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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Research Article

Multi-Objective Optimization in the Presence of OGIPFC Using NSMMP Algorithm

Author(s): Balasubbareddy Mallala*, Venkata Prasad Papana and Kowstubha Palle

Volume 17, Issue 1, 2024

Published on: 21 June, 2023

Page: [60 - 81] Pages: 22

DOI: 10.2174/2352096516666230504105054

Price: $65

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Abstract

Background: Customers expect quality, uninterrupted power with cost-effective electricity in the latest trend. However, outages, severe storms, old infrastructure, and cost pressures can lead to ambiguity in power generation and transmission. To improve line power transmission capability, the right flexible AC transmission systems (FACTS) device may save millions of dollars.

Methods: In this study, a FACTS controller named Optimal Generalized Interline Power Flow Controller (OGIPFC) was developed. Furthermore, for optimization, the Modified Marine Predator Algorithm (MMPA), which is a modification of the recently developed Marine Predator Algorithm (MPA). The optimum technique was used to evaluate a set of prioritized considered objective minimizations. A variety of factors must be maximized, such as generation cost, emissions, and power loss.

Results: The performance of the proposed algorithm was analysed on benchmark test functions, and then single objective optimization problems of standard IEEE-30 bus system were solved and compared with the existing algorithms. The proposed algorithm was restricted to solving the single objective problem only, so it was further implemented with non-dominating sorting to solve the multiobjective optimization problem. The proposed multi-objective version is named as Non-dominating Sorting Modified Marine Predator Algorithm (NSMMPA), and it was validated on benchmark test functions and the IEEE-30 bus system.

Conclusion: Finally, the OPF problem was solved with the incorporation of OGIPFC using the proposed methods, which resulted in better solutions and made the system more effective in operation.

Graphical Abstract

[1]
B. Mallala, V.P. Papana, R. Sangu, K. Palle, and V.K.R. Chinthalacheruvu, "Multi-objective optimal power flow solution using a non-dominated sorting hybrid fruit fly-based artificial bee colony", Energies, vol. 15, no. 11, p. 4063, 2022.
[http://dx.doi.org/10.3390/en15114063]
[2]
M.A.M. Shaheen, D. Yousri, A. Fathy, H.M. Hasanien, A. Alkuhayli, and S.M. Muyeen, "A novel application of improved marine predators algorithm and particle swarm optimization for solving the ORPD problem", Energies, vol. 13, no. 21, p. 5679, 2020.
[http://dx.doi.org/10.3390/en13215679]
[3]
Balasubbareddy Mallala, "Salp swarm algorithm for solving optimal power flow problem with thyristor-controlled series capacitor", J. Electron. Sci. Technol, vol. 20, no. 2, 2022.
[4]
M.Z. Islam, M.L. Othman, N.I. Abdul Wahab, V. Veerasamy, S.R. Opu, A. Inbamani, and V. Annamalai, "Marine predators algorithm for solving single-objective optimal power flow", PLoS One, vol. 16, no. 8, p. e0256050, 2021.
[http://dx.doi.org/10.1371/journal.pone.0256050] [PMID: 34383821]
[5]
P. Jangir, H. Buch, S. Mirjalili, and P. Manoharan, "MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems", Evol. Intell., vol. 16, no. 1, pp. 169-195, 2023.
[http://dx.doi.org/10.1007/s12065-021-00649-z]
[6]
A.A. Heidari, R. Ali Abbaspour, and A. Rezaee Jordehi, "Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems", Appl. Soft Comput., vol. 57, no. August, pp. 657-671, 2017.
[http://dx.doi.org/10.1016/j.asoc.2017.04.048]
[7]
W. Bai, I. Eke, and K.Y. Lee, "An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem", Control Eng. Pract., vol. 61, no. April, pp. 163-172, 2017.
[http://dx.doi.org/10.1016/j.conengprac.2017.02.010]
[8]
M. Balasubbareddy, S. Sivanaga Raju, and V. Chintalapudi, "Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm", Eng. Sci. Technol. an Int. J., vol. 18, no. 4, pp. 603-615, 2015.
[9]
M. Balasubbareddy, "Multi-objective OPF problem analysis with practical constraints in the presence of facts devices using NSHCSA", In: Advances in Intelligent Systems and Computing., vol. 799, Springer Singapore, pp. 423-434, 2019.
[http://dx.doi.org/10.1007/978-981-13-1135-2_32]
[10]
M. Balasubba Reddy, and Y.P. Obulesh, "Optimal power flow in the presence of generalized interline power flow controller", Int. J. Recent Technol. Eng. (IJRTE), vol. 3, no. 2, 2014.
[11]
M. Balasubbareddya, S. Sivanagarajub, C. Venkata Sureshc, A.V. Naresh Babud, and D. Srilathaa, "A non-dominated sorting hybrid cuckoo search algorithm for multi-objective optimization in the presence of FACTS devices", Russ. Electr. Eng., vol. 88, no. 1, pp. 44-53, 2017.
[http://dx.doi.org/10.3103/S1068371217010059]
[12]
M. Balasubbareddy, "Multi-objective optimization in the presence of ramp-rate limits using non-dominated sorting hybrid fruit fly algorithm", Ain Shams Eng. J., vol. 7, no. 2, pp. 895-905, 2016.
[http://dx.doi.org/10.1016/j.asej.2016.01.005]
[13]
M. Balasubbareddy, "Optimal power flow solution using ameliorated ant lion optimization algorithm", Int. J. Mech. Eng, vol. 7, no. 1, 2022.
[14]
W. Assawinchaichote, C. Angeli, and J. Pongfai, "Proportional-integral-derivative parametric autotuning by Novel Stable Particle Swarm Optimization (NSPSO)", IEEE Access, vol. 10, pp. 40818-40828, 2022.
[http://dx.doi.org/10.1109/ACCESS.2022.3167026]
[15]
S.L.V. Tummala, N.S.S. Ayyarao, R.M. Elavarasan, N. Polumahanthi, M. Rambabu, G. Saini, B. Khan, and B. Alatas, "War strategy optimization algorithm: A new effective metaheuristic algorithm for global optimization", IEEE Access, vol. 10, pp. 25073-25105, 2022.
[16]
G. Lei, X. Chang, Y. Tianhang, and W. Tuerxun, "An improved mayfly optimization algorithm based on median position and its application in the optimization of PID parameters of hydro-turbine governor", IEEE Access, vol. 10, pp. 36335-36349, 2022.
[http://dx.doi.org/10.1109/ACCESS.2022.3160714]
[17]
S. Duan, H. Luo, and H. Liu, A multi-strategy seeker optimization algorithm for optimization constrained engineering problems. IEEE, vol. 10, pp. 7165-7195, 2022.
[18]
A. Sowik, and K. Cpaka, "Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications", IEEE Trans. Industr. Inform., vol. 18, no. 1, pp. 546-558, 2022.
[http://dx.doi.org/10.1109/TII.2021.3067719]
[19]
A. Ahmed, A.G. Marwa, Z.M. Yaseen, and R.M. Ghoniem, Grasshopper optimization algorithm with crossover operators for feature selection and solving engineering problems. IEEE, vol. Vol. 10, pp. 23304-23320, 2022.
[20]
M. Balasubbareddy, "Squirrel search algorithm for solving optimal reactive power dispatch problem with FACTS device", Int. J. Inno. Technol. Explor. Eng., vol. 9, no. 3, pp. 854-858, 2020.
[21]
A. Faramarzi, M. Heidarinejad, S. Mirjalili, and H.G. Amir, "Marine Predators Algorithm: A Nature-inspired Metaheuristic", In: Expert Systems With Applications., Elsevier Ltd., 2020.
[22]
A.V. NareshBabu, T. Ramana, and S. Sivanagarajuc, "Analysis of optimal power flow problem based on two stage initialization algorithm", Int. J. Electr. Power Energy Syst., 2014.
[23]
V. Chintalapudi, "Analysis and effect of multi-fuel and practical constraints on economic load dispatch in the presence of Unified Power Flow Controller using UDTPSO", Ain Shams Eng. J., vol. 6, no. 3, pp. 803-817, 2015.
[24]
A-F. Attia, A. Raga, E.L. Sehiemy, and H.M. Hasanien, "Optimal power flow solution in power systems using a novel Sine-Cosine algorithm", Elect. power Energy Sys., vol. 99, no. 2018, pp. 331-343, 2018.
[25]
W. Warid, "Optimal power flow using the AMTPG-Jaya algorithm", Appl. Soft Comput., vol. 91, p. 106252, 2020.
[http://dx.doi.org/10.1016/j.asoc.2020.106252]
[26]
T. Niknam, M. Narimani, M. Jabbari, and A.R. Malekpour, "A modified shuffle frog leaping algorithm for multi-objective optimal power flow", Energy, vol. 36, no. 11, pp. 6420-6432, 2011.
[http://dx.doi.org/10.1016/j.energy.2011.09.027]
[27]
R. Arul, G. Ravi, and S. Velusami, "Solving optimal power flow problems using chaotic self-adaptive differential harmony search algorithm", Electric Power Components Sys., vol. 8, no. 2013, pp. 782-805, 2013.
[http://dx.doi.org/10.1080/15325008.2013.769033]
[28]
G. Chen, S. Qiu, Z. Zhang, Z. Sun, and H. Liao, "Optimal power flow using gbest-guided cuckoo search algorithm with feedback control strategy and constraint domination rule", Hindawi Mathematical Problems Eng., vol. 2017, p. 14, 2017.
[29]
"U. Kılıç, “Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones”", Electr. Eng., vol. 97, no. 2, pp. 101-110, 2015.
[http://dx.doi.org/10.1007/s00202-014-0315-0]
[30]
S. Khunkitti, A. Siritaratiwat, and S. Premrudeepreechacharn, "A hybrid DA-PSO optimization algorithm for multi objective optimal power flow problems", Energies., vol. 11, no. 9, p. 2270, 2018.
[http://dx.doi.org/10.3390/en11092270]
[31]
L. Slimani, and T. Bouktir, "Economic power dispatch of power systems with pollution control using artificial bee colony optimization", Turk. J. Electr. Eng. Comput. Sci., vol. 21, pp. 1515-1527, 2013.
[http://dx.doi.org/10.3906/elk-1106-10]
[32]
M. Balasubbareddy, D. Dwivedi, G.V.K. Murthy, and K.S. Kumar, "Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization", Int. J. Electrical Comp. Eng. (IJECE), vol. 13, no. 1, pp. 1060-1077, 2023.
[http://dx.doi.org/10.11591/ijece.v13i1.pp1060-1077]

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