Vol. 6 No. 2 (2025)

Standard Journal Issues

Auto-Tuned Particle Swarm with perturbed velocities - Optimized CLMF Algorithm for DVR based Power Quality Improvement

Published 2025-10-03

  • Tummala Kranti Kiran
    • ,
  • Balakrishnan Rajagopal
    • ,
  • Yerramilli Butchi Raju

Tummala Kranti Kiran
Department of Electrical Engineering at Annamalai University, Annamalai nagar, Tamil Nadu, India

Balakrishnan Rajagopal
Department of Electrical Engineering at Annamalai University, Annamalai nagar, Tamil Nadu, India

Yerramilli Butchi Raju
Department of Electrical Engineering at Sir C R Reddy College of Engineering, Eluru, Andhra Pradesh, India

Abstract

Problems pertaining to power quality, like voltage sags and swells as well as harmonics, have the potential to impact the reliability of contemporary power systems. An innovative approach that integrates the Composite Least Mean Fourth (CLMF) algorithm with meta-heuristic algorithms like PSO, DE, PSODE and a hybridization Auto-Tuned PSO and DE (APSODE)—to enhance the performance of a Dynamic Voltage Restorer (DVR) is presented in this study. The CLMF algorithm is used mostly to mitigate voltage distortions, while the meta-heuristic algorithms are employed primarily to optimize the DVR's control parameters for improved operation over different power quality conditions. Extensive simulations, conducted in the MATLAB environment, validate the proposed method, showing a substantial improvement in voltage restoration, reduced Total Harmonic Distortion (THD), and faster dynamic response. The findings of the study highlight the effectiveness of combining CLMF with PSO, DE, PSODE and APSODE in addressing power quality issues, offering improved performance and higher adaptability to changing conditions of power system. The proposed APSODE algorithm introduces auto-tuned acceleration coefficients and hybrid velocity mutation mechanisms, offering real-time adaptability and improved convergence over traditional methods, thereby ensuring strong voltage compensation under dynamic power quality conditions.

Keywords

Dynamic Voltage Restorer (DVR), Composite Least Mean Fourth (CLMF) Algorithm, Particle Swarm Optimization and Differential Evolution (PSODE), , Auto-tuned PSO and Differential Evolution (APSODE), Power Quality Enhancement, Total Harmonic Distortion (THD), Voltage Sag/Swell Compensation

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References

  1. F. G. Marroquín, L. A. Barragán, and M. E. Ruiz, "An improved control strategy for DVRs using model predictive control to mitigate power quality issues," IEEE Transactions on Power Delivery, vol. 36, no. 2, pp. 584-593, 2021.
  2. L. R. D. N. R. R. Prasanna and K. V. S. R. Murthy, "Design and control of dynamic voltage restorer for power quality improvement," Journal of Electrical Engineering Technology, vol. 15, no. 3, pp. 732-740, 2020.
  3. R. Singh, S. S. Ghosh, and S. L. P. R. Reddy, "Real-time control of dynamic voltage restorers for enhanced power quality in smart grids," Energy Reports, vol. 8, pp. 2159-2171, 2022.
  4. S. L. Joshi, H. C. Sharma, and A. B. Kiran, "A hybrid control approach for DVRs in mitigating voltage sags under dynamic load conditions," International Journal of Electrical Power and Energy Systems, vol. 134, no. 7, pp. 107228-107235, 2023.
  5. L. C. Lim, F. S. Khaled, and V. S. M. Raj, "Advanced algorithms for dynamic voltage restorer under nonlinear loads in industrial applications," IEEE Transactions on Industry Applications, vol. 61, no. 4, pp. 1234-1243, 2025.
  6. M. R. Patel, S. K. S. Reddy, and N. V. R. Reddy, "Comparison of conventional control strategies for DVRs: SRF theory and IRP theory," IEEE Transactions on Power Delivery, vol. 36, no. 4, pp. 2011-2019, 2021.
  7. H. C. Sharma, V. P. Jagannath, and R. B. Reddy, "Adaptive filtering techniques for voltage compensation in DVRs: A review of LMS, LMF, and CLMF algorithms," IEEE Transactions on Power Electronics, vol. 38, no. 8, pp. 4432-4444, 2023.
  8. S. M. N. R. G. Prasad, N. S. Kumar, and B. P. Singh, "Improved voltage sag mitigation using composite least mean fourth (CLMF) algorithm for dynamic voltage restorer," IEEE Transactions on Industrial Electronics, vol. 69, no. 3, pp. 2578-2588, 2022.
  9. K. P. Vishwakarma, A. K. Reddy, and S. V. R. Prasad, "Application of LMS and LMF algorithms for real-time voltage correction in DVR systems," IEEE Transactions on Power Delivery, vol. 35, no. 5, pp. 1463-1471, 2021.
  10. R. K. Gupta, A. S. Kumar, and P. B. Jain, "Enhancing DVR performance using Convex Least Mean Fourth algorithm for voltage sag and swell compensation," IEEE Transactions on Power Electronics, vol. 40, no. 2, pp. 813-823, 2025.
  11. J. B. Silva, P. R. A. Moreira, and M. L. Silva, "Evolution of artificial intelligence methods in power quality monitoring: A review and future trends," IEEE Transactions on Power Delivery, vol. 37, no. 6, pp. 2218-2230, 2024.
  12. A. G. Shukla, R. R. Sinha, and B. V. K. Rao, "Artificial intelligence techniques in dynamic voltage restorers: A comparative study of neural networks, fuzzy logic, and genetic algorithms," IEEE Transactions on Power Electronics, vol. 39, no. 5, pp. 2287-2295, 2022.
  13. S. K. Singhal, R. P. S. Chouhan, and P. S. R. Reddy, "Artificial intelligence for optimization of voltage compensation in DVRs: Integration of deep learning and PSO techniques," IEEE Transactions on Industrial Electronics, vol. 69, no. 7, pp. 7258-7267, 2023.
  14. N. K. Kumar, R. K. Ghosh, and M. S. R. G. Sahu, "AI-based optimization techniques for voltage sag mitigation in DVR systems: Application of machine learning and reinforcement learning," IEEE Transactions on. Smart Grid, vol. 15, no. 1, pp. 156-165, 2024.
  15. D. R. A. R. Hegde, S. S. R. Joshi, and L. G. Patel, "Artificial intelligence applications in dynamic voltage restorer systems: Current trends and future perspectives," IEEE Transactions on. Neural Networks and Learning Systems, vol. 34, no. 2, pp. 578-587, 2023.
  16. R. S. A. Kumar, A. K. Jain, and N. V. R. Reddy, "Optimization of dynamic voltage restorer performance using particle swarm optimization for improved power quality," IEEE Transactions on Power Delivery, vol. 37, no. 2, pp. 812-821, 2022.
  17. M. M. Manisha, A. K. Ghosh, and S. S. Reddy, "Optimal DVR control using PSO for minimizing voltage sag and swell in distribution networks," IEEE Transactions on Power Electronics, vol. 39, no. 6, pp. 2978-2987, 2024.
  18. S. L. Joshi, A. B. Kiran, and S. M. Ghosh, "Application of PSO-based optimization in dynamic voltage restorer to enhance voltage stability," IEEE Transactions on Industry Applications, vol. 58, no. 3, pp. 2340-2348, 2022.
  19. V. P. Jagannath, P. K. Jain, and M. N. Kumar, "Optimization of DVR with PSO algorithm for voltage sag compensation under transient conditions," IEEE Transactions on Power Systems, vol. 38, no. 4, pp. 1420-1428, 2023.
  20. S. M. N. R. G. Prasad, B. K. Ghosh, and R. S. Agarwal, "Particle swarm optimization-based approach for optimal placement and control of DVRs in smart grid systems," IEEE Transactions on Smart Grid, vol. 14, no. 1, pp. 320-329, 2023.
  21. L. Ravi Srinivas, B. Mahesh Babu and S.S. Tulasi Ram, “DVR-based power quality enhancement using adaptive particle swarm optimisation technique,” International Journal of Bio-Inspired Computation (IJBIC),vol. 18, no. 2, 2021.
  22. R. Storn and K. Price, “Differential Evolution - A simple and special heuristic for global optimization over continuous spaces,” Journal of Global Optimization, pp. 341-359, 1997.
  23. M. Basu, “Optimal power flow with FACTS devices using differential evolution,” International Journal of Electrical Power and Energy Systems, pp. 150-156,2008.
  24. S. Das, A. Konar and U.K. Chakraborty, “Particle Swarm Optimization with a Differentially Perturbed Velocity,” ACM-SIGEVO Proceedings of GECCO’ 05, pp. 991-998,2005.
  25. Swagatam Das, Ajith Abraham and Amit Konar, “Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives,” Advances of Computational Intelligence in Industrial Systems, Springer, pp. 1-38, 2008.
  26. C.Thitithamrongchai and B. Eua-arporn, “Self-adapative Differential Evolution Based Optimal Power Flow For Units with Non-smooth Fuel Cost Functions,” Journal of Electrical Systems, 2007, pp. 88-99.
  27. JiangChuanwen and EtorreBompard, “A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment,” Energy Conversion and Management, pp. 2689-2696, 2005.
  28. IEEE Xplore, “IEEE Standard for Harmonic Control” in IEEE Std.519-2022, pp.1-31, 5th August, 2022.
  29. A. abobakir and A. Abdulazeez, “A Review on Utilizing Machine Learning Classification Algorithms for Skin Cancer”, Journal of Applied Science and Technology Trends, vol. 5, no. 2, pp. 60–71, Aug. 2024.
  30. S. P. Singh, K. Upreti, R. Jain, Rakesh Kumar Arora, A. Tiwari, and G. V. Radhakrishnan, “Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques”, Journal of Applied Science and Technology Trends, vol. 6, no. 1, pp. 74–86, Jun. 2024.

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