Convolutional Neural Network–Long Short-Term Memory Based Intelligent Adaptive DSTATCOM Control for Enhanced Power Quality in Photovoltaic-Integrated Distribution Networks

Document Type : Research Article

Authors
Mir Manjur Elahi 1
Prakash Kumar Ray 2
Pratap Sekhar Puhan 3
1 Department of Electrical Engineering, Biju Pattnaik University of Technology, Rourkela, Odisha, India
2 School of Electrical Sciences, Odisha University of Technology and Research, Bhubaneswar, India
3 Department of Electrical and Electronics Engineering, Sreenidhi institute of science and Technology Hyderabad, India
10.22059/jser.2026.407530.1677
Abstract
This paper presents a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) based intelligent adaptive control strategy for a DSTATCOM to enhance power quality in photovoltaic (PV)-integrated distribution networks. The proposed controller exploits CNN-based spatial feature extraction of voltage–current waveforms and LSTM-based temporal learning to address nonlinear and time-varying disturbances. Simulation and real-time hardware-in-the-loop (HIL) validation using an OPAL-RT platform confirm superior performance under dynamic irradiance (200–1000 W/m²) and load variations (up to 50 kW). The DC-link voltage is tightly regulated within 758–764 V, with a maximum deviation below 3 V. Total Harmonic Distortion (THD) is reduced from 16.5–22.5% to 3.2–4.5%, achieving 79–82% harmonic suppression and compliance with IEEE-519 limits. The power factor improves from 0.78–0.84 to 0.97–0.99, approaching unity. The proposed controller exhibits a fast dynamic response of 3.8 ms, outperforming PI, Fuzzy-PID, ANN, CNN, and LSTM controllers. Reactive power tracking errors remain below 2.7%, demonstrating high robustness and real-time adaptability for smart PV-integrated grids.
Keywords
Unified Power Quality Conditioner
CNN-LSTM Hybrid
Power Quality
Photovoltaic Integration
Intelligent Adaptive Control
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Journal of Solar Energy Research
Volume 11, Issue 1
Winter 2026
Pages 2780-2801