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Centralized Fuzzy Logic Based Optimization of PI Controllers for VSC Control in MTDC Network

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Abstract

Advancements in the field of power electronics led to global changes in the electrical energy generation, transmission, and distribution. The voltage source converter (VSC) based HVDC system is the future of the power system due to the advantages it offers in terms of renewable power generation, transmission, and integration. Currently, the two-terminal VSC–HVDC systems have been successfully commissioned. Multi-terminal VSC–HVDC system is the developing trend for higher power reliability, large scale integration, and smart operation. The performance of a VSC based multi-terminal direct current (MTDC) system greatly depends upon the tuning of the controller. Standard practice is to tune the PI controller using hit and trial method or based on the operator’s experience. However, the tuning process becomes complex when multiple grids are involved. Thus, for MTDC systems, the manual tuning of the controller does not yield the desired results. This paper presents a centralized fuzzy logic-based optimization technique for VSC control of the MTDC system to obtain the optimized parameters for the PI controllers. The optimized parameters ensure a better system performance in terms of fast settling time, better slew rate, minimum undershoot, and minimum overshoot response. The proposed technique is tested on a three-terminal MTDC network in SIMULINK / MATLAB.

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References

  1. May TW, Yeap YM, Ukil A Comparative evaluation of power loss in HVAC and HVDC transmission systems. In: 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016, pp 637–641. doi: 10.1109/TENCON.2016.7848080

  2. Povh D (2000) Use of HVDC and FACTS. Proc IEEE 88(2):235–245. https://doi.org/10.1109/5.824001

    Article  Google Scholar 

  3. Chan-Ki Kim, Vijay K. Sood, Gil-Soo Jang, Seong-Joo Lim, Seok-Jin Lee (2009) HVDC Transmission: power conversion applications in power systems, Singapore.

  4. Eeckhout B, Van Hertem, Dirk & Reza M, Srivastava, Kailash, Belmans, Ronnie (2009) Economic comparison of VSC HVDC and HVAC as a transmission system for a 300  MW offshore wind farm. Euro Trans Electr Power 20:661–671. 10.1002/etep.359

  5. Rodriguez P, Rouzbehi K (2017) Multi-terminal DC grids: challenges and prospects. Jf Modern Power Syst Clean Energy 5(4):515–523. https://doi.org/10.1007/s40565-017-0305-0

    Article  Google Scholar 

  6. Li G et al (2019) Feasibility and reliability analysis of LCC DC Grids and LCC/VSC Hybrid DC Grids. IEEE Access 7:22445–22456. https://doi.org/10.1109/ACCESS.2019.2898387

    Article  Google Scholar 

  7. Livermore L, Liang J, Ekanayake J (2010) MTDC VSC Technology and its applications for wind power. In: 45th international universities power engineering conference UPEC2010, Cardiff, Wales, pp 1-6

  8. Aidan O'Dwyer (eds) (2009), Handbook of PI and PID controller tuning rules, London.

  9. Bibaya L, Liu C, Li G Optimal control tuning of VSC-MTDC using a multi-objective hybrid pso algorithm. In: 2018 2nd IEEE conference on energy internet and energy system integration (EI2), Beijing, 2018, pp 1–6. doi: 10.1109/EI2.2018.8582390

  10. Fuzzy Logic in Process Control (2005) In: Computational Intelligence. Springer, Berlin, Heidelberg

    Google Scholar 

  11. Araki M (2020) PID control in control systems, robotics and automation, vol II, edited by Heinz Unbehauen, Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, EolssPublishers, OxfordUK. (10) (PDF) PID Controller Tuning Techniques: A Review.Ahttps://www.researchgate.net/publication/316990192_PID_Controller_Tuning_Techniques_A_Review. Accessed Mar 07 2020.

  12. Ziegler JG, Nichols NB (1942) Optimum settings for automatic controller. Trans ASME 64:759–768

    Google Scholar 

  13. Cohen GH, Coon GA (1953) Theoretical considerations of retarded control. Trans ASME 75:827–834

    Google Scholar 

  14. Dong Hwa Kim (2005) Tuning of PID controller using gain/phase margin and immune algorithm. In: IEEE mid-summer workshop on soft computing in industrial applications, Helsinki University of Technology, Espoo, Finland, pp 6–74

  15. Dorigo M, Dicaro G The Ant Colony Optimization Meta-heuristic. In: New ideas in optimization, McGraw Hill, London, pp 11–32, 1999

  16. Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artificial Life 5(2):137–172

    Article  Google Scholar 

  17. Salami M, Cain G (1995) An adaptive PID controller based on genetic algorithm processor. In: IEE genetic algorithms in engineering systems, innovations and applications conference, publication No. 414, 12–14 September, 1995.

  18. Kumar SMG, Jain R, Anantharaman N (2008) Genetic algorithm based PID controller tuning for a model bioreactor. Indian ChemEng 50(3):214–226

    Google Scholar 

  19. Gao F, Tong H (2006) Differential evolution, an efficient method in optimal PID tuning and online tuning. In: Proc. of the international conference on complex systems and applications, pp 785–789

  20. Cao YJ (1997) Eigenvalue optimisation problems via evolutionary programming. Electron Lett 33(7):642–643

    Article  Google Scholar 

  21. DOLEŽEL, Petr, MAREŠ, Jan, “Self-tuning PID Control using Genetic Algorithm and Artificial Neural Networks,” ASR 2009 Instruments and Control, pp. 33- 39, 2009.

  22. Mohammed El-Said El-Telbany, “Employing particle swarm optimizer and genetic algorithms for optimal tuning of PID controllers, a comparative study,” ICGST-ACSE Journal, vol. 7.

  23. Kao C-C, Chuang C-W, Fung R-F (2007) The self-tuning PID control in a slider–crank mechanism system by applying Particle Swarm Optimization Approach. Mechatronics 16(8): 513–522. no. 2, pp. 49–54, 2007.

  24. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculation using fast computing machines. J ChemPhys 21:1087–1092

    MATH  Google Scholar 

  25. Takao K, Toru Y, Takao H (2006) A design of PID controllers with a switching structure by a support vector machine. In: International joint conference on neural networks, pp 4684–4689.

  26. Nakano E, Jutan A (1994) Application of response surface methodology in controller fine-tuning. ISA Trans 4:353–366

    Article  Google Scholar 

  27. Zulfatman RMF (2009) Application of self-tuning Fuzzy PID controller on industrial hydraulic actuator using system identification approach. Int J Smart SensIntellSyst 2(2):246–261

    Article  Google Scholar 

  28. Manuel Pérez-Donsión, Gianpaolo Vitale. Advances in Renewable Energies and Power Quality, Chapter thirty-one. https://books.google.com.pk/books?id=ulfSDwAAQBAJ&pg=PA615&lpg=PA615&dq=Importance+of+fuzzy+logic+in+MTDC&source=bl&ots=vAjg-FxGlK&sig=ACfU3U0jB-cv4K1XTnqKRnRMjBdl1gNMMQ&hl=en&sa=X&ved=2ahUKEwjJmtXp8LDqAhUEfBoKHWb1ABUQ6AEwCnoECAoQAQ#v=onepage&q=Importance%2520of%2520fuzzy%2520logic%2520in%2520MTDC&f=false.

  29. McNamara P, Milano F (2018) Model predictive control-based AGC for multi-terminal HVDC-connected AC grids. IEEE Trans Power Syst 33(1):1036–1048. https://doi.org/10.1109/TPWRS.2017.2694768Retrieved on: 2 July 2020

    Article  Google Scholar 

  30. Faisal SF, Beig AR, Thomas S (2020) Time domain particle swarm optimization of PI controllers for bidirectional VSC HVDC light system. Energies 13:866

    Article  Google Scholar 

  31. Starczewski J.T. (2013) Defuzzification of Uncertain Fuzzy Sets. In: Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Studies in Fuzziness and Soft Computing, vol 284. Springer, Berlin, Heidelberg.

  32. Fuzzy Neural Networks for Real-Time Control Applications, Butterworth–Heinemann, 2016, Pages 13–24, ISBN9780128026878, https://doi.org/10.1016/B978-0-12-802687-8.00002-5.

  33. Muravyova EA, Sharipov MI, Bondarev AV Fuzzification concept using the any-time algorithm on the basis of precise term sets. In: 2017 international conference on industrial engineering, applications and manufacturing (ICIEAM), St. Petersburg, 2017, pp 1–4. doi: 10.1109/ICIEAM.2017.8076174.

  34. Topaloğlu F, Pehlıvan H Analysis of the effects of different fuzzy membership functions for wind power plant installation parameters. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, 2018, pp. 1-6. doi: 10.1109/ISDFS.2018.8355383

  35. Rahul Kala, 10 - Fuzzy-Based Planning, Editor(s): Rahul Kala, On-Road Intelligent Vehicles, Butterworth-Heinemann, 2016, Pages 279–317, ISBN:9780128037294, https://doi.org/10.1016/B978-0-12-803729-4.00010-6.

  36. R.S. Jaiswal. M.V. Sarode. An Overview of Fuzzy Logic & Fuzzy Elements. International Research Journal of Computer Science (IRJCS) ISSN: 2393–9842 Issue 2, Volume 3 (March 2015).

  37. Ross T (2000) Membership functions. FuzzificationDefuzzification. https://doi.org/10.1007/978-3-7908-1859-8_3

    Article  Google Scholar 

  38. Yang M, Xie D, Zhu H, Lou Y Architectures and control for multi-terminal DC (MTDC) distribution network-a review. In: 11th IET international conference on AC and DC power transmission, Birmingham, 2015, pp 1–7.doi: 10.1049/cp.2015.0100.

  39. Wang H, Ma K IGBT technology for future high-power VSC–HVDC applications. In: 12th IET international conference on AC and DC power transmission (ACDC 2016), Beijing, 2016, pp 1–6.doi: 10.1049/cp.2016.0485.

  40. Latorre H, Ghandhari M, Soder L (2008) Active and reactive power control of a VSC-HVDC. Electr Power Syst Res 78:1756–1763. https://doi.org/10.1016/j.epsr.2008.03.003

    Article  Google Scholar 

  41. Mathworks, VSC based HVDC transmission.https://www.mathworks.com/help/physmod/sps/examples/vsc-based-hvdc-transmission-system-detailed-model.html. Accessed on:20 Dec 2019.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, North China Electric Power University, Beijing, China

    Shahid Aziz Khan, Chongru Liu & Jamshed Ahmed Ansari

  2. Sukkur IBA University, Sukkur, Pakistan

    Jamshed Ahmed Ansari

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  1. Shahid Aziz Khan
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  2. Chongru Liu
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Khan, S.A., Liu, C. & Ansari, J.A. Centralized Fuzzy Logic Based Optimization of PI Controllers for VSC Control in MTDC Network. J. Electr. Eng. Technol. 15, 2577–2585 (2020). https://doi.org/10.1007/s42835-020-00556-w

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