Journal of Solar Energy Research

Quantum Reinforcement Learning–Enabled DSTATCOM for Transient Power Quality Stabilization in High-Penetration PV Distribution System

Document Type : Research Article

Authors

1 Department of Electrical Engineering, Biju Pattnaik University of Technology, Rourkela, Odisha, India

2 Electrical Engineering, Government College of Engineering Kalahandi, Odisha, India

3 Electrical and Electronics Engineering, Sreenidhi institute of science and Technology Hyderabad, India

Abstract

This study proposes a Quantum Reinforcement Learning-based Distribution Static Compensator (QRL-DSTATCOM) for transient power quality enhancement in high-penetration photovoltaic (PV) distribution systems. Increasing integration of PV generation introduces significant challenges, including voltage instability, harmonic distortion, and power factor degradation due to stochastic irradiance and rapid load fluctuations. To address these issues, a hybrid quantum-classical reinforcement learning framework is developed, incorporating quantum state encoding and amplitude-enhanced exploration to improve learning efficiency and control performance. The proposed QRL-DSTATCOM enables faster convergence and superior policy optimization compared to conventional RL-based compensators. The system is modeled and validated in MATLAB/Simulink under diverse operating scenarios, including variable irradiance, sudden load changes, and grid fault conditions. Simulation results demonstrate that the proposed approach achieves 43–50% faster stabilization time, significantly reduced total harmonic distortion (THD) well within IEEE-519 limits, and maintains bus voltage within ±5% of nominal values under severe disturbances. Overall, the framework validated using OPAL-RT real time software and presents a scalable and intelligent solution for next-generation smart distribution networks with high renewable energy penetration, ensuring improved stability, robustness, and power quality performance.

Keywords

  1. Omoboye, H. J., Olusesi, O. A., Edirin, O. B., & Peter, G. F. (2026). Impact of D-STATCOM and SVC on power quality of low voltage radial distribution networks. International Journal of Recent Engineering Science (IJRES), 13(2), 1–8. doi.org/10.14445/23497157/IJRES-V13I2P101.
  2. Suryawanshi, V., Surbhi, & Gupta, A. (2026). Adaptive control of DSTATCOM using hybrid NN-BAT optimization for voltage stability and THD reduction. In Proceedings of the 2026 International Conference on Intelligent Computing and Automation for Sustainable Solutions (ICASS) (pp. 1–8). Faridabad, India. doi.org/10.1109/ICASS69550.2026.11547552.
  3. Mangaraj, M., Pilla, R., Kalabarige, L. R., Potnuru, U. K., & Holla, M. R. (2026). DBLN-based IC-DSTATCOM for power-line conditioning in the power distribution system. IEEE Access, 14, 79284–79296. doi.org/10.1109/ACCESS.2026.3685356.
  4. Mahla, R., & Garg, M. M. (2026). Power quality improvement using E-SOGI controlled DSTATCOM with wind generation. In Proceedings of the 2026 4th Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON) (pp. 1–6). Bhubaneswar, India. doi.org/10.1109/ODICON66687.2026.11470800
  5. Kaliappan, S., Priya, R. K., Sethi, R., et al. (2026). A comparative study of various optimization techniques for enhancing energy management in hybrid microgrids. Journal of The Institution of Engineers (India): Series B. DOI: 10.1007/s40031-026-01310-y
  6. Wu, Z., Chen, C., Xu, D., & Guan, L. (2025). Frequency-constrained economic dispatch of microgrids considering frequency response performance. Energies, 18(8), 2014. DOI: 10.3390/en18082014
  7. 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. DOI: 10.1016/j.epsr.2024.110288
  8. Bai, H., Yao, R., Zhang, W., Zhong, Z., & Zou, H. (2025). Power quality disturbance classification strategy based on fast S-transform and an improved CNN-LSTM hybrid model. Processes, 13(3), 743. DOI: 10.3390/pr13030743
  9. 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: 10.1049/gtd2.13200
  10. 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), 2221. DOI: 10.3390/en18092221
  11. Meziane, S., Ryad, T., Assolami, Y. O., & Aljohani, T. M. (2025). A hierarchical two-layer MPC-supervised strategy for efficient inverter-based small microgrid operation. Sustainability, 17(19), 8729. DOI: 10.3390/su17198729
  12. Chen, J.-H., Tan, K.-H., & Lee, Y.-D. (2022). Intelligent controlled DSTATCOM for power quality enhancement. Energies, 15(11), 4017. DOI: 10.3390/en15114017
  13. Sivaram Krishnan, 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. DOI: 10.1016/j.egyr.2025.02.004
  14. 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), 5549. DOI: 10.3390/en17225549
  15. 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), 5189. DOI: 10.3390/app14125189
  16. 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), 7407. DOI: 10.3390/s24227407
  17. Alharkan, H., Habib, S., & Islam, M. (2023). Solar power prediction using dual stream CNN-LSTM architecture. Sensors, 23(2), 945. DOI: 10.3390/s23020945
  18. 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), 4902. DOI: 10.3390/en18184902
  19. 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), 5277. DOI: 10.3390/su17125277
  20. Kiasari, M., & Aly, H. (2025). AI-driven control strategies for FACTS devices in power quality management: A comprehensive review. Applied Sciences, 15(22), 12050. DOI: 10.3390/app152212050
  21. 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), 2786. DOI: 10.3390/su16072786
  22. 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), 6755. DOI: 10.3390/app10196755
  23. Bui Duy, L., et al. (2024). Refining long short-term memory neural network input parameters for enhanced solar power forecasting. Energies, 17(16), 4174. DOI: 10.3390/en17164174
  24. Okwako, O. E., et al. (2022). Neural network controlled solar PV battery powered unified power quality conditioner for grid connected operation. Energies, 15(18), 6825. DOI: 10.3390/en15186825
  25. Hao, S., Qi, T., Li, J., Ma, X., & Sun, S. (2026). A PV hot-spot fault detection network based on image segmentation and multi-scale feature fusion under complex background. IEEE Transactions on Automation Science and Engineering. DOI: 10.1109/TASE.2026.3702202
  26. Karchi, N., et al. (2022). Adaptive least mean square controller for power quality enhancement in solar photovoltaic system. Energies, 15(23), 8909. DOI: 10.3390/en15238909
  27. 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. DOI: 10.3390/en14175375
  28. Ganthia, B. P., Mohanty, S., Rana, P. K., & Sahu, P. K. (2016). Compensation of voltage sag using DVR with PI controller. In Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 2138–2142). Chennai, India. DOI: 10.1109/ICEEOT.2016.7755068
  29. Ganthia, B. P., Pradhan, R., Das, S., & Ganthia, S. (2017). Analytical study of MPPT based PV system using fuzzy logic controller. In Proceedings of the International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3266–3269). Chennai, India. DOI: 10.1109/ICECDS.2017.8390063
  30. Rubavathy, S. J., et al. (2021). Smart grid based multiagent system in transmission sector. In Proceedings of the International Conference on Intelligent Computing, Information and Control Systems for Communication (ICIRCA) (pp. 1–5). Coimbatore, India. DOI: 10.1109/ICIRCA51532.2021.9544644
  31. 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 the International Conference on Microelectronics, Computing and Communications (MicroCom) (pp. 1–6). Durgapur, India. DOI: 10.1109/MicroCom.2016.7522539
  32. 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 the International Conference on Advances in Power, Signal, and Information Technology (APSIT) (pp. 675–680). Bhubaneswar, India. DOI: 10.1109/APSIT58554.2023.10201763
  33. Suresh, D., Rao, D. M., & Sukumar, G. D. (2016). Reduced rating hybrid DSTATCOM for three phase four wire distribution system. In Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1–4). Delhi, India. DOI: 10.1109/ICPEICES.2016.7853646
  34. Hussain, I., Agarwal, R. K., & Singh, B. (2021). Delayed LMS based adaptive control of PV-DSTATCOM system. In Proceedings of the 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies (ICEPE) (pp. 1–6). Shillong, Meghalaya, India. DOI: 10.1109/ICEPE50861.2021.9404442
  35. Kundela, P. K. Y., Mangaraj, M., & Sudabattula, S. K. (2022). Operation of inductively coupled DSTATCOM for power quality enhancement. In Proceedings of the International Mobile and Embedded Technology Conference (MECON) (pp. 210–214). Noida, India. DOI: 10.1109/MECON53876.2022.9752372
  36. Yarlagadda, V., Karthika, G. A., Nagajyothi, M., & Bhavani, J. (2022). DSTATCOM based closed loop controlled wind power plant with self excited induction generator for controlling terminal voltage against load disturbances. In Proceedings of the IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1–6). Bengaluru, India. DOI: 10.1109/ICAECC54045.2022.9716651
  37. Li, G., Shi, S., Chen, J., & Liu, X. (2026). A voltage self-stability control of STATCOM for damping subsynchronous oscillation in photovoltaic power station under weak grid. IEEE Transactions on Industrial Electronics, 73(2), 2139–2149. DOI: 10.1109/TIE.2025.3595993
  38. Hurkadli, P., & Kulkarni, D. B. (2025). Coordinated energy management and reactive power control for enhancing microgrid voltage stability using electric vehicles. In Proceedings of the 2nd Asia Pacific Conference on Innovation in Technology (APCIT) (pp. 1–6). Mysore, India. DOI: 10.1109/APCIT65661.2025.11411633
  39. Thomas, N., & N, N. (2025). Voltage control strategy in single phase grid tied systems based on D-STATCOM PV inverter. In Proceedings of the IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS) (pp. 1–7). Coimbatore, India. DOI: 10.1109/ICINVENTS64613.2025.11401419
  40. Kumar, C. M. S., & Gopalakrishnan, V. (2025). Enhancing power quality in renewable energy through hybrid solutions. In Proceedings of the 25th Conference of the Electric Power Supply Industry (CEPSI) (pp. 1047–1053). Singapore, Singapore. DOI: 10.1109/CEPSI66359.2025.11403241
Journal of Solar Energy Research
Volume 11, Issue 2
April 2026
Pages 3890-3908