Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters

Poblete, P; Pizarro, G; Droguett, G; Nunez, F; Judge, PD; Pereda, J

Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters

Poblete, P Pizarro, G Droguett, G Nunez, F Judge, PD Pereda, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Power Electronics, 2022, 37, (9), pp. 10306-10318
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Poblete, P
dc.contributor.author	Pizarro, G
dc.contributor.author	Droguett, G
dc.contributor.author	Nunez, F
dc.contributor.author	Judge, PD
dc.contributor.author	Pereda, J
dc.date.accessioned	2023-06-30T04:50:09Z
dc.date.available	2023-06-30T04:50:09Z
dc.date.issued	2022-09-01
dc.identifier.citation	IEEE Transactions on Power Electronics, 2022, 37, (9), pp. 10306-10318
dc.identifier.issn	0885-8993
dc.identifier.issn	1941-0107
dc.identifier.uri	http://hdl.handle.net/10453/171039
dc.description.abstract	Modular multilevel converters (MMCs) have become one of the most popular power converters for medium/high-power transmission systems and motor drive applications. Standard control schemes for MMCs use a voltage measurement per submodule (SM) to balance the capacitor voltages and govern the MMC. Consequently, the control system requires a significant amount of sensors and the effective communication of sensitive data under relevant electromagnetic interference (EMI), impacting the reliability and cost of the MMC. This work presents a distributed neural network (DNN) observer inspired by a general predictor-corrector structure for estimating the capacitor voltages at each SM. The proposed observer predicts each SM capacitor voltage using a standard average model. Then, each prediction is corrected and denoised by a neural network of reduced computational complexity. As a result, the proposed observer reduces the number of required voltage sensors per arm to only one and filters the high-frequency noise without noticeable delay in the estimated SM capacitor voltages for both transient and steady-state operations. Experiments conducted in a three-phase MMC with 24 SMs confirm the effectiveness of the proposed DNN observer.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Power Electronics
dc.relation.isbasedon	10.1109/TPEL.2022.3163395
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Distributed Neural Network Observer for Submodule Capacitor Voltage Estimation in Modular Multilevel Converters
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-06-30T04:50:08Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	9

Abstract:

Modular multilevel converters (MMCs) have become one of the most popular power converters for medium/high-power transmission systems and motor drive applications. Standard control schemes for MMCs use a voltage measurement per submodule (SM) to balance the capacitor voltages and govern the MMC. Consequently, the control system requires a significant amount of sensors and the effective communication of sensitive data under relevant electromagnetic interference (EMI), impacting the reliability and cost of the MMC. This work presents a distributed neural network (DNN) observer inspired by a general predictor-corrector structure for estimating the capacitor voltages at each SM. The proposed observer predicts each SM capacitor voltage using a standard average model. Then, each prediction is corrected and denoised by a neural network of reduced computational complexity. As a result, the proposed observer reduces the number of required voltage sensors per arm to only one and filters the high-frequency noise without noticeable delay in the estimated SM capacitor voltages for both transient and steady-state operations. Experiments conducted in a three-phase MMC with 24 SMs confirm the effectiveness of the proposed DNN observer.

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http://hdl.handle.net/10453/171039