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Analysis of Transient Stability through a Novel Algorithm with Optimization under Contingency Conditions

Author

Listed:
  • Kumar Reddy Cheepati

    (Electrical and Electronics Engineering, KSRM College of Engineering, Kadapa 516005, India)

  • Suresh Babu Daram

    (Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati 517102, India)

  • Ch. Rami Reddy

    (Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
    Electrical and Electronics Engineering, Joginpally B. R. Engineering College, Hyderabad 500075, India)

  • T. Mariprasanth

    (Electrical and Electronics Engineering, KSRM College of Engineering, Kadapa 516005, India)

  • Basem Alamri

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Mohammed Alqarni

    (Department of Electrical Engineering, College of Engineering, University of Business and Technology (UBT), Jeddah 21361, Saudi Arabia)

Abstract

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform.

Suggested Citation

  • Kumar Reddy Cheepati & Suresh Babu Daram & Ch. Rami Reddy & T. Mariprasanth & Basem Alamri & Mohammed Alqarni, 2024. "Analysis of Transient Stability through a Novel Algorithm with Optimization under Contingency Conditions," Energies, MDPI, vol. 17(17), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4404-:d:1470203
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    References listed on IDEAS

    as
    1. Yanjun Zhang & Tie Li & Guangyu Na & Guoqing Li & Yang Li, 2015. "Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, November.
    2. Lvjiang Yin & Meier Zhuang & Jing Jia & Huan Wang, 2020. "Energy Saving in Flow-Shop Scheduling Management: An Improved Multiobjective Model Based on Grey Wolf Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, October.
    3. Zhou, Binzhen & Huang, Xu & Lin, Chusen & Zhang, Hengming & Peng, Jiaxin & Nie, Zuli & Jin, Peng, 2024. "Experimental study of a WEC array-floating breakwater hybrid system in multiple-degree-of-freedom motion," Applied Energy, Elsevier, vol. 371(C).
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