Abstract
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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control. McGraw-Hill, NY. doi:10.1201/9781420009248
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
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
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 1–6
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
Wood AJ, Wollenberg BF, Sheblé GB (2013) Power generation, operation, and control, 3rd edn. Wiley, New Jersey
Eslami M, Shareef H, Mohamed A (2010) Application of PSS and FACTS devices for intensification of power system stability. Int Rev Electr Eng 5:552–570
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
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
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
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
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
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
Khan MT, Siddiqui AS (2016) FACTS device control strategy using PMU. Perspect Sci. doi:10.1016/j.pisc.2016.06.072
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
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
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
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
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
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
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 2012:250–255
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
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
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
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
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 1–6
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
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 38–43
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
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
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
Shayeghi H, Shayanfar H, Jalilzadeh S (2009) Simultaneous coordinated designing of UPFC and PSS output feedback controllers using PSO. J Electr Eng 60:177–184
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 2:32–37
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, Germany
Shayeghi H, Shayanfar H, Jalilzadeh S (2009) Simultaneous coordinated designing of UPFC and PSS output feedback controllers using PSO. J Electr Eng 60:177–184
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 2:838–845
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
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
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
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
Wilamowski BM, Hao Y (2010) Improved computation for Levenberg–Marquardt training. IEEE Trans Neural Netw 21:930–937. doi:10.1109/TNN.2010.2045657
Wilamowski BM, Irwin JD (2011) The industrial electronics handbook Intelligent systems. CRC Press, Boca Raton, Florida, United States
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Appendix
Appendix
System parameters used are given below:
-
Transmission line and generator:
-
M = 8 MJ/MVA, D = 0, xd = 1.0 pu, xq = 0.6 pu, T′d0 = 5.044 s, x′d = 0.3 pu, ωb = 377 rad/s and XL = 0.1 pu
-
Machine excitation system: KA = 100, TA = 0.01 s
-
Transformer: XET = 0.1 pu, XBT = 0.1 pu, XT = 0.1 pu
-
DC link capacitor:
-
VDC = 2 pu and CDC = 1.2 pu
-
PSS parameters (fixed gain):
-
PSS: K = 15.71, T1 = 0.3, T2 = 0.3, T3 = 0.39, T4 = 0.6623
-
UPFC: δE = 68.113°, δB = 41.12°, mB = 0.96, mE = 0.7667
-
PSS parameters (LM-NN):
-
1 ≤ K ≤ 50, 0.01 ≤ T1 ≤ 1.0
Performance of the proposed technique was tested with different well-known error measures including MSE, RMSE, MAPE and R2.
For total n data samples, actual values ya and predicted values yp mathematical formulas for the error measures are presented below:
where \( \bar{y}_{\text{a}} \) is the mean of actual value.
Rights and permissions
About this article
Cite this article
Rana, M.J., Shahriar, M.S. & Shafiullah, M. Levenberg–Marquardt neural network to estimate UPFC-coordinated PSS parameters to enhance power system stability. Neural Comput & Applic 31, 1237–1248 (2019). https://doi.org/10.1007/s00521-017-3156-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-017-3156-8