Manjur Elahi, Mir, Ray, Prakash, Puhan, Pratap. (1404). Convolutional Neural Network–Long Short-Term Memory Based Intelligent Adaptive DSTATCOM Control for Enhanced Power Quality in Photovoltaic-Integrated Distribution Networks. , 11(1), 2780-2801. doi: 10.22059/jser.2026.407530.1677
Mir Manjur Elahi; Prakash Kumar Ray; Pratap Sekhar Puhan. "Convolutional Neural Network–Long Short-Term Memory Based Intelligent Adaptive DSTATCOM Control for Enhanced Power Quality in Photovoltaic-Integrated Distribution Networks". , 11, 1, 1404, 2780-2801. doi: 10.22059/jser.2026.407530.1677
Manjur Elahi, Mir, Ray, Prakash, Puhan, Pratap. (1404). 'Convolutional Neural Network–Long Short-Term Memory Based Intelligent Adaptive DSTATCOM Control for Enhanced Power Quality in Photovoltaic-Integrated Distribution Networks', , 11(1), pp. 2780-2801. doi: 10.22059/jser.2026.407530.1677
Manjur Elahi, Mir, Ray, Prakash, Puhan, Pratap. Convolutional Neural Network–Long Short-Term Memory Based Intelligent Adaptive DSTATCOM Control for Enhanced Power Quality in Photovoltaic-Integrated Distribution Networks. , 1404; 11(1): 2780-2801. doi: 10.22059/jser.2026.407530.1677

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

Journal of Solar Energy Research
دوره 11، شماره 1، بهار 2026، صفحه 2780-2801    اصل مقاله (1.45 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22059/jser.2026.407530.1677
نویسندگان
Mir Manjur Elahi* 1؛ Prakash Kumar Ray2؛ Pratap Sekhar Puhan3
1Department of Electrical Engineering, Biju Pattnaik University of Technology, Rourkela, Odisha, India
2School of Electrical Sciences, Odisha University of Technology and Research, Bhubaneswar, India
3Department of Electrical and Electronics Engineering, Sreenidhi institute of science and Technology Hyderabad, India
چکیده
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.
کلیدواژه‌ها
Unified Power Quality Conditioner؛ CNN-LSTM Hybrid؛ Power Quality؛ Photovoltaic Integration؛ Intelligent Adaptive Control
مراجع
آمار
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