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Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

Author

Listed:
  • Yanjun Zhang
  • Tie Li
  • Guangyu Na
  • Guoqing Li
  • Yang Li

Abstract

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnlmpe:529724
    DOI: 10.1155/2015/529724
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    Cited by:

    1. Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
    2. Shitu Zhang & Zhixun Zhu & Yang Li, 2021. "A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges," Energies, MDPI, vol. 14(21), pages 1-13, November.
    3. 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.
    4. Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).

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