VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach
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1
Department of Electrical and Electronics Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh 535005, India
2
Department of Electrical Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha 751029, India
3
Department of Electrical and Electronics Engineering, Vignan Institute of Technology and Management, Berhampur,Odisha 761008, India
Power Electronics and Drives 2022;7 (42):174-186
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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.