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Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination

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This article has four main objectives. These are: to develop the dynamic fitness-distance balance (dFDB) selection method for meta-heuristic search algorithms, to develop a strong optimization algorithm using the dFDB method, to create an optimization model of the coordination of directional overcurrent relays (DOCRs) problem, and to optimize the DOCRs problem using the developed algorithm, respectively. A comprehensive experimental study was conducted to analyze the performance of the developed dFDB selection method and to evaluate the optimization results of the DOCRs problem. Experimental studies were carried out in two steps. In the first step, to test the performance of the developed dFDB method and optimization algorithm, studies were conducted on three different benchmark test suites consisting of different problem types and dimensions. The data obtained from the experimental studies were analyzed using non-parametric statistical methods and the most effective among the developed optimization algorithms was determined. In the second step, the DOCRs problem was optimized using the developed algorithm. The performance of the proposed method for the solution to the DOCRs coordination problem was evaluated on five test systems including the IEEE 3-bus, the IEEE 4-bus, the 8-bus, the 9-bus, and the IEEE 30-bus test systems. The numerical results of the developed algorithm were compared with previously proposed algorithms available in the literature. Simulation results showed the effectiveness of the proposed method in minimizing the relay operating time for the optimal coordination of DOCRs.

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References

  1. Kahraman HT, Aras S, Gedikli E (2020) Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl-Based Syst 190:105169

    Google Scholar 

  2. Kahraman HT, Aras S (2019) Investigation of the Most effective meta-heuristic optimization technique for constrained engineering problems. In: The International Conference on Artificial Intelligence and Applied Mathematics in Engineering, Springer, Cham, pp 484–501

  3. Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300

    Google Scholar 

  4. Zeineldin HH, El-Saadany EF, Salama MMA (2006) Optimal coordination of overcurrent relays using a modified particle swarm optimization. Electr Power Syst Res 76(11):988–995

    Google Scholar 

  5. Mahari A, Seyedi H (2013) An analytic approach for optimal coordination of overcurrent relays. IET Gener Transm Distrib 7(7):674–680

    Google Scholar 

  6. Korashy A, Kamel S, Youssef AR, Jurado F (2019) Modified water cycle algorithm for optimal direction overcurrent relays coordination. Appl Soft Comput 74:10–25

    Google Scholar 

  7. Farzinfar M, Jazaeri M, Razavi F (2014) A new approach for optimal coordination of distance and directional over-current relays using multiple embedded crossover PSO. Int J Electr Power Energy Syst 61:620–628

    Google Scholar 

  8. So CW, Li KK, Lai KT, Fung KY (1997) Application of genetic algorithm for overcurrent relay coordination. In: International Conf. of Developments in Power Syst. Protection, pp 66–69

  9. Mansour MM, Mekhamer SF, El-Kharbawe N (2007) A modified particle swarm optimizer for the coordination of directional overcurrent relays. IEEE Trans Power Deliv 22(3):1400–1410

    Google Scholar 

  10. Razavi F, Abyaneh HA, Al-Dabbagh M, Mohammadi R, Torkaman H (2008) A new comprehensive genetic algorithm method for optimal overcurrent relays coordination. Electr Power Syst Res 78(4):713–720

    Google Scholar 

  11. Shih MY, Enríquez AC, Trevino LMT (2014) On-line coordination of directional overcurrent relays: performance evaluation among optimization algorithms. Electr Power Syst Res 110:122–132

    Google Scholar 

  12. Moravej Z, Adelnia F, Abbasi F (2015) Optimal coordination of directional overcurrent relays using NSGA-II. Electr Power Syst Res 119:228–236

    Google Scholar 

  13. Amraee T (2012) Coordination of directional overcurrent relays using seeker algorithm. IEEE Trans Power Deliv 27(3):1415–1422

    Google Scholar 

  14. Singh M, Panigrahi BK, Abhyankar AR (2013) Optimal coordination of directional over-current relays using teaching learning-based optimization (TLBO) algorithm. Int J Electr Power Energy Syst 50:33–41

    Google Scholar 

  15. Moirangthem J, Krishnanand KR, Dash SS, Ramaswami R (2013) Adaptive differential evolution algorithm for solving non-linear coordination problem of directional overcurrent relays. IET Gener Transm Distrib 7(4):329–336

    Google Scholar 

  16. Chelliah TR, Thangaraj R, Allamsetty S, Pant M (2014) Coordination of directional overcurrent relays using opposition based chaotic differential evolution algorithm. Int J Electr Power Energy Syst 55:341–350

    Google Scholar 

  17. Albasri FA, Alroomi AR, Talaq JH (2015) Optimal coordination of directional overcurrent relays using biogeography-based optimization algorithms. IEEE Trans Power Deliv 30(4):1810–1820

    Google Scholar 

  18. Kim CH, Khurshaid T, Wadood A, Farkoush SG, Rhee SB (2018) Gray wolf optimizer for the optimal coordination of directional overcurrent relay. J Electr Eng Technol 13(3):1043–1051

    Google Scholar 

  19. Rajput VN, Pandya KS (2017) Coordination of directional overcurrent relays in the interconnected power systems using effective tuning of harmony search algorithm. Sustain Comput Inform Syst 15:1–15

    Google Scholar 

  20. El-Fergany AA, Hasanien HM (2017) Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm. Int Trans Electr Energy Syst 27(11):e2395

    Google Scholar 

  21. Zellagui M, Benabid R, Boudour M, Chaghi A (2014) Application of firefly algorithm for optimal coordination of directional overcurrent protection relays in presence of series compensation. J Autom Syst Eng:92–107

  22. Hussain MH, Musirin I, Abidin AF, Rahim SRA (2014) Solving directional overcurrent relay coordination problem using artificial bees colony. Int J Electr Electron Sci Eng 8(5):705–710

    Google Scholar 

  23. El-Fergany A (2016) Optimal directional digital overcurrent relays coordination and arc-flash hazard assessments in meshed networks. Int Trans Electr Energy Syst 26(1):134–154

    Google Scholar 

  24. Saha D, Datta A, Das P (2016) Optimal coordination of directional overcurrent relays in power systems using symbiotic organism search optimisation technique. IET Gener Transm Distrib 10(11):2681–2688

    Google Scholar 

  25. Srinivas STP (2019) Application of improved invasive weed optimization technique for optimally setting directional overcurrent relays in power systems. Appl Soft Comput 79:1–13

    Google Scholar 

  26. Ahmadi SA, Karami H, Sanjari MJ, Tarimoradi H, Gharehpetian GB (2016) Application of hyper-spherical search algorithm for optimal coordination of overcurrent relays considering different relay characteristics. Int J Electr Power Energy Syst 83:443–449

    Google Scholar 

  27. Korashy A, Kamel S, Jurado F, Youssef AR (2019) Hybrid whale optimization algorithm and Grey wolf optimizer algorithm for optimal coordination of direction overcurrent relays. Electr Power Compon Syst 47(6–7):644–658

    Google Scholar 

  28. Khurshaid T, Wadood A, Farkoush SG, Kim CH, Yu J, Rhee SB (2019) Improved firefly algorithm for the optimal coordination of directional overcurrent relays. IEEE Access 7:78503–78514

    Google Scholar 

  29. Radosavljević J, Jevtić M (2016) Hybrid GSA-SQP algorithm for optimal coordination of directional overcurrent relays. IET Gener Transm Distrib 10(8):1928–1937

    Google Scholar 

  30. Zellagui M, Abdelaziz AY (2015) Optimal coordination of directional over-current relays using hybrid PSO-DE algorithm. International Electrical Engineering Journal (IEEJ) 6(4):1841–1849

    Google Scholar 

  31. Radosavljević J (2018) Metaheuristic optimization in power engineering. Institution of Engineering and Technology

    Google Scholar 

  32. Corrêa R, Cardoso G Jr, de Araújo OC, Mariotto L (2015) Online coordination of directional overcurrent relays using binary integer programming. Electr Power Syst Res 127:118–125

    Google Scholar 

  33. Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H (2020) Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng Appl Artif Intell 87:103330

    Google Scholar 

  34. Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018

    Google Scholar 

  35. Zhang Y, Jin Z (2020) Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246

    Google Scholar 

  36. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2019) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190

    Google Scholar 

  37. Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10(2):253–277

    Google Scholar 

  38. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Google Scholar 

  39. Tang D, Liu Z, Yang J, Zhao J (2018) Memetic frog leaping algorithm for global optimization. Soft Comput 1-29

  40. Chen X, Xu B (2018) Teaching-learning-based artificial bee colony. In: International Conference on Swarm Intelligence. Springer, Cham, pp 166–178

  41. Pierezan J, Coelho LDS (2018) Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1–8

  42. Civicioglu P, Besdok E, Gunen MA, Atasever UH (2018) Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Comput & Applic:1–15

  43. Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DN (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22

    Google Scholar 

  44. Punnathanam V, Kotecha P (2016) Yin-Yang-pair optimization: a novel lightweight optimization algorithm. Eng Appl Artif Intell 54:62–79

    Google Scholar 

  45. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Google Scholar 

  46. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  47. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18

    Google Scholar 

  48. Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Google Scholar 

  49. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Google Scholar 

  50. Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Google Scholar 

  51. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183

    Google Scholar 

  52. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144

    MathSciNet  MATH  Google Scholar 

  53. Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247

    Google Scholar 

  54. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp 210–214

  55. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  56. Baloochian H, Ghaffary HR, Balochian S (2020) Metaheuristic anopheles search algorithm. Evolutionary Intelligence. https://doi.org/10.1007/s12065-019-00348-w

  57. Zhao W, Wang L, Zhang Z (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl-Based Syst 163:283–304

    Google Scholar 

  58. Anita, Yadav A (2019) AEFA: Artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108

    Google Scholar 

  59. Wang GG (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing:1–14

  60. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  61. Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 53:407–419

    Google Scholar 

  62. Mittal H, Pal R, Kulhari A, Saraswat M (2016) Chaotic kbest gravitational search algorithm (ckgsa). In: 2016 Ninth International Conference on Contemporary Computing (IC3), pp 1–6

  63. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Google Scholar 

  64. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  65. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    MATH  Google Scholar 

  66. Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore

  67. Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical Report

    Google Scholar 

  68. Thangaraj R, Pant M, Deep K (2010) Optimal coordination of over-current relays using modified differential evolution algorithms. Eng Appl Artif Intell 23(5):820–829

    Google Scholar 

  69. Singh M, Panigrahi BK, Abhyankar AR, Das S (2014) Optimal coordination of directional over-current relays using informative differential evolution algorithm. J Comput Sci 5(2):269–276

    Google Scholar 

  70. Mohammadi R, Abyaneh HA, Rudsari HM, Fathi SH, Rastegar H (2011) Overcurrent relays coordination considering the priority of constraints. IEEE Trans Power Deliv 26(3):1927–1938

    Google Scholar 

  71. Dipti (2007) Hybrid genetic algorithms and their applications. PhD thesis, Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

  72. Deep K, Bansal JC (2009) Optimization of directional overcurrent relay times using laplace crossover particle swarm optimization (LXPSO). In: Proc nature & biologically inspired computing, 2009. NaBIC 2009. World Congress, pp 288–293

  73. Deep K, Birla D, Maheshwari R, Gupta H, Takur M (2006) A population based heuristic algorithm for optimal relay operating time. World Journal of Modelling and Simulation 3:167–176

    Google Scholar 

  74. Thakur M (2007) New real coded genetic algorithms for global optimization. Ph.D. Thesis, India: Department of Mathematics, Indian Institute of Technology Roorkee

  75. Thakur M, Kumar A (2016) Optimal coordination of directional over current relays using a modified real coded genetic algorithm: a comparative study. Int J Electr Power Energy Syst 82:484–495

    Google Scholar 

  76. Darji GU, Patel MJ, Rajput VN, Pandya KS (2015) A tuned cuckoo search algorithm for optimal coordination of Directional Overcurrent Relays. In 2015 International Conference on Power and Advanced Control Engineering (ICPACE), pp 162–167

  77. Noghabi A, Sadeh J, Mashhadi H (2009) Considering different network topologies in optimal overcurrent relay coordination using a hybrid GA. IEEE Trans Power Deliv 24(4):1857–1863

    Google Scholar 

  78. Yu J, Kim CH, Rhee SB (2019) Oppositional Jaya algorithm with distance-adaptive coefficient in solving directional over current relays coordination problem. IEEE Access 7:150729–150742

    Google Scholar 

  79. Korashy A, Kamel S, Youssef A. R, Jurado F (2019) Most valuable player algorithm for solving direction overcurrent relays coordination problem. In 2019 International conference on innovative trends in computer engineering (ITCE), pp 466–471

  80. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

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Authors and Affiliations

  1. Software Engineering, OF Technology Faculty, Karadeniz Technical University, 61080, Trabzon, Turkey

    Hamdi Tolga Kahraman &  Sefa ARAS

  2. Department of Mechatronics, Dogus Vocational School of Higher Education, Dogus University, 34775, Istanbul, Turkey

    Huseyin Bakir

  3. Electrical Engineering, Engineering and Natural Sciences Faculty, Bandirma Onyedi Eylul University, 10200, Bandirma, Turkey

    Serhat Duman

  4. HAVELSAN, Ankara, Turkey

    Mehmet Katı

  5. Electrical and Electronics Engineering, Technology Faculty, Duzce University, 81620, Duzce, Turkey

    Ugur Guvenc

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  1. Hamdi Tolga Kahraman
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  2. Huseyin Bakir
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  3. Serhat Duman
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  6. Ugur Guvenc
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Correspondence to Hamdi Tolga Kahraman.

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Kahraman, .T., Bakir, H., Duman, S. et al. Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination. Appl Intell 52, 4873–4908 (2022). https://doi.org/10.1007/s10489-021-02629-3

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