Power system oscillation damping controller design: a novel approach of integrated HHO-PSO algorithm

Journal title

Archives of Control Sciences


Devarapalli, Ramesh : Department of Electrical Engineering, B.I.T. Sindri, Dhanbad, Jharkhand, India ; Kumar, Vikash : Department of Electrical Engineering, B.I.T. Sindri, Dhanbad, Jharkhand, India



Harris hawk optimization ; Power system stabilizers ; STATCOM ; FACTS ; particle swarm optimization

Divisions of PAS

Nauki Techniczne




Committee of Automatic Control and Robotics PAS


[1] M. Crepinsek, S.-H. Liu, and L. Mernik: A note on teaching–learningbased optimization algorithm. Information Sciences, 212 (2012), 79–93, DOI: 10.1016/j.ins.2012.05.009.
[2] Anita and A. Yadav: AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48 (2019), 93–108, DOI: 10.1016/j.swevo.2019.03.013.
[3] R. Devarapalli and B. Bhattacharyya: A hybrid modified grey wolf optimization-sine cosine algorithm-based power system stabilizer parameter tuning in a multimachine power system. Optimal Control Applications and Methods, 41(4), (2020), 1143-1159, DOI: 10.1002/oca.2591.
[4] M. Jain, V. Singh, and A. Rani: A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarmand Evolutionary Computation, 44 (2019), 148–175, DOI: 10.1016/j.swevo.2018.02.013.
[5] A.E. Eiben and J.E. Smith: What is an Evolutionary Algorithm? In Introduction to Evolutionary Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, 25–48, DOI: 10.1007/978-3-662-44874-8_3.
[6] A. Kaveh and M. Khayatazad: A new meta-heuristic method: Ray Optimization. Computers & Structures, 112–113, (2012), 283–294, DOI: 10.1016/j.compstruc.2012.09.003.
[7] P.J.M. van Laarhoven and E.H.L. Aarts: Simulated annealing. In Simulated Annealing: Theory and Applications, P.J.M. van Laarhoven and E.H.L. Aarts, Eds. Dordrecht: Springer Netherlands, 1987, 7–15, DOI: 10.1007/978-94-015-7744-1_2.
[8] Agenetic algorithm tutorial. SpringerLink. (accessed Mar. 20, 2020).
[9] J. Kennedy and R. Eberhart: Particle Swarm Optimization. Proc. of ICNN’95 International Conference on Neural Networks, 4 (1995), 1942– 1948.
[10] M. Neshat, G. Sepidnam, M. Sargolzaei, and A.N. Toosi: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), (2014), 965–997, DOI: 10.1007/s10462-012-9342-2.
[11] M. Dorigo, M. Birattari, and T. Stutzle: Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), (2006), 28–39, DOI: 10.1109/ MCI.2006.329691.
[12] M. Roth and S. Wicker: Termite: ad-hoc networking with stigmergy. In GLOBECOM’03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 5 (2003), 2937–2941, DOI: 10.1109/GLOCOM.2003.1258772.
[13] D. Karaboga and B. Akay: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), (2009), 108– 132, DOI: 10.1016/j.amc.2009.03.090.
[14] A. Mucherino and O. Seref: Monkey search: a novel metaheuristic search for global optimization. AIP Conference Proceedings, 953(1), (2007), 162– 173, DOI: 10.1063/1.2817338.
[15] E.Atashpaz-Gargari and C. Lucas: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation, (2007), 4661–4667, DOI: 10.1109/CEC.2007.4425083.
[16] D. Simon: Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), (2008), 702–713, DOI: 10.1109/TEVC.2008.919004.
[17] X.-S. Yang: Firefly algorithm. Stochastic, test, functions and design optimisation. arXiv:1003.1409 [math], Mar. 2010, Accessed: Mar. 20, 2020. [Online]. Available:
[18] K.M.Gates and P.C.M. Molenaar: Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), (2012), 310–319, DOI: 10.1016/j.neuroimage.2012.06.026.
[19] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi: GSA: A gravitational search algorithm. Information Sciences, 179(13), (2009), 2232–2248, DOI: 10.1016/j.ins.2009.03.004.
[20] Y. Tan andY. Zhu: Fireworks Algorithm for Optimization. In: TanY., ShiY., Tan K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, 6145, Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13495-1_44.
[21] X.-S. Yang: Bat algorithm for multi-objective optimisation. arXiv: 1203. 6571 [math], Mar. 2012, Accessed: Mar. 20, 2020. [Online]. Available:
[22] LingWang, Xiao-long Zheng, and Sheng-yaoWang:Anovel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 48 17–23, (2013), DOI: 10.1016/j.knosys.2013.04.003.
[23] X.-S. Yang: Flower Pollination Algorithm for Global Optimization. In Unconventional Computation and Natural Computation, Berlin, Heidelberg, 2012, 240–249, DOI: 10.1007/978-3-642-32894-7_27.
[24] G.-G. Wang, L. Guo, A.H. Gandomi, G.-S. Hao, and H. Wang: Chaotic Krill Herd algorithm. Information Sciences, 274 (2014), 17–34, DOI: 10.1016/j.ins.2014.02.123.
[25] A. Kaveh and N. Farhoudi: A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59 (2013), 53–70, DOI: 10.1016/ j.advengsoft.2013.03.004.
[26] S. Mirjalili, S.M. Mirjalili, and A. Lewis: GreyWolf optimizer. Advances in Engineering Software, 69 (2014), 46–61, DOI: 10.1016/j.advengsoft.2013.12.007.
[27] A. Hatamlou: Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222 (2013), 175–184, DOI: 10.1016/ j.ins.2012.08.023.
[28] A. Sadollah, A. Bahreininejad, H. Eskandar and M. Hamdi: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problem. Applied Soft Computing, 13(5), (2013), 2592–2612, DOI: 10.1016/j.asoc.2012.11.026.
[29] S. Mirjalili: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), (2016), 1053–1073, DOI: 10.1007/s00521-015-1920-1.
[30] S. Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89 (2015), 228–249, DOI: 10.1016/j.knosys.2015.07.006.
[31] F.A. Hashim, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, and S. Mirjalili: Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101 (2019), 646–667, DOI: 10.1016/j.future.2019.07.015.
[32] S. Mirjalili: The ant lion optimizer. Advances in Engineering Software, 83 (2015), 80–98, DOI: 10.1016/j.advengsoft.2015.01.010.
[33] H. Shareef, A.A. Ibrahim, and A.H. Mutlag: Lightning search algorithm. Applied Soft Computing, 36 (2015), 315–333, DOI: 10.1016/j.asoc.2015.07.028.
[34] S.A. Uymaz, G. Tezel, and E. Yel: Artificial algae algorithm (AAA) for nonlinear global optimization. Applied Soft Computing, 31 (2015), 153–171, DOI: 10.1016/j.asoc.2015.03.003.
[35] M.D. Li, H. Zhao, X.W. Weng, and T. Han: A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92 (2016), 65–88, DOI: 10.1016/j.advengsoft.2015.11.004.
[36] O. Abedinia, N. Amjady, and A. Ghasemi: A new metaheuristic algorithm based on shark smell optimization. Complexity, 21(5), (2016), 97–116, DOI: 10.1002/cplx.21634.
[37] S. Mirjalili, S.M. Mirjalili, and A. Hatamlou: Multi-Verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), (2016), 495–513, DOI: 10.1007/s00521-015-1870-7.
[38] S. Mirjalili and A. Lewis: The whale optimization algorithm. Advances in Engineering Software, 95 (2016), 51–67, DOI: 10.1016/j.advengsoft. 2016.01.008.
[39] A. Askarzadeh: A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169 (2016), 1–12, DOI: 10.1016/j.compstruc.2016.03.001.
[40] T. Wu, M. Yao, and J. Yang: Dolphin swarm algorithm. Frontiers of Information Technology & Electronic Engineering, 17(8), (2016), 717–729, DOI: 10.1631/FITEE.1500287.
[41] S. Mirjalili: SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96 (2016), 120–133, DOI: 10.1016/j.knosys.2015.12.022.
[42] A. Kaveh and A. Dadras: A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, (2017), 69–84, DOI: 10.1016/j.advengsoft.2017.03.014.
[43] M.M. Mafarja, I. Aljarah, A. Asghar Heidari, A.I. Hammouri, H. Faris, Ala’M. Al-Zoubi, and S. Mirjalili: Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems, 145 (2018), 25–45, DOI: 10.1016/j.knosys.2017.12.037.
[44] A. Tabari and A. Ahmad: A new optimization method: Electro-search algorithm. Computers and Chemical Engineering, 103 (2017), 1–11, DOI: 10.1016/j.compchemeng.2017.01.046.
[45] G. Dhiman and V. Kumar: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114 (2017), 48–70, DOI: 10.1016/j.advengsoft. 2017.05.014.
[46] S.-A. Ahmadi: Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Comput and Applications, 28(S1), (2017), 233–244, DOI: 10.1007/s00521-016-2334-4.
[47] A.F. Nematollahi, A. Rahiminejad, and B. Vahidi: A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Applied Soft Computing, 59 (2017), 596–621, DOI: 10.1016/j.asoc.2017.06.033.
[48] R.A. Ibrahim, A.A. Ewees, D. Oliva, M. Abd Elaziz, and S. Lu: Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 10(8), (2019), 3155–3169, DOI: 10.1007/s12652-018-1031-9.
[49] E. Jahani and M. Chizari: Tackling global optimization problems with a novel algorithm – Mouth brooding fish algorithm. Applied Soft Computing, 62 (2018), 987–1002, DOI: 10.1016/j.asoc.2017.09.035.
[50] X. Qi, Y. Zhu, and H. Zhang: A new meta-heuristic butterfly-inspired algorithm. Journal of Computational Science, 23 (2017), 226–239, DOI: 10.1016/j.jocs.2017.06.003.
[51] S. Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89 (2015), 228–249, DOI: 10.1016/j.knosys.2015.07.006.
[52] M. Dorigo, V. Maniezzo, and A. Colorni: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), (1996), 29–41, DOI: 10.1109/3477.484436.
[53] S. Mirjalili and S.Z.M. Hashim: A new hybrid PSOGSA algorithm for function optimization. In 2010 International Conference on Computer and Information Application, (2010), 374–377, DOI: 10.1109/ICCIA.2010.6141614.
[54] F.A. Senel, F. Gokce, A.S. Yuksel, and T. Yigit: A novel hybrid PSO– GWO algorithm for optimization problems. Engineering with Computers, 35(4), 1359–1373, DOI: 10.1007/s00366-018-0668-5.
[55] D.T. Bui, H. Moayedi, B. Kalantar, and A. Osouli: Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility. Sensors, 19(14), (2019), 3590, DOI: 10.3390/s19163590.
[56] H. Chen, S. Jiao, M.Wang, A.A. Heidari, and X. Zhao: Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. Journal of Cleaner Production, 244 (2020), p. 118778, DOI: 10.1016/j.jclepro.2019.118778.
[57] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen: Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97 (2019), 849–872, DOI: 10.1016/ j.future.2019.02.028.
[58] M. Jamil and X.-S. Yang: A literature survey of benchmark functions for global optimization problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), (2013), 150, DOI: 10.1504/IJMMNO.2013.055204.
[59] A. Kaveh and S. Talatahari: A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3–4), (2010), 267–289, DOI: 10.1007/s00707-009-0270-4.
[60] J. Luo and B. Shi: Ahybrid whale optimization algorithm based on modified differential evolution for global optimization problems. Applied Intelligence, 49(5), (2000), 1982–2000, DOI: 10.1007/s10489-018-1362-4.
[61] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen: Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97 (2019), 849–872, DOI: 10.1016/ j.future.2019.02.028.
[62] P. Pruski and S. Paszek: Location of generating units most affecting the angular stability of the power system based on the analysis of instantaneous power waveforms. Archives of Control Sciences, 30(2), (2020), 273–293, DOI: 10.24425/acs.2020.133500.
[63] M.M. Hossain and A.Z. Khurshudyan: Heuristic control of nonlinear power systems: Application to the infinite bus problem. Archives of Control Sciences, 29(2), (2019), 279–288, DOI: 10.24425/acs.2019.129382.
[64] R. Devarapalli and B. Bhattacharyya:Aframework for H2=H? synthesis in damping power network oscillations with STATCOM. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44 (2020), 927-948, DOI: 10.1007/s40998-019-00278-4.
[65] G. Gurrala and I. Sen: Power system stabilizers design for interconnected power systems. IEEE Transactions on Power Systems, 25(2), (2010), 1042– 1051, DOI: 10.1109/TPWRS.2009.2036778.
[66] R.K. Varma: Introduction to FACTS controllers. In 2009 IEEE/PES Power Systems Conference and Exposition, (2009), 1–6, DOI: 10.1109/PSCE.2009.4840114.
[67] P. Kundur: Power System Stability and Control. Tata McGraw-Hill Education, 1994.
[68] M. Belazzoug, M. Boudour, and K. Sebaa: FACTS location and size for reactive power system compensation through the multi-objective optimization. Archives of Control Sciences, 20(4), (2010), 473–489, DOI: 10.2478/v10170-010-0027-2






DOI: 10.24425/acs.2021.138692