<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation.</p>
</abstract>

With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation.

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

Department of Electrical and Electronics Engineering

Odisha University of Technology and Research

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach"}

With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power...

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

Online veröffentlicht: 31. Aug. 2022

Seitenbereich: 174 - 186

Eingereicht: 17. Apr. 2022

Akzeptiert: 29. Juni 2022

DOI: https://doi.org/10.2478/pead-2022-0013

Schlüsselwörter
DL-Approach, DSTATCOM, ALMS and PQ

© 2022 Mrutyunjaya Mangaraj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

Online veröffentlicht: 31. Aug. 2022

Seitenbereich: 174 - 186

Eingereicht: 17. Apr. 2022

Akzeptiert: 29. Juni 2022

DOI: https://doi.org/10.2478/pead-2022-0013

SchlüsselwörterDL-Approach, DSTATCOM, ALMS and PQ

© 2022 Mrutyunjaya Mangaraj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
DL-Approach, DSTATCOM, ALMS and PQ