Adaptive FPA Algorithm based OPF with Unified Power Flow Controller


  • Immanuel A. Audisankara College of Engineering & Technology, India
  • Challa Babu Siddartha Institute of Science and Technology, India
  • Sudheer P. Sreenivasa Institute of Technology and Management Studies, India
  • Pavan Kumar Naidu R. Sasi Institute of Technology and Engineering, India
  • Nageswara Rao Atyam Presidency University, Bangalore, India



OPF, Adaptive FPA, Fuzzy, FACTS, UPFC


In this work a novel modified flower pollination algorithm has been developed to solve the problem of single and multi-objective Optimal Power Flow operations for Unified power Flow Controller in Flexible Alternating Current Transmission Systems. In the proposed Adaptive Flower Pollination Algorithm the best initial solution can be chosen from the fittest and also the weights are adaptively adjusted to get better convergence characteristics. The nature of the objective functions is non-linear and difficult to get best possible solutions within the boundary conditions of total power demand. The weak nodes are determined in the system to locate the UPFC with Fuzzy approach considering input parameters as L-Index and voltage magnitudes. The projected method is validated using IEEE-30 and IEEE-57 bus systems for three objective functions, namely, system real power loss minimization, fuel cost minimization and the combination of total generating cost and system real power loss. Results of Fuzzy- Adaptive Flower Pollination Algorithm based OPF optimization for UPFC produced optimum results for the considered objectives of total fuel cost, real power loss and for the multiobjective.


Download data is not yet available.


J. Carpentier, “Contribution e létude do Dispatching Economique,” Bull. Soc. Franc. Elect., pp. 431–447, 1962.

Stephen Frank & Steffen Rebennack (2016) An introduction to optimal power flow: Theory, formulation, and examples, IIE Transactions, 48:12, 1172-1197. DOI:

P. Biswas, P. Suganthan and G. Amaratunga, "Optimal Power Flow Solutions Using Algorithm Success History Based Adaptive Differential Evolution with Linear Population Reduction", 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 249-254. DOI:

Mohd Herwan Sulaiman, Zuriani Mustaffa, Ahmad Johari Mohamad, Mohd Mawardi Saari, Mohd Rusllim Mohamed, Optimal power flow with stochastic solar power using barnacles mating optimizer, International transactions on electrical energy systems, vol.31, Issue 5, May 2021. DOI:

S. Surender Reddy and P.R Bijwe “Multi-Objective Optimal Power Flow Using Efficient Evolutionary Algorithm”, International Journal of Emerging Electric Power Systems, April 1, 2017.

Y. R. Sood, “Evolutionary Programming Based Optimal Power Flow and its Validation for Deregulated Power System Analysis”, Electrical Power and Energy System, Elsevier, Vol. 29, No. 1, pp. 67-75, 2007. DOI:

LinWM, Cheng F S, Tsay M T(2002) An improved tabu search for economic dispatch with multiple minima. IEEE Trans Power Syst. 17(1):108–112. DOI:

Abdullah Khan ,Hashim Hizam ,Noor Izzri bin Abdul Wahab,Mohammad Lutfi Othman, “Optimal power flow using hybrid firefly and particle swarm optimization algorithm” PLOS ONE August 10, 2020 DOI:

Man Ding, Hanning Chen, NaLin, Shikai Jing, Fang Liu, Xiaodan Liang, WeiLiu, “Dynamic population artificial bee colony algorithm for multi-objective optimal power flow”, Saudi Journal of Biological Sciences Volume 24, Issue 3, March 2017, PP:703-710. DOI:

Layth Tawfeeq Al-Bahran and Ali Qasim Abdulrasool “Multi objective functions of constraint optimal power flow based on modified ant colony system optimization technique”, IOP Conference Series: Materials Science and Engineering, Volume 1105,December 2020, Baghdad, Iraq DOI:

A. Panda, M. Tripathy, “Optimal Power Flow Solution of Wind Integrated Power System Using Modified Bacteria Foraging Algorithm”, Elect. Power and Energy Systems, Elsevier, Vol. 54, pp.306-314, 2014. DOI:

Spoorthi Rakesh, Shanthi Mahesh, "A comprehensive overview on variants of CUCKOO search algorithm and applications", Electrical Electronics Communication Computer and Optimization Techniques (ICEECCOT) 2017 International Conference on, pp. 1-5, 2017. DOI:

M.A. Abido, “Optimal Power Flow Using Tabu SearchAlgorithm”, Electric Power Components System, Vol. 30, pp. 469–483, 2002. DOI:

S. Sivasubramani, K. S. Swarup, “Multi-Objective Harmony Search Algorithm for Optimal Power Flow Problem”, Electrical Power and Energy Systems. Elsevier, Vol. 33, pp. 745-752, 2011. DOI:

H. R. E. H. Bouchekara, “OPF Using Black-Hole-Based Optimization Approach”, App. Soft Computing. Elsevier, Vol. 24, pp. 879-888, 2014. DOI:

M. Sailaja, S. Maheswarapu, “Enhanced Genetic Algorithm Based Computation Technique for Multi-Objective OPF Solution”, Electrical. Power and Energy Systems, Elsevier, Vol. 32, pp. 736-742, 2010. DOI:

B. Mandal, P. K. Roy, “Multi-Objective Optimal Power Flow Using Quasi-Oppositional Teaching Learning Based Optimization”, Applied Soft Computing, Elsevier, Vol. 21, pp. 590-606, 2014. DOI:

S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, “Optimal Power Flow Using Gravitational Search Algorithm”, Energy Conversion and Management, Elsevier, Vol. 59, pp. 86-95, 2012. DOI:

X. S. Yang, “Flower Pollination Algorithm for Global Optimization”, in: Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249, 2012.

X. S. Yang, M. Karamanoglu, X. He., “Multiobjective Flower Algorithm for Optimization”, International Conference on Computational Science . Vol. 18, pp. 861-868, 2013. DOI:

K. S. Pandya, D. A. Dabhi and S. K. Joshi, “Comparative Study of Bat & Flower Pollination Optimization Algorithms in Highly Stressed Large Power System”, Power Syst. Conf. (PSC), Clemson University, 2015. DOI:

Hari Mohan Dubey, Manjaree Pandit, B.K. Panigrah, “Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism forwind integrated multi-objective dynamic economic dispatch”, Renewable Energy, Elsevier, Vol. 83, pp. 188-202,2015. DOI:

Sarjiya, F. P. Sakti and S. P. Hadi, "Optimal Power Flow Based on Flower Pollination Algorithm," 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 329-334, 2018. DOI:

Taranto GN, Pinto LMVG, Pereira MVF. Representation of FACTS devices in power system economic dispatch. IEEE Trans Power Syst 1992;7 (2):572–576. DOI:

Padhy, N.P. & M. A., Abdel-Moamen. (2008). A Generalized Newton’s Optimal Power Flow Modelling with Facts Devices. International Journal of Modelling and Simulation. 28. 229-238. DOI:

Ambriz-Perez H, Acha E, Fuerte-Esquivel CR. Advanced SVC model for Newton–Raphson Load Flow and Newton optimal power flow studies. IEEE Trans Power Syst 2000;15 (1):129–136. DOI:

R. P. Singh, V. Mukherjee, D. Prasad and W. A. Ansari, "Solution of optimal power flow problem of system with FACTS devices using MDE algorithm," 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 2020, pp. 1-6. DOI:

Khunkitti, S.; Siritaratiwat, A.; Premrudeepreechacharn, S.; Chatthaworn, R.; Watson, N.R. A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems. Energies 2018, 11, 2270. DOI:

Kessel P, Glavitch H (1986) Estimating the voltage stability of a power system. IEEE Trans Power Deliv 1(3): 346–354. DOI:

X. S. Yang, “Flower Pollination Algorithm for Global Optimization”, in: Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249, 2012. DOI:

Pavlyukevich, I. Lévy flights, “Non-Local Search and Simulated Annealing”, J. Computational Physics, Vol. 226, pp. 1830–1844, 2007. DOI:

Reynolds, A.M., Frye, M.A., “Free-Flight Odor Tracking in Drosophila is Consistent with an Optimal Intermittent Scale-Free Search”, PLoS One, 2, e354, 2007. DOI:

R. N. Mantegna,“Fast, Accurate Algorithm for Numerical Simulation of Lévy Stable Stochastic Process.” Phys Rev E., Vol. 49, No. 5, pp. 4677- 4683, 1994; DOI:

H. R. Tizhoosh. Opposition-Based Learning, “A New Scheme for Machine Intelligence”, International Conference on Computational Intelligence for Modelling, Control and Automation 2005, Vol. 1, pp. 695-701, 2005.

S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama,“Quasi-Oppositional Differential Evolution”. IEEE Congress on Evolutionary Computation, pp. 2229-2236, 2007. DOI:

Dwaipayan Chakraborty, SankhadipSahaand, Oindrilla Dutta, “DE- FPA: A Hybrid Differential Evolution-Flower Pollination Algorithm for Function Minimization”, International Conference on High Performance Computing and Applications (ICHPCA), Bhubaneswar, India, 2014. DOI:

Alsac O, Stott B (1973) Optimal load flow with steady state security. IEEE Trans. On Power Electronics, PAS-93, 745-751. DOI:

Abido MA (2002) Optimal power flow using particle swarm optimization. Electr Power Energy Syst 24(7): 563–571. DOI:

Ongsakul W,tantimaporn.T. Optimal power flow by improved evolutionary programming. Electr. Power comput, Syst 34(2006): 79–95. DOI:

C.Thitithamrongchai,B.Eua-arporn,self adaptive differential evolution based optimal power flow for units with non smooth fuel cost functions, J.Electr.Syst.32(2007): 88-99.

K. Vaisakh · L. R. Srinivas · Kala Meah, Genetic evolving ant direction PSODV hybrid algorithm for OPF with non-smooth cost functions, Electr Eng (2013) 95:185–199 DOI:




How to Cite

A. I, Babu C, P. S, R. PKN, Atyam NR. Adaptive FPA Algorithm based OPF with Unified Power Flow Controller. EAI Endorsed Trans Energy Web [Internet]. 2022 Oct. 12 [cited 2023 Feb. 6];9(40):e4. Available from: