Hybrid DSTATCOM Design Using Deep Belief Networks for Enhanced Power Quality Assessment

Pappu Suneetha; Kanna Subba Ramaiah; Nagalamadaka Visali

doi:10.2478/pead-2026-0008

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Hybrid DSTATCOM Design Using Deep Belief Networks for Enhanced Power Quality Assessment

Power Electronics and Drives

Volume 11 (2026): Issue 1 (January 2026)

By: Pappu Suneetha , Kanna Subba Ramaiah and Nagalamadaka Visali

Open Access

|Apr 2026

Figures & Tables

Circuit connection of the DBLN based DSTATCOM. DBLN, deep belief learning network; DSTATCOM, distributed static compensator; VSC, voltage source converter.

Circuit connection of the DBLN-based LC supported. DBLN, deep belief learning network; DSTATCOM, distributed static compensator; VSC, voltage source converter.

P3W Voltage Source Inverter-based DSTATCOM. DSTATCOM, distributed static compensator.

Stability analysis of LC filter by using Bode plot.

Overall DBLN control algorithm of DSTATCOM. DBLN, deep belief learning network; DSTATCOM, distributed static compensator.

Flow chart for finding the tuned weight to improve the shunt compensation using DBLN technique. DBLN, deep belief learning network.

Learning mechanism using DBLN for the extraction of reactive part for a-phase. DBLN, deep belief learning network.

(a) System performance for DSTATCOM based on DBLN, (b). THD of the load current for DSTATCOM based under DBLN, (c). THD of the source current for DSTATCOM based on DBLN, (d). Source side power factor p.f of the phase-a, (e). Load side power factor of the phase-a. DBLN, deep belief learning network; DSTATCOM, distributed static compensator; THD, total harmonic distortions.

(a) System performance for LC supported DSTATCOM based on DBLN, (b). THD of the load current for LC supported DSTATCOM based under DBLN, (c). THD of the source current for LC supported DSTATCOM based under DBLN, (d). Source side power factor of the phase-a, (e). Load side power factor of the phase-a. DBLN, deep belief learning network; DSTATCOM, distributed static compensator; THD, total harmonic distortions.

Experimental setup of the DBLN-based LC-supported DSTATCOM. DBLN, deep belief learning network; DSTATCOM, distributed static compensator.

Experimental waveform of source current, compensator current and load current using DBLN mechanism under (a) constant loading, (b) diversity loading. DBLN, deep belief learning network.

Experimental results of source current, current of compensator and current of using DBLN controlled LC coupled DSTATCOM for (a). Constant loading, (b) diversity loading. DBLN, deep belief learning network; DSTATCOM, distributed static compensator.

Comparison of different algorithms for analysis of filtering characteristics

Control algorithm	Year of publication	Robustness	Convergence capability	Control flexibility	Power loss	Cost	LC coupling	Filtering process
ALMS	2018	✓	×	-	-	-	×	Active
KHLMS	2019	✓	✓	---	---	--	×	Active
DRL	2020	✓	✓	---	--	--	×	Active
Delta-Bar-Delta NN	2020	✓	✓	--	--	--	×	Active
SLMS	2021	✓	✓	---	--	--	×	Active
ADALINE-LMS	2021	✓	×	-	-	-	×	Active
DL	2022	✓	✓	--	-	--	×	Active
DBLN	2023	✓	✓	---	-	--	✓	Hybrid

Comparative performance evaluation of different types of DSTATCOM_

Performance parameter	ALMS based DSTATCOM	DBLN based DSTATCOM	DBLN based LC supported DSTATCOM
i_s (A), %THD	55.88, 4.56	55.46, 4.07	54.46, 2.07
v_s (V), %THD	321.4, 2.54	321.4, 2.23	321, 1.42
i_l (A), %THD	42.96, 18.96	51.13, 20.64	51.13, 20.64
Power factor	0.94	0.97	0.99
v_dc (V)	680	671.6	605.6

Simulation/experimental parameters for proposed configuration_

Symbol	Definition	Value
v_s	3-phase source voltage	230 V/phase
f_s	Frequency	50 Hz
R_s	Source resistance	0.5 Ω
L_s	Source inductance	2 mH
L_f	Passive filter inductance	0.5 mH
C_f	Passive filter capacitance	10 μF
K_pr	AC proportional controller	0.2
C_dc	Capacitor	2,000 μF
K_pa	DC proportional controller	0.01
K_ia	DC integral controller	0.05
v_dc _(ref)	DC link voltage	650 V
R_c	VSC resistance	0.25 Ω
L_c	VSC inductance	1.5 mH
K_ir	AC integral controller	1.1

References

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DOI: https://doi.org/10.2478/pead-2026-0008 | Journal eISSN: 2543-4292 | Journal ISSN: 2451-0262

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Language: English

Page range: 128 - 141

Submitted on: Dec 20, 2025

Accepted on: Mar 4, 2026

Published on: Apr 17, 2026

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

distributed static compensator,

deep belief learning network,

inductor and capacitor coupling,

power quality

Related subjects:

Computer sciences,

Artificial intelligence,

Engineering,

Electrical engineering,

Electronics

© 2026 Pappu Suneetha, Kanna Subba Ramaiah, Nagalamadaka Visali, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 11 (2026): Issue 1 (January 2026)

Hybrid DSTATCOM Design Using Deep Belief Networks for Enhanced Power Quality Assessment

Figures & Tables

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Comparison of different algorithms for analysis of filtering characteristics

Comparative performance evaluation of different types of DSTATCOM_

Simulation/experimental parameters for proposed configuration_

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