Fractional-Order Deterministic Learning for Fast and Robust Detection of Sub-Synchronous Oscillations in Wind Power Systems

Omar Kahouli (), Lilia El Amraoui, Mohamed Ayari () and Omar Naifar ()
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Omar Kahouli: Department of Electronics Engineering, Applied College, University of Ha’il, Ha’il 81451, Saudi Arabia
Lilia El Amraoui: Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Mohamed Ayari: Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Arar 91431, Saudi Arabia
Omar Naifar: Control and Energy Management Laboratory, National School of Engineering, University of Sfax, Sfax 3038, Tunisia

Mathematics, 2025, vol. 13, issue 22, 1-21

Abstract: This work explores the issue of identifying sub-synchronous oscillations (SSOs). Regular detection techniques face issues with response timings to variations in viewpoint and adaptability to variations in conditions of the system but our proposed method overcomes them. We have actually come up with a new framework called Tempered Fractional Deterministic Learning (TF-DL) that successfully combines tempered fractional calculus with deterministic learning theory. This method makes a memory-based learner that works best for oscillatory dynamics. This lets SSO identification happen faster through a recursive structure that can run in real time. Theoretical analysis validates exponential convergence in the context of persistent excitation. Simulations show that detection time is 62.7% shorter than gradient descent, with better convergence and better parameters.

Keywords: sub-synchronous oscillations; wind power systems; tempered fractional calculus; deterministic learning; oscillation detection; power system stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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