Skip to main content
Log in

Elitist Harris Hawks Optimized Voltage Stability Enhancement in Radial Distribution System

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

In this paper, a novel Elitist Harris Hawks Optimization (EHHO) algorithm is presented for multi-objective function by optimal location and size of the photovoltaic (PV) and distribution static compensators (DSTATCOM). The multi-objective function includes power loss reduction, voltage profile enhancement, and voltage stability improvement in Distributed Generation (DG). The proposed EHHO algorithm is used to calculate the size and search for the finest location of PV and DSTATCOM. The Newton–Raphson technique is applied to analyse the load flow. To demonstrate the performance of the proposed method, IEEE 30-bus Radial Distribution System (RDS) test system is used, and compared with the FLSA, IWHO, TLBO, and BONMIN algorithms under various loading conditions. According to simulation results, the proposed EHHO method is more effective at supporting power loss reduction, improves voltage profile and voltage stability, in distribution networks than other existing algorithms. The proposed EHHO method has a Voltage Stability Index value of 0.9879 p. u and a loss of power value of 39.2908 kW.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Hung DQ, Mithulananthan N, Bansal RC (2013) Analytical strategies for renewable distributed generation integration considering energy loss minimization. Appl Energy 105:75–85

    Article  Google Scholar 

  2. Pepermans G, Driesen J, Haeseldonckx D, Belmans R, Dhaeseleer W (2005) Distributed generation: definition, benefits and issues. Energy Policy 33(6):787–798

    Article  Google Scholar 

  3. Acharya N, Mahat P, Mithulananthan N (2006) An analytical approach for DG allocation in primary distribution network. Int J Electr Power Energy Syst 28(10):669–678

    Article  Google Scholar 

  4. Agarwal U, Jain N (2019) Distributed energy resources and supportive methodologies for their optimal planning under modern distribution network: a review. Technol Econ Smart Grids Sustain Energy 4(1):1–21

    Article  Google Scholar 

  5. Moghaddam MJH (2021) Power quality improvement in the distribution network using optimization of the hybrid distributed generation system (Doctoral dissertation, Victoria University)

  6. Suresh MCV, Edward JB (2020) A hybrid algorithm based optimal location of DG units for loss reduction in the distribution system. Appl Soft Comput 91:106191

    Article  Google Scholar 

  7. Rafi FHM, Hossain MJ, Rahman MS, Taghizadeh S (2020) An overview of unbalance compensation techniques using power electronic converters for active distribution systems with renewable generation. Renew Sustain Energy Rev 125:109812

    Article  Google Scholar 

  8. Heidari A, Agelidis VG, Kia M, Pou J, Aghaei J, Shafie-Khah M, Catalão JP (2016) Reliability optimization of automated distribution networks with probability customer interruption cost model in the presence of DG units. IEEE Trans Smart Grid 8(1):305–315

    Article  Google Scholar 

  9. Jamil M, Anees AS (2016) Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits. Energy 103:231–239

    Article  Google Scholar 

  10. Naderi Y, Hosseini SH, Zadeh SG, Mohammadi-Ivatloo B, Vasquez JC, Guerrero JM (2018) An overview of power quality enhancement techniques applied to distributed generation in electrical distribution networks. Renew Sustain Energy Rev 93:201–214

    Article  Google Scholar 

  11. Prabha DR, Jayabarathi T (2016) Optimal location and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm. Ain Shams Eng J 7(2):683–694

    Article  Google Scholar 

  12. Prakash P, Khatod DK (2016) Optimal sizing and siting techniques for distributed generation in distribution systems: a review. Renew Sustain Energy Rev 57:111–130

    Article  Google Scholar 

  13. Ganesh S, Kanimozhi R (2018) Meta-heuristic technique for network reconfiguration in distribution system with photovoltaic and D-STATCOM. IET Gener Transm Distrib 12(20):4524–4535

    Article  Google Scholar 

  14. Zellagui M, Lasmari A, Settoul S, El-Sehiemy RA, El-Bayeh CZ, Chenni R (2021) Simultaneous allocation of photovoltaic DG and DSTATCOM for techno-economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms. Int Trans Electr Energy Syst 31(8):e12992

    Article  Google Scholar 

  15. Iqbal F, Khan MT, Siddiqui AS (2018) Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement. Alex Eng J 57(2):755–765

    Article  Google Scholar 

  16. Thangaraj Y, Kuppan R (2017) Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm. J Appl Res Technol 15(5):477–491

    Article  Google Scholar 

  17. Moazzami M, Gharehpetian GB, Shahinzadeh H, Hosseinian SH (2017) Optimal locating and sizing of DG and D-STATCOM using Modified Shuffled Frog Leaping Algorithm. In: 2017 2nd conference on swarm intelligence and evolutionary computation (CSIEC), pp 54–59. IEEE

  18. Isha G, Jagatheeswari P (2021) Optimal allocation of DSTATCOM and PV array in distribution system employing fuzzy-lightning search algorithm. Automatika 62(3–4):339–352

    Article  Google Scholar 

  19. Ali MH, Kamel S, Hassan MH, Tostado-Véliz M, Zawbaa HM (2022) An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks. Energy Rep 8:582–604

    Article  Google Scholar 

  20. Quadri IA, Bhowmick S (2020) A hybrid technique for simultaneous network reconfiguration and optimal location of distributed generation resources. Soft Comput 24(15):11315–11336

    Article  Google Scholar 

  21. Ahmed EM, Rakočević S, Ćalasan M, Ali ZM, Hasanien HM, Turky RA, Aleem SHA (2022) BONMIN solver-based coordination of distributed FACTS compensators and distributed generation units in modern distribution networks. Ain Shams Eng J 13(4):101664

    Article  Google Scholar 

  22. Srinivasan T, Wang X, Kim HJ, Ra IH (2021) Performance enhancement for microgrids under the demand uncertainties with the presence of multiple DGs through stochastic ranking algorithm. J Electr Eng Technol 16(1):223–238

    Article  Google Scholar 

  23. Liu J, Zeng P, Li Y, Xing H (2020) Coordinated optimal allocation of distributed generations in smart distribution grids considering active management and contingencies. J Electr Eng Technol 15(5):1969–1983

    Article  Google Scholar 

  24. Bujal NR, Sulaiman M, Abd Kadir AF, Khatib T, Eltawil N (2021) A Comparison between GSA and IGSA for optimal allocation and sizing of dg and impact to voltage stability margin in electrical distribution system. J Electr Eng Technol 16(6):2949–2966

    Article  Google Scholar 

  25. Ranjan KG, Prusty BR, Jena D (2021) Review of preprocessing methods for univariate volatile time-series in power system applications. Electric Power Syst Res 191:106885

    Article  Google Scholar 

  26. Prusty BR, Jena D (2016) Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach. CSEE J Power Energy Syst 2(2):71–78

    Article  Google Scholar 

  27. Hussien AG, Abualigah L, Abu Zitar R, Hashim FA, Amin M, Saber A, Almotairi KH, Gandomi AH (2022) Recent advances in harris hawks optimization: a comparative study and applications. Electronics 11(12):1919

    Article  Google Scholar 

  28. Jangir P, Heidari AA, Chen H (2021) Elitist non-dominated sorting Harris hawks optimization: framework and developments for multi-objective problems. Expert Syst Appl 186:115747

    Article  Google Scholar 

Download references

Acknowledgements

As the Author thanks the supervisor for his constant guidance and support throughout this research, he extends a deep sense of gratitude.

Funding

No funding was provided to the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Isha.

Ethics declarations

Conflicts of interest

A conflict of interest does not exist between the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Isha, G., Jagatheeswari, P. & Jasmine Gnana Malar, A. Elitist Harris Hawks Optimized Voltage Stability Enhancement in Radial Distribution System. J. Electr. Eng. Technol. 18, 2683–2693 (2023). https://doi.org/10.1007/s42835-023-01375-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-023-01375-5

Keywords

Navigation