Skip to main content
SpringerLink
Log in

Application of SOS Algorithm for Solution of ORPD Problem

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

In this current research initiative, symbiotic organism search (mostly known as SOS) evolutionary computational methodology is used to find out solution for reactive power dispatch (ORPD) issues in power system. SOS algorithm is influenced by interaction among organisms of the ecosystem and independent from algorithm specific control parameters. In ORPD problem, multiple optimization objectives such as transmission line’s real power loss reduction, voltage stability-index improvement and reduction of total voltage deviation are undertaken. The control variables, e.g., generator and slack bus voltages, transformer tap setting and shunt capacitance are set to its optimal values in order to find the best possible solution of undertaken objective functions. A trial of adopted SOS methodology is performed on “IEEE-30 bus, -57 bus and -118 bus standard test cases” in power systems. Acquired sophisticated findings help to infer that the recommended SOS method is competent enough in comparison with other cited methods in the progressive literatures.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

\({\text{BF}}_{(1)}\),\({\text{BF}}_{(2)}\) :

Benefit factors

\(B_{(ij)}\),\(G_{(ij)}\) :

Transfer susceptance and conductance, respectively

\(F_{(ji)}\) :

\(\left( {i,j} \right){\text{th}}\) Components of bus admittance matrix (\(Y_{{({\text{Bus}})}}\))

\(L_{(k)}\) :

Voltage stability index of the \(k{\text{th}}\) node

\(M\_V\) :

Relationship between vectors \(X_{(i)}\) and \(X_{(j)}\)

\(N_{{({\text{B}})}}\),\(N_{{({\text{C}})}}\),\(N_{{({\text{G}})}}\),\(N_{{({\text{l}})}}\),\(N_{{({\text{L}})}}\),\(N_{{({\text{T}})}}\) :

Number of buses, shunt capacitors, generator buses, transmission lines, load buses and transformer branches, respectively

\(P_{{({\text{D}})}}\),\(P_{{({\text{G}})}}\),\({\kern 1pt} P_{{({\text{Loss}})}}\) :

Active power demand, generation and transmission line losses, respectively

\(P\_V\) :

Relationship between parasite vectors

\(Q_{{({\text{C}})}}\),\(Q_{{({\text{D}})}}\),\(Q_{{({\text{G}})}}\) :

Reactive power compensation, demand and generation, respectively

\(r_{(m)}\) :

Random number specified within [0, 1]

\(S_{(l)}\) :

Complex power of branch l

\(T_{(i)}\) :

Transformer tap (integer) installed in the ith line

\(V_{{({\text{G}})}}\) :

Generator voltage (continuous)

\(V_{(i)}\), \(V_{(j)}\), \(V_{(i)}^{{{\text{ref}}}}\),\(V_{{({\text{L}})}}\) :

Voltages at the i and j buses, referred (i.e., 1.0 pu) and load voltages, respectively

\(X_{(i)}\), \(X_{(j)}\),\(X_{{({\text{best}})}}\) :

iTh and jth organism in the ecosystem and highest degree of adaptation, respectively

\(\left( {X_{{{\text{best}}}} - X_{j} } \right)\) :

Benefit of \(X_{(j)}\)

\(X_{{(i\,{\text{new}})}}\), \(X_{{(j\,{\text{new}})}}\) :

Mutuality effort given by the organisms to enhanced adaptability at the buses i and j, respectively

\(Y_{(jj)}\), \(Y_{(ji)}\) :

Self and mutual admittances, respectively

\(\delta_{(i)}\), \(\delta_{(j)}\) :

Phase angle at i and j buses, respectively

\(\alpha_{(ij)}\) :

Phase angle of the term \(F_{(ij)}\)

References

  1. M. A. Abido, Multi objective optimal VAR dispatch using strength pareto evolutionary algorithm, in IEEE Congress on Evolution Computing Vancouver, BC, Canada (2006), pp. 730–36

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

    Google Scholar 

  3. A.A. Abou El Ela, M.A. Abido, S.R. Spea, Differential evolution algorithm for optimal reactive power dispatch. Electr. Power Syst. Res. 81(2), 458–464 (2011)

    Article  Google Scholar 

  4. U.H.H. Tehzeeb, R. Zafar, S.A. Mohsin, O. Latee, Reduction in power transmission loss using fully informed particle swarm optimization. Electr. Power Energy Syst. 43, 364–368 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. B. Mandal, P.K. Roy, Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Electr. Power Energy Syst. 53, 123–134 (2013)

    Article  Google Scholar 

  7. B. Shaw, V. Mukherjee, S.P. Ghoshal, Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Electr. Power Energy Syst. 55, 29–40 (2014)

    Article  Google Scholar 

  8. A. Ghasemi, K. Valipour, A. Tohidi, Multi objective optimal reactive power dispatch using a new multi objective strategy. Electr. Power Energy Syst. 57, 318–334 (2014)

    Article  Google Scholar 

  9. M. Ghasemia, S. Ghavidela, M.M. Ghanbarianb, A. Habibi, A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables. Appl. Soft Comput. 22, 126–140 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. A. Rajan, T. Malakar, Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Electr. Power Energy Syst. 66, 9–24 (2015)

    Article  Google Scholar 

  12. M.H. Qais, H.M. Hasanien, S. Alghuwainem, Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy 195, 117040 (2020)

    Article  Google Scholar 

  13. M.A. Shaheen, H.M. Hasanien, S.F. Mekhamer, H.E. Talaat, Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm. IEEE Access 7, 109289–109300 (2019)

    Article  Google Scholar 

  14. M.A. Shaheen, H.M. Hasanien, A. Alkuhayli, A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Eng. J. 12, 621–630 (2020)

    Article  Google Scholar 

  15. L.S. Titare, P. Singh, L.D. Arya, S.C. Choube, Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage stability. Int. J. Electr. Power Energy Syst. 63, 588–599 (2014)

    Article  Google Scholar 

  16. L.D. Arya, P. Singh, L.S. Titare, Anticipatory reactive power reserve maximization using differential evolution. Int. J. Electr. Power Energy Syst. 35(1), 66–73 (2012)

    Article  Google Scholar 

  17. M.Y. Cheng, D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Strut. 139, 98–112 (2014)

    Article  Google Scholar 

  18. R.P. Singh, V. Mukherjee, S.P. Ghoshal, Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl. Soft Comput. 29, 298–309 (2015)

    Article  Google Scholar 

  19. E. Mezura-Montesa, C.C.A. Coello, Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011)

    Article  Google Scholar 

  20. R. Mallipeddi, S. Jeyadevi, P.N. Suganthan, S. Baskar, Efficient constraint handling for optimal reactive power dispatch problems. Swarm Evol. Comput. 5, 28–36 (2012)

    Article  Google Scholar 

  21. S. Verma, S. Saha, V.A. Mukherjee, novel symbiotic organisms search algorithm for congestion management in deregulated environment. Exp. Theor. Artif. Intell. 29, 1–21 (2015)

    Google Scholar 

Download references

Funding

No fund has been accepted from any funding agency for this paper.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal, India

    Dharmbir Prasad & Rudra Pratap Singh

  2. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

    V. Mukherjee

Authors
  1. Dharmbir Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rudra Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dharmbir Prasad.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest with any other individual or organization.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prasad, D., Mukherjee, V. & Singh, R.P. Application of SOS Algorithm for Solution of ORPD Problem. J. Inst. Eng. India Ser. B 103, 747–766 (2022). https://doi.org/10.1007/s40031-021-00700-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-021-00700-8

Keywords

Search

Navigation