میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,149
تعداد مقالات76,913
تعداد مشاهده مقاله154,945,690
تعداد دریافت فایل اصل مقاله116,919,152
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، فروردین 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
  1. Wu, G., Hu, D., Zhang, Y., Bao, G., & He, T. (2024). A convolutional neural network–long short-term memory–attention solar photovoltaic power prediction–correction model based on the division of twenty-four solar terms. Energies, 17(22), Article 5549. https://doi.org/10.3390/en17225549
  2. Hu, F., Zhang, L., & Wang, J. (2024). A hybrid convolutional–long short-term memory–attention framework for short-term photovoltaic power forecasting, incorporating data from neighboring stations. Applied Sciences, 14(12), Article 5189. https://doi.org/10.3390/app14125189
  3. Ledmaoui, Y., El Maghraoui, A., El Aroussi, M., & Saadane, R. (2024). Enhanced fault detection in photovoltaic panels using CNN-based classification with PyQt5 implementation. Sensors, 24(22), Article 7407. https://doi.org/10.3390/s24227407
  4. Chen, J.-H., Tan, K.-H., & Lee, Y.-D. (2022). Intelligent controlled DSTATCOM for power quality enhancement. Energies, 15(11), Article 4017. https://doi.org/10.3390/en15114017
  5. Alharkan, H., Habib, S., & Islam, M. (2023). Solar power prediction using dual stream CNN-LSTM architecture. Sensors, 23(2), Article 945. https://doi.org/10.3390/s23020945
  6. Vali, A. K., Varma, P. S., Reddy, C. R., Alanazi, A., & Elrashidi, A. (2025). Deep-learning-based controller for parallel DSTATCOM to improve power quality in distribution system. Energies, 18(18), Article 4902. https://doi.org/10.3390/en18184902
  7. Wang, J., Zhang, Z., Xu, W., Li, Y., & Niu, G. (2025). Short-term photovoltaic power forecasting using a Bi-LSTM neural network optimized by hybrid algorithms. Sustainability, 17(12), Article 5277. https://doi.org/10.3390/su17125277
  8. Kiasari, M., & Aly, H. (2025). AI-driven control strategies for FACTS devices in power quality management: A comprehensive review. Applied Sciences, 15(22), Article 12050. https://doi.org/10.3390/app152212050
  9. Ren, X., Zhang, F., Yan, J., & Liu, Y. (2024). A novel convolutional neural net architecture based on incorporating meteorological variable inputs into ultra-short-term photovoltaic power forecasting. Sustainability, 16(7), Article 2786. https://doi.org/10.3390/su16072786
  10. Garcia, C. I., et al. (2020). A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM. Applied Sciences, 10(19), Article 6755. https://doi.org/10.3390/app10196755
  11. Bui Duy, L., et al. (2024). Refining long short-term memory neural network input parameters for enhanced solar power forecasting. Energies, 17(16), Article 4174. https://doi.org/10.3390/en17164174
  12. Okwako, O. E., et al. (2022). Neural network controlled solar PV battery powered unified power quality conditioner for grid connected operation. Energies, 15(18), Article 6825. https://doi.org/10.3390/en15186825
  13. Lim, S.-C., Huh, J.-H., Hong, S.-H., Park, C.-Y., & Kim, J.-C. (2022). Solar power forecasting using CNN-LSTM hybrid model. Energies, 15(21), Article 8233. https://doi.org/10.3390/en15218233
  14. Shang, L., et al. (2023). CNN-LSTM hybrid model to promote signal processing of ultrasonic guided Lamb waves for damage detection in metallic pipelines. Sensors, 23(16), Article 7059. https://doi.org/10.3390/s23167059
  15. Kim, J. W., & Park, S. (2018). Magnetic flux leakage sensing and artificial neural network pattern recognition-based automated damage detection and quantification for wire rope non-destructive evaluation. Sensors, 18, Article 109. https://doi.org/10.3390/s18010109
  16. Bai, H., et al. (2025). Power quality disturbance classification strategy based on fast S-transform and an improved CNN-LSTM hybrid model. Processes, 13(3), Article 743. https://doi.org/10.3390/pr13030743
  17. Yang, L., Li, H., Zhang, H., Wu, Q., & Cao, X. (2024). Stochastic-distributionally robust frequency-constrained optimal planning for an isolated microgrid. IEEE Transactions on Sustainable Energy, 15, 2155–2169. https://doi.org/10.1016/j.ijepes.2025.110986
  18. Khetarpal, P., et al. (2023). Power quality disturbances detection and classification based on deep convolution auto-encoder networks. IEEE Access, 11, 46026–46038. doi: 10.1109/ACCESS.2023.3274732.
  19. Li, J., Lin, H., Liang, C., Teng, Z., & Cheng, D. (2019). Power quality disturbance detection method based on dual-resolution S-transform and learning vector quantization neural network. Transactions of China Electrotechnical Society, 34, 3453–3463. https://doi.org/10.32604/ee.2023.025900
  20. Zhang, L., Ding, Z., Xia, R., & Guo, F. (2023). Power quality data compression algorithm combining second-generation wavelet and fast Fourier transform. Southern Power System Technology, 7, (5). https://doi.org/10.3390/pr13030743
  21. Zhu, K., et al. (2024). Aiming to complex power quality disturbances: A novel decomposition and detection framework. IEEE Transactions on Industrial Informatics, 20, 4317–4326. DOI: 1049/gtd2.13200
  22. Karchi, N., et al. (2022). Adaptive least mean square controller for power quality enhancement in solar photovoltaic system. Energies, 15(23), Article 8909. https://doi.org/10.3390/en15238909
  23. Iweh, C. D., et al. (2021). Distributed generation and renewable energy integration into the grid: Prerequisites, push factors, practical options, issues and merits. Energies, 14, 5375. https://doi.org/10.3390/en14175375
  24. Patel, H., Gajjar, R., & Pandya, R. (2019). Artificial intelligence based MPPT techniques for solar PV system: A review. JETIR, 8, 1043–1059. https://ssrn.com/abstract=3708145
  25. Salem, E. Z. M., & Alranini, M. A. L. (2021). Review of maximum power point tracking algorithms of the PV system. Frontiers in Engineering and Built Environment, 1, 68–80. DOI: 1108/FEBE-03-2021-0019
  26. Pallavi, V., & Mahajan, P. (2020). Smooth LMS-based adaptive control of PV system tied to the grid for enhanced power quality. IET Power Electronics, 13, 3456–3466. https://doi.org/10.1049/iet-pel.2020.0134
  27. Avdhesh, K., & Garg, R. (2021). Control of grid integrated photovoltaic system using new variable step size least mean square adaptive filter. Electrical Engineering, 103, 2945–2959. https://doi.org/10.3390/en15238909
  28. Banik, A., et al. (2022). Design, modelling, and analysis of novel solar PV system using MATLAB. Materials Today: Proceedings, 51, 756–763. DOI: 1016/j.matpr.2021.06.226
  29. Jenisha, C. M. (2021). Decoupled control with constant DC link voltage for PV-fed single-phase grid connected systems. In Integration of renewable energy sources with smart grid (pp. 171–185). Hoboken, NJ, USA: Wiley. https://doi.org/10.3390/en15238909
  30. Babu, N. P. (2024). Adaptive grid-connected inverter control schemes for power quality enrichment in microgrid systems: Past, present, and future perspectives. Electric Power Systems Research, 230, 110288. https://doi.org/10.1016/j.epsr.2024.110288
  31. Ganthia, B. P., Mohanty, S., Rana, P. K., & Sahu, P. K. (2016). Compensation of voltage sag using DVR with PI controller. In Proceedings of ICEEOT (pp. 2138–2142). Chennai, India. doi: 10.1109/ICEEOT.2016.7755068.
  32. Rajendran, G., Raute, R., & Caruana, C. (2025). A comprehensive review of solar PV integration with smart-grids: Challenges, standards, and grid codes. Energies, 18(9), Article 2221. https://doi.org/10.3390/en18092221
  33. Li, P., & Huo, X. (2025). Simplified finite control set model predictive control for single-phase grid-tied inverters with twisted parameters. Electric Power Systems Research, 238, 111063. https://doi.org/10.3390/su17198729
  34. SivaramKrishnan, M., et al. (2025). Smart charging solution for electric vehicles: Leveraging grid connected solar PV with UPQC using HBA-MORARNN approach. Energy Reports, 13, 2454–2467. https://doi.org/10.1016/j.egyr.2025.02.004
  35. Ganthia, B. P., Pradhan, R., Das, S., & Ganthia, S. (2017). Analytical study of MPPT based PV system using fuzzy logic controller. In Proceedings of ICECDS (pp. 3266–3269). Chennai, India. doi: 10.1109/ICECDS.2017.8390063.
  36. Rubavathy, S. J., et al. (2021). Smart grid based multiagent system in transmission sector. In Proceedings of ICIRCA (pp. 1–5). Coimbatore, India. doi: 10.1109/ICIRCA51532.2021.9544644.
  37. Sahu, P. K., Mohanty, A., Ganthia, B. P., & Panda, A. K. (2016). A multiphase interleaved boost converter for grid-connected PV system. In Proceedings of MicroCom (pp. 1–6). Durgapur, India. doi: 10.1109/MicroCom.2016.7522539.
  38. Mohanty, M., Nayak, N., Ganthia, B. P., & Behera, M. K. (2023). Power smoothening of photovoltaic system using dynamic PSO with ESC under partial shading condition. In Proceedings of APSIT (pp. 675–680). Bhubaneswar, India. doi: 10.1109/APSIT58554.2023.10201763.
آمار
تعداد مشاهده مقاله: 264
تعداد دریافت فایل اصل مقاله: 216





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب