Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 268-280

Original Article

Hybrid Learning Algorithm for Power System Stability: Fuzzy-Neural Control Approach

Amal Ibrahim Nasser 1 Wafaa Saeed Majeed 1 Layth Tawfeeq Al -Bahrani 1
1 Electrical Engineering Department, Mustansiriyah University, Baghdad, Iraq
History
  • Received: December 27, 2023
  • Revised: June 9, 2024
  • Accepted: June 20, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.25

Abstract

With the aim of enhancing the small signal stability of electric power systems, the present paper evaluated and compared some power system stabilizers (PSSs). The dilemma of small signal instability is avoided by equipping the generator’s automatic voltage regulator (AVR) with a backup controller known as a PSS. Conventional PSS operates with acceptable efficiency when designed to suit specific operating conditions, but there are limitations and drawbacks that arise when disturbances lead to fluctuation in system parameters. Strengthening the design methodology for PSS in the face of these limitations is achieved by adopting artificial intelligence. This research presents a fuzzy, neural system-based approach to the development of PSS. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to design the Fuzzy Neural Power Systems stabilizer (FNPSS) . ANFIS eliminates the disadvantages of using fuzzy logic and neural networks independently in PSS design. The single machine infinite bus (SMIB) power system was used as a case study to evaluate the effectiveness of the proposed methodology. Additionally, the study includes root locus scheme for loop of voltage regulation by utilizing proportional Integral controller, P-I controller, a widely used traditional linear design technique, for comparison. The simulation results confirm the effectiveness of the method, demonstrating the superiority of the ANFIS design method over other PSS designs. MATLAB, along with Control System Toolbox and SIMULINK, is used for simulation and design.

Keywords

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