Intelligent Power Quality Enhancement Strategies for PV-Integrated Smart Distribution Networks: A State-of-the-Art Review

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Mir Manjur Elahi,

  • Prakash Kumar Ray,

  • Pratap Sekhar Puhan,

  1. Assistant Professor, Professor, Department of Electrical Engineering, Biju Pattnaik University of Technology, Rourkela, Odisha, India
  2. Associate Professor, School of Electrical Sciences, Odisha University of Technology and Research, Bhubaneswar, Odisha, India
  3. Professor, Department of Electrical and Electronics Engineering, Sreenidhi institute of science and Technology,Hyderabad, Telangana, India

Abstract

The rapid integration of photovoltaic (PV) systems into modern power distribution networks has introduced significant challenges related to power quality. Issues such as voltage fluctuations, harmonic distortion, flicker, and reactive power imbalance arise due to the intermittent and nonlinear nature of solar energy generation. This paper presents a concise literature review of various power quality enhancement techniques employed in PV-integrated networks. Key approaches include the use of active power filters (APFs), dynamic voltage restorers (DVRs), unified power quality conditioners (UPQCs), and advanced control strategies such as fuzzy logic, artificial neural networks (ANN), and model predictive control (MPC). Additionally, grid-connected inverter control techniques and energy storage integration are discussed as effective solutions for mitigating disturbances. The review highlights the comparative performance of these techniques in improving voltage stability, reducing total harmonic distortion (THD), and ensuring reliable grid operation. The findings indicate that hybrid and intelligent control-based approaches offer superior performance in dynamic and uncertain conditions. Power quality issues in PV systems may now be detected and compensated for in real time thanks to recent advancements in smart grid technology and Internet of Things (IoT)-based monitoring systems. The efficiency, dependability, and fault management capabilities of the system have all been enhanced by the use of adaptive and data-driven optimization techniques. This study provides insights into the current trends and future directions for enhancing power quality in PV- based power systems.

Keywords: Photovoltaic systems, Power quality, Harmonic distortion, Active power filter, UPQC, Smart grid, Renewable energy integration, Voltage stability

How to cite this article:
Mir Manjur Elahi, Prakash Kumar Ray, Pratap Sekhar Puhan. Intelligent Power Quality Enhancement Strategies for PV-Integrated Smart Distribution Networks: A State-of-the-Art Review. International Journal of Electrical Power and Machine Systems. 2026; 04(01):-.
How to cite this URL:
Mir Manjur Elahi, Prakash Kumar Ray, Pratap Sekhar Puhan. Intelligent Power Quality Enhancement Strategies for PV-Integrated Smart Distribution Networks: A State-of-the-Art Review. International Journal of Electrical Power and Machine Systems. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijepms/article=2026/view=245086


References

  1. Bajaj M, Singh AK. A streamlined approach for assessing the power quality in renewable energy systems. In2020 IEEE 17th India council international conference (INDICON) 2020 Dec 10 (pp. 1-6). IEEE.
  2. Kumar C, Mishra MK, Mekhilef S. A new voltage control strategy to improve performance of DSTATCOM in electric grid. CES Transactions on Electrical Machines and Systems. 2020 Dec;4(4):295-302.
  3. Dahdouh A, Mazouz L, Youcefa BE. Nonlinear predictive direct power control based on space vector modulation of 3-phase 3-level solar PV integrated unified power quality conditioner. European Journal of Electrical Engineering. 2022 Apr 1;24(2):77.
  4. Karanki SB, Geddada N, Mishra MK, Kumar BK. A DSTATCOM topology with reduced DC-link voltage rating for load compensation with nonstiff source. IEEE Transactions on Power Electronics. 2011 Aug 8;27(3):1201-11.
  5. Kallon MA, Nyakoe GN, Muriithi CM. DSTATCOM application for distribution network power quality enhancement: A review. 2021 IEEE PES/IAS PowerAfrica. 2021 Aug 23:1-5.
  6. SUDABATTULA SK, Mangaraj M, Sudabattula SK. Cascaded inductively coupled DSTATCOM for improved PQ performance. International Journal of Renewable Energy Research (IJRER). 2022 Dec 15;12(4):1787-96.
  7. Kouadria MA, Allaoui T, Denai M. A hybrid fuzzy sliding-mode control for a three-phase shunt active power filter. Energy Systems. 2017 May;8(2):297-308.
  8. Ghiasi M. Technical and economic evaluation of power quality performance using FACTS devices considering renewable generations. Renewable Energy Focus. 2019 Jun 1;29:49-62.
  9. Ray P, Ray PK, Dash SK. Power quality enhancement and power flow analysis of a PV integrated UPQC system in a distribution network. IEEE Transactions on Industry Applications. 2021 Dec 6;58(1):201-11.
  10. Das SK, Swain SC, Dash R, Reddy J, Chinthaginjala R, Jena R, Kotb H, ELrashidi A. Design and analysis of UPQC in a microgrid using model reference adaptive control ensemble with back-stepping controller. Heliyon. 2024 Jul 30;10(14).
  11. Mohammed DM, Hassan RF, Yasin NM, Alruwaili M, Ibrahim MA. A Comparative study of direct power control strategies for STATCOM using three-level and five-level diode-clamped inverters. Energies. 2025 Jul 7;18(13):3582.
  12. Irfan MM, Alharbi M, Basha CH. Artificial neural network controlled DSTATCOM for mitigating power quality concerns in solar PV and wind system. Scientific Reports. 2025 Feb 11;15(1):5016.
  13. Singh AR, Dashtdar M, Bajaj M, Garmsiri R, Blazek V, Prokop L, Misak S. AI-enhanced power quality management in distribution systems: implementing a dual-phase UPQC control with adaptive neural networks and optimized PI controllers. Artificial Intelligence Review. 2024 Sep 30;57(11):311.
  14. Fawzy IY, Mossa MA, Elsawy AM, Suwarno I, Diab AA. Deployment of STATCOM with fuzzy logic control for improving the performance of power system under different faults conditions. Journal of Robotics and Control (JRC). 2024 Mar 22;5(3):636-46.
  15. Lolamo M, Kumar R, Sharma V. Enhancing power quality of PV-DSTATCOM integrated grid with modified adaptive LMS control. Electrical Engineering. 2025 Apr;107(4):4601-14.
  16. Mohanraj MR, Prakash R. A unified power quality conditioner for power quality improvement in distributed generation network using adaptive distributed power balanced control (ADPBC). International Journal of Wavelets, Multiresolution and Information Processing. 2020 Jan 2;18(01):1941021.
  17. Blaabjerg F, Teodorescu R, Liserre M, Timbus AV. Overview of control and grid synchronization for distributed power generation systems. IEEE Transactions on industrial electronics. 2006 Oct 2;53(5):1398-409.
  18. Palanisamy T, Thangamuthu GS. Optimal power quality improvement in distribution system with UPQC using an improved strategy. Optimal Control Applications and Methods. 2024 Jul;45(4):1433- 55.
  19. Shi J, Noshadi A, Kalam A, Shi P. Fuzzy logic control of DSTATCOM for improving power quality and dynamic performance. In2015 Australasian universities power engineering conferenCE (AUPEC) 2015 Sep 27 (pp. 1-6). IEEE.
  20. Aldbaiat B, Nour M, Radwan E, Awada E. Grid-connected PV system with reactive power management and an optimized SRF-PLL using genetic algorithm. Energies. 2022 Mar 16;15(6):2177.
  21. Mallajoshula R, Naidu IE. Novel DSOGI-Based SRF Controller for Power Quality Enhancement in Three-Phas e-Gri d-Con necte d Dynamic Voltage Restorer Integrated With Photovoltaic System. Electrica. 2025 Jan 1;25(1).
  22. Chaudhari MB, Patel ND, Bhavsar PR, Vaghamshi AL, Mali SD, Patni VN, Jaradi PK, Trivedi T, Gupta C. Implementation of Shunt Active Power Filter using DSP Controller based on Synchronous Reference Frame for Harmonic Mitigation. jes. 2024 Jul;20(10s):2043-8.
  23. Parthiban S, Madhaiyan V. Experimental validation of solar PV sustained ZSI based unified active power filter for enrichment of power quality. Automatika. 2021 Jan 2;62(1):137-53.
  24. Khorasani PG, Joorabian M, Seifossadat SG. Smart grid realization with introducing unified power quality conditioner integrated with DC microgrid. Electric Power Systems Research. 2017 Oct 1;151:68-85.
  25. Mary MP, Sivachidambaranathan V. Enhancement of active power filter operational performance using SRF theory for renewable source. Indian Journal of Science and Technology. 2015 Sep;8(21):1-7.
  26. Dey P, Mekhilef S. Synchronous reference frame based control technique for shunt hybrid active power filter under non-ideal voltage. In2014 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) 2014 May 20 (pp. 481-486). IEEE.
  27. Önal Y. A New Controller For Photovoltaic Panel Fed Unified Power Quality Conditioner To Power Quality Improvement. Mugla Journal of Science and Technology. 2021 Jun 6;7(1):14-24.
  28. Li D, Wang T, Pan W, Ding X, Gong J. A comprehensive review of improving power quality using active power filters. Electric Power Systems Research. 2021 Oct 1;199:107389.
  29. Dash DK, Sadhu PK. A review on the use of active power filter for grid-connected renewable energy conversion systems. Processes. 2023 May 12;11(5):1467.
  30. Reguieg Z, Bouyakoub I, Mehedi F, Bouhadji F. Enhancing electrical grid stability through power quality optimization via PV-PO-UPQC: An integrated approach. In2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC) 2024 May 12 (pp. 1-7). IEEE.
  31. USMAN Z, AZMI M, MOHAMMAD NS, MUSA S. Performance improvement of harmonic detection algorithm in three phase three wire shunt active power filter under balance voltage condition. INTERNATIONAL JOURNAL. 2025;16(1):380.
  32. Kołek K. Active Power Filter Controller Applied to Quality Improvement of Periodic Loads. In2025 29th International Conference on Methods and Models in Automation and Robotics (MMAR) 2025 Aug 26 (pp. 284-288). IEEE.
  33. Bourourou F, Tadjer SA, Habi I. ACTIVE POWER FILTER IMPACT ON POWER QUALITY IMPROVEMENT FOR INDUSTRUAL PLANT SUPPLIED BY PHOTOVOLTAIQUE STATION. Journal of Electrical Systems. 2023 Mar 1;19(1).
  34. Bajaj M, Singh AK, Alowaidi M, Sharma NK, Sharma SK, Mishra S. Power quality assessment of distorted distribution networks incorporating renewable distributed generation systems based on the analytic hierarchy process. IEEE Access. 2020 Aug 5;8:145713-37.
  35. Singh S, Letha SS. Various custom power devices for power quality improvement: a review. In2018 international conference on power energy, environment and intelligent control (PEEIC) 2018 Apr 13 (pp. 689-695). IEEE.
  36. Qu Z, Dong Y, Mugemanyi S, Yu T, Bo X, Li H, Li Y, Rugema FX, Bananeza C. Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system. IET Renewable Power Generation. 2022 May;16(7):1401-24.
  37. Wang P, Bi J, Li F, Liu C, Sun Y, Cheng W, Wang Y, Kang W. Research on Energy Storage-Based DSTATCOM for Integrated Power Quality Enhancement and Active Voltage Support. Electronics. 2025 Jul 15;14(14):2840.
  38. Kwan KH, Png YS, Chu YC, So PL. Model predictive control of unified power quality conditioner for power quality improvement. In2007 IEEE International Conference on Control Applications 2007 Oct 1 (pp. 916-921). IEEE.
  39. Singh B, Al-Haddad K, Chandra A. A review of active filters for power quality improvement. IEEE transactions on industrial electronics. 1999 Oct 31;46(5):960-71.
  40. Hossain E, Tür MR, Padmanaban S, Ay S, Khan I. Analysis and mitigation of power quality issues in distributed generation systems using custom power devices. Ieee Access. 2018 Mar 12;6:16816-33.
  41. Kumar M, Swarnkar A, Gupta N, Niazi KR. Design and operation of DSTATCOM for power quality improvement in distribution systems. The Journal of Engineering. 2017;2017(13):2328-33.
  42. Uyyuru KR, Mishra MK. Comparison of Control Strategies for DSTATCOM in Power Distribution System. International Journal of Emerging Electric Power Systems. 2011 Jul 1;12(3).
  43. Chen JH, Tan KH, Lee YD. Intelligent controlled DSTATCOM for power quality enhancement. Energies. 2022 May 30;15(11):4017.
  44. Irfan MM, Alharbi M, Basha CH. Artificial neural network controlled DSTATCOM for mitigating power quality concerns in solar PV and wind system. Scientific Reports. 2025 Feb 11;15(1):5016.
  45. Varshney S, Srivastava L, Pandit M. ANN based control of Statcom for improving voltage profile in power system. InIndia International Conference on Power Electronics 2010 (IICPE2010) 2011 Jan 28 (pp. 1-7). IEEE.
  46. Capizzi G, Bonanno F, Napoli C. Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources. In2011 international conference on clean electrical power (ICCEP) 2011 Jun 14 (pp. 336-340). IEEE.
  47. Sugavanam KR, Jeyabharath R, Veena P. Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije. 2021 Oct 20;62(3-4):471-85.
  48. Ibrahim NF, Mahmoud MM, Al Thaiban AM, Barnawi AB, Elbarbary ZM, Omar AI, Abdelfattah H. Operation of grid-connected PV system with ANN-based MPPT and an optimized LCL filter using GRG algorithm for enhanced power quality. IEEE access. 2023 Sep 22;11:106859-76.
  49. Mangaraj M, Panda AK, Penthia T. Neural network control technique based sensorless DSTATCOM for the power conditioning. In2015 Annual IEEE India Conference (INDICON) 2015 Dec 17 (pp. 1-6). IEEE.
  50. Mahar H, Munir HM, Soomro JB, Akhtar F, Hussain R, Elnaggar MF, Kamel S, Guerrero JM. Implementation of ANN controller based UPQC integrated with microgrid. Mathematics. 2022 Jun 9;10(12):1989.
  51. Ramana PV, Rosalina KM. Power Quality Enhancement Using Artificial Neural Network-Proportional Integral Controller and Fuzzy Granular Controller for DSTATCOM Integrated with Renewable Energy and Battery Storage System. Journal of New Materials for Electrochemical Systems. 2024 Dec 1;27(4).
  52. Iqbal M, Jawad M, Jaffery MH, Akhtar S, Rafiq MN, Qureshi MB, Ansari AR, Nawaz R. Neural networks based shunt hybrid active power filter for harmonic elimination. IEEE Access. 2021 May 3;9:69913-25.
  53. Patel SK, Arya SR, Maurya R. Optimal step LMS-based control algorithm for DSTATCOM in distribution system. Electric Power Components and Systems. 2019 May 9;47(8):675-91.
  54. Arya SR, Singh B. Performance of DSTATCOM using leaky LMS control algorithm. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2013 Jun 6;1(2):104-13.
  55. Reguieg Z, Bouyakoub I, Mehedi F. Harmonic mitigation in grid-integrated renewable energy systems with nonlinear loads. Energy. 2025 Jun 1;324:135882.
  56. Penthia T, Panda AK, Dash AR, Mangaraj M. Performance of modified-leaky LMS control algorithm for supercapacitor based SAPF. In2018 IEEMA Engineer Infinite Conference (eTechNxT) 2018 Mar 13 (pp. 1-6). IEEE.
  57. Elmetwaly AH, Eldesouky AA, Sallam AA. An adaptive D-FACTS for power quality enhancement in an isolated microgrid. IEEE Access. 2020 Mar 17;8:57923-42.
  58. Rahman S, Khan I, Ayaz MA, Iqbal A. A distributed filtering-based comprehensive framework to optimize grid performance of harmonic-polluted distribution systems. IEEE Transactions on Industry Applications. 2024 Jul 24;60(6):8159-67.
  59. Ehtesham M, Ahmad M, Kirmani S. LMS algorithm computation for designing adaptive filtering technique to enhance power quality of integrated power system with non-linear load. Journal of The Institution of Engineers (India): Series B. 2025 Dec;106(6):1737-48.
  60. Varghese LJ, Jacob SS, Selvaraj J, Azerefegn TM. Optimized CNN-BiLSTM framework for reactive power management and voltage profile improvement in renewable energy based power grids. Scientific Reports. 2025 Nov 20;15(1):41018.
  61. Rizeakos V, Bachoumis A, Andriopoulos N, Birbas M, Birbas A. Deep learning-based application for fault location identification and type classification in active distribution grids. Applied Energy. 2023 May 15;338:120932.
  62. Shi J, Foggo B, Yu N. Power system event identification based on deep neural network with information loading. IEEE Transactions on Power Systems. 2021 May 14;36(6):5622-32.
  63. Zhang J, Pan L, Han QL, Chen C, Wen S, Xiang Y. Deep learning based attack detection for cyber- physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica. 2021 Sep 13;9(3):377-91.
  64. Abbasi M, Prieto J, Valdeolmillos D, Rollán SA. Hybrid CNN-LSTM Model for Energy Consumption Prediction Using Fourier Transform Features in Renewable Energy Communities. InInternational Symposium on Distributed Computing and Artificial Intelligence 2025 Jun 25 (pp. 151-161). Cham: Springer Nature Switzerland.
  65. Wang J. Electric Power Prediction Based On CNN-LSTM Network Model. Academic Journal of Computing & Information Science. 2024 Nov 20;7(11):67-71..
  66. Moradzadeh A, Teimourzadeh H, Mohammadi-Ivatloo B, Pourhossein K. Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults. International Journal of Electrical Power & Energy Systems. 2022 Feb 1;135:107563.
  67. Murzakhanov I, Vishwanath GM, Vemalaiah K, Prashal G, Chatzivasileiadis S, Padhy NP. A novel decentralized inverter control algorithm for loss minimization and LVRT improvement. Electric Power Systems Research. 2023 Aug 1;221:109433.
  68. Khan SU, Mynuddin M, Islam MA, Hossain MI, Ahad DM, Tanvir MS. Deep learning based power quality disturbance recognition using residual network in smart grid. In2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) 2023 Jul 19 (pp. 1-6). IEEE.
  69. Garcia CI, Grasso F, Luchetta A, Piccirilli MC, Paolucci L, Talluri G. A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM. Applied sciences. 2020 Sep 27;10(19):6755.
Ahead of Print Subscription Review Article
Volume 04
01
Received 12/04/2026
Accepted 17/04/2026
Published 25/05/2026
Publication Time 43 Days
1

Login


My IP

PlumX Metrics