Transmission Expansion Planning for 133 Bus Tamil Nadu Test System Using Artificial Immune System Algorithm

  • S. PrakashEmail author
  • Joseph Henry
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)


Due to ever increase in load demand in developing countries similar to India, there is a necessity of proper power system equipment to serve the needs of consumers. In view of ever growth in load demand, it is required to enhance the power infra along with enhancement in load demand. Therefore there is a necessity of expansion of transmission lines to pump the power to the load centers and hence it requires proper planning of transmission expansion of power system. This paper describes Transmission Expansion Planning (TEP) which includes cost function with economic criteria as well as technical criteria. Economic criteria involves investment cost, the maintenance cost and the operation cost and also it involves cost rise owing to project delay, cost rise owing to inflation and cost rise owing to Right of Way. Transmission Expansion Planning (TEP) is developed with the help of Genetic Algorithms (GA), Bacterial Foraging Optimization Algorithm (BFOA) and Artificial Immune System Algorithm (AISA). The proposed approach is validated by performing 133 Bus TNS (Tamil Nadu System). TEP results are compared for proposed three methods.


Transmission expansion planning GA BFOA AISA Cost of line addition Cost escalation due to project delay Right of way and inflation 


  1. 1.
    Siefi, H., Sepasian, M.S.: Electric Power System Planning. Springer, Berlin, Heidelberg (2011)Google Scholar
  2. 2.
    Lee Willis, H., Rashid, M.H.: Understanding Electric Utilities and Deregulation. CRC Press, Taylor & Francis Group (2006)Google Scholar
  3. 3.
    Villasana, R., Garver, L.L., Salon, S.L.: Transmission network planning using linear programming. IEEE Trans. Power Apparat. Syst. PAS-104, 349–356, (1985)Google Scholar
  4. 4.
    Study on Project Schedule and cost overruns, KPMG (Initiative by Ministry of Statistics and Programme Implementation, Govt. of India), November 2012Google Scholar
  5. 5.
    A report on “Economic survey of India (2012–13)”, Union Budget and Economic survey, January 2012Google Scholar
  6. 6.
    Comptroller and Auditor General (COG) of India report on planning and implementation of transmission projects by Power Grid Corporation of India Limited (PGCIL) and Grid management by Power System Operation Corporation Limited (POSOCO), Govt. of India, March 2013Google Scholar
  7. 7.
    Schmitt, L.M.: Theory of genetic algorithms. Theor. Comput. Sci. (Elsevier) 259(1–2), 1–61, May 2001Google Scholar
  8. 8.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  9. 9.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization & control. IEEE Control Syst. Mag. 52–67 (2002)Google Scholar
  10. 10.
    Liu, Y., Passino, K.M.: Biomimicry of Social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J. Optim. Theory Appl. 115(3), 603–628 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Al-Enezi, J.R., Abbod, M.F., Alsharhan, S.: Artificial immune systems models, algorithms and applications. IJRRAS 3(2) (2010)Google Scholar
  12. 12.
    Nanda, S.J.: Artificial immune systems: principle, algorithms and applications, Thesis (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical & Electronics EngineeringVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyAvadi, ChennaiIndia

Personalised recommendations