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Implementation and Optimal Sizing of TCSC for the Solution of Reactive Power Planning Problem Using Quasi-Oppositional Salp Swarm Algorithm

Author

Listed:
  • Saurav Raj

    (Institute of Chemical Technology, Mumbai, India)

  • Sheila Mahapatra

    (Alliance University, India)

  • Chandan Kumar Shiva

    (S R Engineering College, Warangal, India)

  • Biplab Bhattacharyya

    (Indian Institute of Technology, Dhanbad, India)

Abstract

In this article, innovative algorithms named as salp swarm algorithm (SSA) and hybrid quasi-oppositional SSA (QOSSA) techniques have been proposed for finding the optimal coordination for the solution of reactive power planning (RPP). Quasi-oppositional based learning is a promising technique for improving convergence and is implemented with SSA as a new hybrid method for RPP. The proposed techniques are successfully implemented on standard test systems for deprecation of real power losses and overall cost of operation along with retention of bus voltages under acceptable limits. Optimal planning has been achieved by minimizing reactive power generation and transformer tap settings with optimal placement and sizing of TCSC. Identification of weakest branch in the power network is done for optimal TCSC placement and is tendered through line stability index method. Optimal TCSC placement renders a reduction in transmission loss by 8.56% using SSA and 8.82% by QOSSA in IEEE 14 bus system and 7.57% using SSA and 9.64% by QOSSA in IEEE 57 bus system with respect to base condition.

Suggested Citation

  • Saurav Raj & Sheila Mahapatra & Chandan Kumar Shiva & Biplab Bhattacharyya, 2021. "Implementation and Optimal Sizing of TCSC for the Solution of Reactive Power Planning Problem Using Quasi-Oppositional Salp Swarm Algorithm," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 10(2), pages 74-103, April.
    • Handle: RePEc:igg:jeoe00:v:10:y:2021:i:2:p:74-103
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    Citations

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    Cited by:

    1. Sheila Mahapatra & Nitin Malik & Saurav Raj & Mohan Krishna Srinivasan, 2022. "Constrained optimal power flow and optimal TCSC allocation using hybrid cuckoo search and ant lion optimizer," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 721-734, April.
    2. Raj, Saurav & Mahapatra, Sheila & Babu, Rohit & Verma, Sumit, 2023. "Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    3. Vikash Kumar Gupta & Rohit Babu, 2022. "Reactive power planning problem considering multiple type of FACTS in power systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1885-1894, August.
    4. Manjulata Badi & Sheila Mahapatra, 2023. "Optimal reactive power management through a hybrid BOA–GWO–PSO algorithm for alleviating congestion," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1437-1456, August.
    5. Swetha Shekarappa Gudadappanavar & Sheila Mahapatra, 2022. "Metaheuristic nature-based algorithm for optimal reactive power planning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1453-1466, June.

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