Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter

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Abstract

In this paper we demonstrate how a statistical model checking approach can be used to check the dynamic performance of the finite set model predictive control algorithm for a standalone 3-level neutral point diode clamped converter. The robustness of the control algorithm under parameter uncertainty is also analyzed. Finite control set model predictive control (FCS-MPC) algorithm has found many applications in power electronics due to the straightforward control design and the possibility to include different control objectives. The control algorithm for 3-level neutral point diode clamped (NPC) converter has to address several objectives to provide optimal reference tracking during load transients. Therefore, looking from the perspective of the implementation, the FCS-MPC algorithm suits the control requirements of NPC converter. However, the problem remains in performing an analytical performance verification of the algorithm to demonstrate its robustness, which is compulsory for any industrial application. In this paper, we present how a statistical model checking approach can be used to solve this problem and also provide valuable data about the algorithm’s performance during transients and in the case of parameter uncertainty. A benchmark model is created in Matlab/Simulink to validate the correct system modeling in UPPAAL SMC toolbox.
Original languageEnglish
Title of host publicationProceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe)
Number of pages10
PublisherIEEE Press
Publication dateSep 2018
Pages1-10
Article number8515598
ISBN (Print)978-1-5386-4145-3
ISBN (Electronic)978-9-0758-1528-3
Publication statusPublished - Sep 2018
Event20th European Conference on Power Electronics and Applications, EPE 2018 ECCE Europe - Riga, Latvia
Duration: 17 Sep 201821 Sep 2018

Conference

Conference20th European Conference on Power Electronics and Applications, EPE 2018 ECCE Europe
CountryLatvia
CityRiga
Period17/09/201821/09/2018

Fingerprint

Model predictive control
Diodes
Model checking
Power electronics
Industrial applications

Keywords

  • Converter Control
  • Uninterruptible Power Supply (UPS)
  • Robustness
  • Statistics
  • Voltage Source Converter (VSC)

Cite this

Novak, M., Nyman, U. M., Dragicevic, T., & Blaabjerg, F. (2018). Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter. In Proceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe) (pp. 1-10). [8515598] IEEE Press.
Novak, Mateja ; Nyman, Ulrik Mathias ; Dragicevic, Tomislav ; Blaabjerg, Frede. / Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter. Proceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe). IEEE Press, 2018. pp. 1-10
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abstract = "In this paper we demonstrate how a statistical model checking approach can be used to check the dynamic performance of the finite set model predictive control algorithm for a standalone 3-level neutral point diode clamped converter. The robustness of the control algorithm under parameter uncertainty is also analyzed. Finite control set model predictive control (FCS-MPC) algorithm has found many applications in power electronics due to the straightforward control design and the possibility to include different control objectives. The control algorithm for 3-level neutral point diode clamped (NPC) converter has to address several objectives to provide optimal reference tracking during load transients. Therefore, looking from the perspective of the implementation, the FCS-MPC algorithm suits the control requirements of NPC converter. However, the problem remains in performing an analytical performance verification of the algorithm to demonstrate its robustness, which is compulsory for any industrial application. In this paper, we present how a statistical model checking approach can be used to solve this problem and also provide valuable data about the algorithm’s performance during transients and in the case of parameter uncertainty. A benchmark model is created in Matlab/Simulink to validate the correct system modeling in UPPAAL SMC toolbox.",
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Novak, M, Nyman, UM, Dragicevic, T & Blaabjerg, F 2018, Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter. in Proceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe)., 8515598, IEEE Press, pp. 1-10, 20th European Conference on Power Electronics and Applications, EPE 2018 ECCE Europe, Riga, Latvia, 17/09/2018.

Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter. / Novak, Mateja; Nyman, Ulrik Mathias; Dragicevic, Tomislav; Blaabjerg, Frede.

Proceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe). IEEE Press, 2018. p. 1-10 8515598.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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N2 - In this paper we demonstrate how a statistical model checking approach can be used to check the dynamic performance of the finite set model predictive control algorithm for a standalone 3-level neutral point diode clamped converter. The robustness of the control algorithm under parameter uncertainty is also analyzed. Finite control set model predictive control (FCS-MPC) algorithm has found many applications in power electronics due to the straightforward control design and the possibility to include different control objectives. The control algorithm for 3-level neutral point diode clamped (NPC) converter has to address several objectives to provide optimal reference tracking during load transients. Therefore, looking from the perspective of the implementation, the FCS-MPC algorithm suits the control requirements of NPC converter. However, the problem remains in performing an analytical performance verification of the algorithm to demonstrate its robustness, which is compulsory for any industrial application. In this paper, we present how a statistical model checking approach can be used to solve this problem and also provide valuable data about the algorithm’s performance during transients and in the case of parameter uncertainty. A benchmark model is created in Matlab/Simulink to validate the correct system modeling in UPPAAL SMC toolbox.

AB - In this paper we demonstrate how a statistical model checking approach can be used to check the dynamic performance of the finite set model predictive control algorithm for a standalone 3-level neutral point diode clamped converter. The robustness of the control algorithm under parameter uncertainty is also analyzed. Finite control set model predictive control (FCS-MPC) algorithm has found many applications in power electronics due to the straightforward control design and the possibility to include different control objectives. The control algorithm for 3-level neutral point diode clamped (NPC) converter has to address several objectives to provide optimal reference tracking during load transients. Therefore, looking from the perspective of the implementation, the FCS-MPC algorithm suits the control requirements of NPC converter. However, the problem remains in performing an analytical performance verification of the algorithm to demonstrate its robustness, which is compulsory for any industrial application. In this paper, we present how a statistical model checking approach can be used to solve this problem and also provide valuable data about the algorithm’s performance during transients and in the case of parameter uncertainty. A benchmark model is created in Matlab/Simulink to validate the correct system modeling in UPPAAL SMC toolbox.

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Novak M, Nyman UM, Dragicevic T, Blaabjerg F. Statistical Performance Verification of FCS-MPC Applied to Three Level Neutral Point Clamped Converter. In Proceedings of 2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe). IEEE Press. 2018. p. 1-10. 8515598