Manara - Qatar Research Repository
Browse

Stability improvement of the PSS-connected power system network with ensemble machine learning tool

Download (3.22 MB)
journal contribution
submitted on 2024-09-03, 05:34 and posted on 2024-09-03, 05:35 authored by M.S. Shahriar, M. Shafiullah, M.I.H. Pathan, Y.A. Sha’aban, Houssem R.E.H. Bouchekara, Makbul A.M. Ramli, M.M. Rahman

Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.

Other Information

Published in: Energy Reports
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.egyr.2022.08.225

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU