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Metaheuristic nature-based algorithm for optimal reactive power planning

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Abstract

An oppositional based Harris Hawks optimization technique enthralled is suggested and implemented to reactive power optimization planning in power systems in this research. As transmission loss minimization is a fundamental criterion for secured power system operation, Var planning has become increasingly important for improved coordination in modern power systems. The Oppositional based Harris Hawk Optimizer (OHHO) algorithm, which is implemented on the IEEE 57 bus system, is proposed in this paper as an enhanced meta-heuristic nature inspired approach. The suggested algorithm is based on the Harris Hawk Optimizer (HHO) algorithm, which is a speculative algorithm with no intrinsic dependent variables. To get improved estimation for the predominant approach, the search space is subsequently altered by combining HHO with the Oppositional Based Learning (OBL) technique. In the present study, the OHHO is proposed for reducing transmission losses, operating costs, and improving voltage profile at buses. The impact of the optimizers' update technique on the objective functions is examined. The research focuses on issues such as reactive power provided by generator buses, shunt capacitors, and transformer tap position changes. The simulation outcomes gained on typical test systems demonstrate that the proposed OHHO outperforms HHO, and other optimization techniques recently published in the state-of-the-art literature.

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References

  • Babu R, Raj S, Bhattacharyya B (2020) Weak bus-constrained PMU placement for complete observability of a connected power network considering voltage stability indices. Prot Control Modern Power Syst 5(1):1–14

    Article  Google Scholar 

  • Bhattacharya A, Chattopadhyay PK (2010) Solution of optimal reactive power flow using biogeography-based optimization. Int J Electr Electron Eng 4(8):568–576

    Google Scholar 

  • Bhattacharyya B, Saurav R (2015) A novel approach for the voltage stability assessment and reactive power planning. In: 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), pp. 1534–1538. IEEE, 2015.

  • Bhattacharyya B, Karmakar N (2020) A planning strategy for reactive power in power transmission network using soft computing techniques. Int J Power Energy Syst. https://doi.org/10.2316/J.2020.203-0214

    Article  Google Scholar 

  • Bhattacharyya B, Raj S (2016) PSO based bio inspired algorithms for Var planning. Int J Electr Power Energy Syst 74:396–402

    Article  Google Scholar 

  • Bhattacharyya B, Raj S (2017) Differential evolution technique for the optimization of reactive power reserves. J Circuits Syst Comput 26(10):1750155

    Article  Google Scholar 

  • Chiang HD, Wang JC, Cockings O, Shin HD (1990) Optimal capacitor placements in distribution systems: part 2: solution algorithms and numerical results. IEEE Trans Power Deliv 5(2):643–649

    Article  Google Scholar 

  • Chiang H-D, Wang J-C, Cockings O, Shin H-D (1990) Optimal capacitor placements in distribution systems. I. A new formulation and the overall problem. IEEE Trans Power Delivery 5(2):634–642

    Article  Google Scholar 

  • Dai C, Chen W, Zhu Y, Zhang X (2009) Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst 24(3):1218–1231

    Google Scholar 

  • Deng Z, Rotaru MD, Sykulski JK (2019) Kriging assisted surrogate evolutionary computation to solve optimal power flow problems. IEEE Trans Power Syst 35(2):831–839

    Article  Google Scholar 

  • Duman Serhat, Sönmez Y, Güvenç U, Yörükeren N (2012) Optimal reactive power dispatch using a gravitational search algorithm. IET Gener Transm Distrib 6(6):563–576

    Article  Google Scholar 

  • Ela El, Abou AA, Abido MA, Spea SR (2011) Differential evolution algorithm for optimal reactive power dispatch. Elect Power Syst Res 81(2):458–464

    Article  Google Scholar 

  • Ettappan M, Vimala V, Ramesh S, ThiruppathyKesavan V (2020) Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using artificial bee colony algorithm. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103085

    Article  Google Scholar 

  • Ghasemi M, Ghavidel S, Ghanbarian MM, Gitizadeh M (2015) Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm. Inf Sci 294:286–304

    Article  MathSciNet  Google Scholar 

  • Grudinin N (1998) Reactive power optimization using successive quadratic programming method. IEEE Trans Power Syst 13(4):1219–1225

    Article  Google Scholar 

  • Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  • Karmakar N, Bhattacharyya B (2021) Techno-economic model for reactive power planning using series-shunt compensation devices under load demand in power transmission network. Energy Technol. https://doi.org/10.1002/ente.202100156

    Article  Google Scholar 

  • Karmakar N, Bhattacharyya B (2021) Techno-economic approach towards reactive power planning ensuring system security on energy transmission network. Int J Emerg Electr Power Syst 22(3):309–324

    Google Scholar 

  • Kotb MF, El-Fergany AA (2019) Optimal power flow solution using moth swarm optimizer considering generating units prohibited zones and valve ripples. J Elect Eng Technol. https://doi.org/10.1007/s42835-019-00144-7

    Article  Google Scholar 

  • Lee KY, Park YM, Ortiz JL (1984) Optimal real and reactive power dispatch. Electr Power Syst Res 7(3):201–212

    Article  Google Scholar 

  • Mahadevan K, Kannan PS (2010) Comprehensive learning particle swarm optimization for reactive power dispatch. Appl Soft Comput 10(2):641–652

    Article  Google Scholar 

  • Mahapatra S, Jha AN, Panigrahi BK (2016) Hybrid technique for optimal location and cost sizing of thyristor controlled series compensator to upgrade voltage stability. IET Gener Trans Distrib 10(8):1921–1927

    Article  Google Scholar 

  • Sheila M, Badi M, Raj S (2019) Implementation of PSO, it’s variants and Hybrid GWO-PSO for improving Var planning. In: 2019 Global Conference for Advancement in Technology (GCAT), pp. 1–6. IEEE, 2019.

  • Sheila M, Saurav R, Mohan Krishna S (2020) Optimal TCSC Location for Reactive Power Optimization Using Oppositional Salp Swarm Algorithm. In Innovation in Electrical Power Engineering, Communication, and Computing Technology, pp. 413–424. Springer, Singapore, 2020.

  • Mukherjee A, Mukherjee V (2015) Solution of optimal reactive power dispatch by chaotic krill herd algorithm. IET Gener Transm Distrib 9(15):2351–2362

    Article  Google Scholar 

  • Patel N, Bhattacharjee K (2020) A comparative study of economic load dispatch using sine cosine algorithm Scientia Iranica. Trans D Comput Sci Eng Elect 27(3):1467–1480

    Google Scholar 

  • Raj, Saurav, Bhattacharyya B (2016) Weak bus-oriented optimal Var planning based on grey wolf optimization. In: 2016 National Power Systems Conference (NPSC), pp. 1–5. IEEE, 2016.

  • Raj, Saurav, Bhattacharyya B (2016) Weak bus determination and real power loss minimization using Grey wolf optimization. In: 2016 IEEE 6th International Conference on Power Systems (ICPS), pp. 1–4. IEEE, 2016.

  • Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid nelder-mead simplex based firefly algorithm. Int J Electr Power Energy Syst 66:9–24

    Article  Google Scholar 

  • Roeva O, Zoteva D, Atanassova V, Atanassov K, Castillo O (2020) Cuckoo search and firefly algorithms in terms of generalized net theory. Soft Comput 24(7):4877–4898. https://doi.org/10.1007/s00500-019-04241-7

    Article  Google Scholar 

  • Saurav R, Bhattacharyya B (2018) Var planning by opposition-based grey wolf optimization method. Int Trans Electr Energy Syst 28(6):e2551

    Article  Google Scholar 

  • Saurav R, Mahapatra S, Shiva CK, Bhattacharyya B (2021) Implementation and optimal sizing of TCSC for the solution of Var planning problem using quasi-oppositional salp swarm algorithm. Int J Energy Optim Eng (IJEOE) 10(2):74–103

    Google Scholar 

  • Sereeter B, Vuik C, Witteveen C (2019) On a comparison of newton-raphson solvers for power flow problems. J Comput Appl Math 360:157–169

    Article  MathSciNet  Google Scholar 

  • Shaheen AM, El-Sehiemy RA, Farrag SM (2019) A Var planning procedure considering iterative identification of VAR candidate buses. Neural Comput Appl 31:653–674

    Article  Google Scholar 

  • Shaw Binod, Mukherjee V, Ghoshal SP (2014) Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Int J Electr Power Energy Syst 55:29–40

    Article  Google Scholar 

  • Sheila M, Malik N, Jha AN, Panigrahi BK (2017) Voltage stability enhancement by IGSA-FA hybrid technique implementation for optimal location of TCSC. J Eng Sci Technol 12(9):2360–2373

    Google Scholar 

  • Sheila M, Bishwajit D, Saurav R (2021) A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems. Int J Intell Syst 36(12):7641–7681

    Article  Google Scholar 

  • Shekarappa G, Swetha, Mahapatra S, Raj S (2021) Voltage constrained reactive power planning problem for reactive loading variation using hybrid harris hawk particle swarm optimizer. Electr Power Compon Syst. https://doi.org/10.1080/15325008.2021.1970060

    Article  Google Scholar 

  • Swetha Shekarappa G, Sheila M, Saurav R (2021) Voltage Constrained Var planning by Ameliorated HHO Technique. In: Recent Advances in Power Systems, pp. 435–443. Springer, Singapore

  • Shin MRN, Sulaiman MH, Mustaffa Z, Daniyal H (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput 59:210–222

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.engappai.2019.103330

    Article  Google Scholar 

  • Tizhoosh Hamid R (2005) Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), vol. 1, pp. 695–701. IEEE, 2005.

  • Wang C, Hao Zhong C, Yao LZ (2008) Reactive power optimization by plant growth simulation algorithm. In: 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 771–774. IEEE, 2008.

  • Yuan G, Yang W (2019) Study on optimization of economic dispatching of electric power system based on hybrid intelligent algorithms (PSO and AFSA). Energy 183:926–935

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Alliance University, Bangalore, 562106, India

    Swetha Shekarappa Gudadappanavar & Sheila Mahapatra

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  1. Swetha Shekarappa Gudadappanavar
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  2. Sheila Mahapatra
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Correspondence to Sheila Mahapatra.

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Gudadappanavar, S.S., Mahapatra, S. Metaheuristic nature-based algorithm for optimal reactive power planning. Int J Syst Assur Eng Manag 13, 1453–1466 (2022). https://doi.org/10.1007/s13198-021-01489-x

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