A2 - Simani, S.

A2 - Conte, G.

PB - Springer

T2 - 15th European Workshop on Advanced Control and Diagnosis

Y2 - 21 November 2019 through 22 November 2019

ER -

Vajpayee V, Becerra V , Bausch N, Deng J. Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification. In Zattoni E, Simani S, Conte G, editors, 15th European Workshop on Advanced Control and Diagnosis, ACD 2019. Springer. 2022. p. 679–693. (Lecture Notes in Control and Information Sciences). doi: 10.1007/978-3-030-85318-1_40

Wavelet-based model predictive control of PWR nuclear reactor using multi-scale subspace identification

Abstract

Publication series

Workshop

Keywords

Access to Document

Embargoed Document

Fingerprint

Projects

Fault Tolerant Control for Increased Safety and Security of Nuclear Power Plants

Cite this