Deep-Learning-Based Controller for Parallel DSTATCOM to Improve Power Quality in Distribution System

A. Kasim Vali, P. Srinivasa Varma, Ch. Rami Reddy (), Abdulaziz Alanazi and Ali Elrashidi ()
Additional contact information
A. Kasim Vali: Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, Andhra Pradesh, India
P. Srinivasa Varma: Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, Andhra Pradesh, India
Ch. Rami Reddy: Department of Electrical and Electronics Engineering, Joginpally B R Engineering College, Hyderabad 500075, India
Abdulaziz Alanazi: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
Ali Elrashidi: Electrical Engineering Department, University of Business and Technology, Jeddah 23435, Saudi Arabia

Energies, 2025, vol. 18, issue 18, 1-28

Abstract: Modern utility systems are being heavily strained by rising energy consumption and dynamic load variations, which have an impact on the quality and reliability of the supply. Harmonic injection and reactive power imbalance are caused by the widespread divergence. Power quality (PQ) issues are mostly caused by renewable energy powered by power electronic converters that are integrated into the utility grid, despite the fact that a range of industries require high-quality power to function properly at all times. Several solutions have been created, but continuing efforts and newly improved solutions are needed to solve these problems by operating according to various international standards. Distributed Static Compensator (DSTATCOM) was created in the proposed model to enhance PQ in a standard bus system. A standard bus system using the DSTATCOM model was initially developed. A real-time dataset was gathered while applying various PQ disturbance conditions. A deep learning controller was created using this generated dataset, which examined the bus voltages to generate the DSTATCOM pulse signal. Two case studies, the IEEE 13 bus and the IEEE 33 bus system, were used to analyze the proposed work. Performance of the proposed deep learning controller was verified in various situations, including interruption, swell, harmonics, and sag. The outcome of THD in the IEEE 13 bus is 0.09% at the sag period, 0.08% at the swell period, 0.01% at the interruption period, and in the IEEE 33 bus was 1.99% at the sag period, 0.44% at the swell period, and 0.01% at the interruption period. Also, the effectiveness of the proposed deep learning controller was examined and contrasted with current methods like K-Nearest Neighbor (KNN) and Feed Forward Neural Network (FFNN). The validated results show that the suggested method provides an efficient mitigation mechanism, making it suitable for all cases involving PQ issues.

Keywords: power quality; DSTATCOM; IEEE 13 system; deep learning controller; IEEE 33 system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/18/4902/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/18/4902/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:18:p:4902-:d:1749932

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().