Neural Computing and Applications

, Volume 31, Issue 4, pp 1237–1248 | Cite as

Levenberg–Marquardt neural network to estimate UPFC-coordinated PSS parameters to enhance power system stability

  • Md Juel Rana
  • Mohammad Shoaib Shahriar
  • Md ShafiullahEmail author
Original Article


Due to the presence of weak tie line interconnections, small signal oscillations are created in power system networks. Damping out these oscillations is one of the most crucial issues to be settled down for the stability of power system industry. The employment of flexible AC transmission systems (FACTS) may suppress these oscillations effectively in addition to the enhancement of power transfer capability. Unified power flow controller (UPFC) is one of those FACTS devices which are installed in the powers grids, which ensures proper functionality of high-voltage transmission lines. To select the proper parameters of power system stabilizer (PSS) when applied with UPFC is a challenge in this field which can be represented as a multi-objective optimization problem. This work aims to optimize the PSS parameters of power network incorporating UPFC using the artificial neural network (ANN) in real time to damp out the small signal oscillations with a view to enhancing the stability of the power system where the Levenberg–Marquardt (LM) algorithm is used as the training algorithm. System eigenvalues obtained from ANN-tuned PSS coordinated with UPFC and the fixed gain conventional PSS with UPFC are compared to investigate the efficiency of the proposed technique for different loading conditions. Additionally, the comparison has been made in time domain simulation results which prove the superiority of the proposed technique over conventional technique. Moreover, the satisfactory values of statistical performance measures validate the efficacy of the prediction capability of the proposed LM-NN approach.


Artificial neural network (ANN) Low-frequency oscillations (LFOs) Power system stability Unified power flow controller (UPFC) 


Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control. McGraw-Hill, NY. doi: 10.1201/9781420009248
  2. 2.
    Sambariya DK, Prasad R (2013) Design of PSS for SMIB system using robust fast output sampling feedback technique. In: 7th international conference of intelligent systems control 166–171. doi: 10.1109/ISCO.2013.6481142
  3. 3.
    Larsen E, Swann D (1981) Applying power system stabilizers part I: general concepts. IEEE Trans Power Appar Syst PAS-100:3017–3024. doi: 10.1109/TPAS.1981.316355 CrossRefGoogle Scholar
  4. 4.
    Shafiullah M, Rana MJ, Coelho LS et al (2017) Designing lead–lag PSS employing backtracking search algorithm to improve power system damping. In: 9th IEEE GCC Conference and Exhibition. pp 1–6Google Scholar
  5. 5.
    Abido MA, Al-Awami AT, Abdel-Magid YL (2006) Simultaneous design of damping controllers and internal controllers of a unified power flow controller. IEEE Power Eng Soc Gen Meet. doi: 10.1109/PES.2006.1709297 Google Scholar
  6. 6.
    Wood AJ, Wollenberg BF, Sheblé GB (2013) Power generation, operation, and control, 3rd edn. Wiley, New JerseyGoogle Scholar
  7. 7.
    Eslami M, Shareef H, Mohamed A (2010) Application of PSS and FACTS devices for intensification of power system stability. Int Rev Electr Eng 5:552–570Google Scholar
  8. 8.
    Alam MS, Razzak MA, Shafiullah M, Chowdhury AH (2012) Application of TCSC and SVC in damping oscillations in Bangladesh power system. In: 7th international conference on electrical computer engineering, pp 571–574. doi: 10.1109/ICECE.2012.6471614
  9. 9.
    Alam MS, Shafiullah M, Hossain MI, Hasan MN (2015) Enhancement of power system damping employing TCSC with genetic algorithm based controller design. Int Conf Electr Eng Inf Commun Tech. doi: 10.1109/ICEEICT.2015.7307353 Google Scholar
  10. 10.
    Siddiqui AS, Khan MT, Iqbal F (2015) Determination of optimal location of TCSC and STATCOM for congestion management in deregulated power system. Int J Syst Assur Eng Manag. doi: 10.1007/s13198-014-0332-4 Google Scholar
  11. 11.
    Mukherjee A, Mukherjee V (2016) Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int J Electr Power Energy Syst 78:700–714. doi: 10.1016/j.ijepes.2015.12.001 CrossRefGoogle Scholar
  12. 12.
    Inkollu SR, Kota VR (2016) Optimal setting of FACTS devices for voltage stability improvement using PSO adaptive GSA hybrid algorithm. Eng Sci Technol an Int J. doi: 10.1016/j.jestch.2016.01.011 Google Scholar
  13. 13.
    Shafiullah M, Alam MS, Hossain MI, Hasan MN (2014) Transient performance improvement of power system by optimal design of SVC controller employing genetic algorithm. In: 8th international conference on electrical computer engineering, pp 540–543. doi: 10.1109/ICECE.2014.7026947
  14. 14.
    Khan MT, Siddiqui AS (2016) FACTS device control strategy using PMU. Perspect Sci. doi: 10.1016/j.pisc.2016.06.072 Google Scholar
  15. 15.
    Wang HF (1999) Applications of modelling UPFC into multi-machine power systems. IEE Proc Gener Transm Distrib 146:306. doi: 10.1049/ip-gtd:19990170 CrossRefGoogle Scholar
  16. 16.
    Wartana IM, Agustini NP (2011) Optimal placement of UPFC for maximizing system loadability and minimizing active power losses in system stability margins by NSGA-II. In: Proceedings of international conference on electrical engineering and informatics, pp 1–6. doi: 10.1109/ICEEI.2011.6021665
  17. 17.
    Xiaoyan Bian X, Tse CT, Chung CY, Wang KW (2009) Coordinated design of probabilistic PSS and FACTS controllers to damp oscillations. Int Conf Sustain Power Gener Supply. doi: 10.1109/SUPERGEN.2009.5348380 Google Scholar
  18. 18.
    Hassan LH, Moghavvemi M, Almurib HAF, Muttaqi KM (2014) A coordinated design of PSSs and UPFC-based stabilizer using genetic algorithm. IEEE Trans Ind Appl 50:2957–2966. doi: 10.1109/TIA.2014.2305797 CrossRefGoogle Scholar
  19. 19.
    Hassan LH, Moghavvemi M, Almurib HAF, Muttaqi KM (2013) A coordinated design of PSSs and UPFC-based stabilizer using genetic algorithm. IEEE Ind Appl Soc Annu Meet 2013:1–9. doi: 10.1109/IAS.2013.6682601 Google Scholar
  20. 20.
    Shahriar MS, Shafiullah M, Asif MA et al (2015) Design of multi-objective UPFC employing backtracking search algorithm for enhancement of power system stability. In: 18th international conference on computer infomation and technology, pp 323–328. doi: 10.1109/ICCITECHN.2015.7488090
  21. 21.
    Baskaran S, Karpagam N, Devaraj D (2012) Optimization of UPFC controllable parameters for stability enhancement with real-coded genetic algorithm. Int Conf Adv Eng Sci Manag 2012:250–255Google Scholar
  22. 22.
    Vanitila R, Sudhakaran M (2012) Differential evolution algorithm based Weighted Additive FGA approach for optimal power flow using muti-type FACTS devices. Int Conf Emerg Trends Electr Eng Energy Manag. doi: 10.1109/ICETEEEM.2012.6494459 Google Scholar
  23. 23.
    Hamid Z, Musirin I, Othman MM, Khalil MR (2010) Optimum tuning of unified power flow controller via ant colony optimization technique. In: 4th international power engineering and optimization conference, pp 170–177. doi: 10.1109/PEOCO.2010.5559166
  24. 24.
    Al-Awami AT, Abdel-Magid YL, Abido MA (2007) A particle-swarm-based approach of power system stability enhancement with unified power flow controller. Int J Electr Power Energy Syst 29:251–259. doi: 10.1016/j.ijepes.2006.07.006 CrossRefGoogle Scholar
  25. 25.
    Masiur Rahman S, Khondaker AN, Imtiaz Hossain M et al (2017) Neurogenetic modeling of energy demand in the United Arab Emirates, Saudi Arabia, and Qatar. Environ Prog Sustain Energy. doi: 10.1002/ep.12558 Google Scholar
  26. 26.
    Kumar J, Kumar PP, Mahesh A, Shrivastava A (2011) Power system stabilizer based on artificial neural network. In: IEEE international conference on power and energy systems, pp 1–6Google Scholar
  27. 27.
    Ijaz M, Shafiullah M, Abido MA (2015) Classification of power quality disturbances using Wavelet Transform and Optimized ANN. In: Proceedings of 18th international conference intell system applications to power systems (ISAP), pp 1–6. doi: 10.1109/ISAP.2015.7325522
  28. 28.
    Yang L, Hao Y, Liu Q, Zhu X (2015) Ship traffic volume forecast in bridge area based on enhanced hybrid radial basis function neural networks. In: IEEE international conference transportation information safety, pp 38–43Google Scholar
  29. 29.
    Mishra S, Prusty R, Hota PK (2015) Analysis of Levenberg–Marquardt and Scaled Conjugate gradient training algorithms for artificial neural network based LS and MMSE estimated channel equalizers. Int Conf Man Mach Interfacing 2015:1–7. doi: 10.1109/MAMI.2015.7456617 Google Scholar
  30. 30.
    Shafiullah M, Ijaz M, Abido MA, Al-Hamouz Z (2017) Optimized support vector machine & wavelet transform for distribution grid fault location. In: 11th IEEE international conference on compatibility, power electronics and power engineering, pp 77–82. doi: 10.1109/CPE.2017.7915148
  31. 31.
    Kim MK (2015) Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms. IET Gener Transm Distrib 9:1553–1563. doi: 10.1049/iet-gtd.2014.0957 CrossRefGoogle Scholar
  32. 32.
    Shayeghi H, Shayanfar H, Jalilzadeh S (2009) Simultaneous coordinated designing of UPFC and PSS output feedback controllers using PSO. J Electr Eng 60:177–184Google Scholar
  33. 33.
    Shahriar MS, Ahmed MA, Ullah MS (2012) Design and analysis of a model predictive unified power flow controller (MPUPFC) for power system stability assessment. Int J Electr Comput Sci 2:32–37Google Scholar
  34. 34.
    Shahriar MS, Ahmed MA, Ullah MS (2012) Model predictive unified power flow controller (MPUPFC): performance analysis of an MPUPFC for power system stability assessment. LAP LAMBERT Academic Publishing, Saarbrücken, GermanyGoogle Scholar
  35. 35.
    Shayeghi H, Shayanfar H, Jalilzadeh S (2009) Simultaneous coordinated designing of UPFC and PSS output feedback controllers using PSO. J Electr Eng 60:177–184Google Scholar
  36. 36.
    Ajami A, Armaghan M (2010) Application of multi-objective PSO algorithm for power system stability enhancement by means of SSSC. Int J Comput Electr Eng 2:838–845CrossRefGoogle Scholar
  37. 37.
    Abdel-Magid YL, Abido MA (2003) Optimal multiobjective design of robust power system stabilizers using genetic algorithms. IEEE Trans Power Syst 18:1125–1132. doi: 10.1109/TPWRS.2003.814848 CrossRefGoogle Scholar
  38. 38.
    Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144. doi: 10.1016/j.amc.2013.02.017 MathSciNetzbMATHGoogle Scholar
  39. 39.
    Shafiullah M, Abido MA, Coelho LS (2015) Design of robust PSS in multimachine power systems using backtracking search algorithm. In: Proceedings 18th international conference intell system applications to power systems (ISAP), pp 1–6. doi: 10.1109/ISAP.2015.7325528
  40. 40.
    Shafiullah M, Rana MJ, Alam MS, Uddin MA (2016) Optimal placement of Phasor measurement units for transmission grid observability. Int Conf Innov Sci Eng Technol 2016:1–4. doi: 10.1109/ICISET.2016.7856492 Google Scholar
  41. 41.
    Wilamowski BM, Hao Y (2010) Improved computation for Levenberg–Marquardt training. IEEE Trans Neural Netw 21:930–937. doi: 10.1109/TNN.2010.2045657 CrossRefGoogle Scholar
  42. 42.
    Wilamowski BM, Irwin JD (2011) The industrial electronics handbook Intelligent systems. CRC Press, Boca Raton, Florida, United StatesGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Md Juel Rana
    • 1
  • Mohammad Shoaib Shahriar
    • 1
  • Md Shafiullah
    • 1
    Email author
  1. 1.Electrical Engineering DepartmentKing Fahd University of Petroleum & MineralsDhahranSaudi Arabia

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