- Tytuł:
- VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach
- Autorzy:
-
Mangaraj, Mrutyunjaya
Sabat, Jogeswara
Barisal, Ajit Kumar
Ramaiah, K. Subba
Rao, Gudivada Eswara - Powiązania:
- https://bibliotekanauki.pl/articles/2175932.pdf
- Data publikacji:
- 2022
- Wydawca:
- Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
- Tematy:
-
DL approach
deep learning approach
DSTATCOM
distributed static compensator
ALMS
PQ
power quality - Opis:
- 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.
- Źródło:
-
Power Electronics and Drives; 2022, 7, 42; 174--186
2451-0262
2543-4292 - Pojawia się w:
- Power Electronics and Drives
- Dostawca treści:
- Biblioteka Nauki
Artykuł