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Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS

Author

Listed:
  • Susanta Dutta

    (Department of Electrical Engineering, Dr. B.C. Roy Engineering College, Durgapur, India)

  • Provas Kumar Roy

    (Department of Electrical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India)

  • Debashis Nandi

    (Department of Information Technology, National Institute of Technology Durgapur, Durgapur, India)

Abstract

In this paper, quasi-oppositional teaching-learning based optimization (QOTLBO) is introduced and successfully applied for solving an optimal power flow (OPF) problem in power system incorporating flexible AC transmission systems (FACTS). The main drawback of the original teaching-learning based optimization (TLBO) is that it gives a local optimal solution rather than the near global optimal one in limited iteration cycles. In this paper, opposition based learning (OBL) concept is introduced to improve the convergence speed and simulation results of TLBO. The effectiveness of the proposed method implemented with MATLAB and tested on modified IEEE 30-bus system in four different cases. The simulation results show the effectiveness and accuracy of the proposed QOTLBO algorithm over other methods like conventional BBO and hybrid biogeography-based optimization (HDE-BBO). This method gives better solution quality in finding the optimal parameter settings for FACTS devices to solve OPF problems. The simulation study also shows that using FACTS devices, it is possible to improve the quality of the electric power supply thereby providing an economically attractive solution to power system problems.

Suggested Citation

  • Susanta Dutta & Provas Kumar Roy & Debashis Nandi, 2016. "Quasi Oppositional Teaching-Learning based Optimization for Optimal Power Flow Incorporating FACTS," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 5(2), pages 64-84, April.
    • Handle: RePEc:igg:jeoe00:v:5:y:2016:i:2:p:64-84
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    Cited by:

    1. Li, Jia & Liu, Feng & Li, Zuyi & Mei, Shengwei & He, Guangyu, 2018. "Impacts and benefits of UPFC to wind power integration in unit commitment," Renewable Energy, Elsevier, vol. 116(PA), pages 570-583.

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