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RESEARCH ARTICLE

Neural network-based distributed fault estimation for multi-Area interconnected power systems with actuator faults and network constraints

Nezar M. Alyazidi1,2 ,  Mutaz M. Hamdan3†* ,  Ahmed Eltayeb2† ,  Mahmoud S. AbouOmar2† ,  Gamil Ahmed2† ,  Maged S. Al-Quraishi2†
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1 Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
2 Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
3 Robotics and AI Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan
†These authors contributed equally to this work.
IJOCTA, 026140050 https://doi.org/10.36922/IJOCTA026140050
Received: 31 March 2026 | Revised: 19 March 2026 | Accepted: 22 May 2026 | Published online: 9 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

With the growing reliance of multi-area interconnected power systems on communication networks for large-scale data transmission, sensing, and control, network constraints can impair transient performance. This work develops a unified distributed fault-estimation framework based on a radial basis function neural network (RBF-NN) for multi-area interconnected power systems, addressing both communication constraints and actuator faults. We construct an augmented system by integrating the fault dynamics into the system model, and a distributed observer is developed to simultaneously estimate the system states and fault dynamics. The unknown fault effect is approximated by a radial basis function neural network (RBF-NN). A projection-based adaptation law ensures bounded parameter estimation under modeling uncertainties. State and fault estimates are updated using quantized measurements transmitted over networked channels, maintaining accuracy despite time-varying delays and disturbances. Sufficient stability and performance conditions are established by Lyapunov analysis and LMI conditions, guaranteeing exponential stability of the observer error dynamics and prescribed performance bounds under bounded time-varying delays and quantization effects. A three-area case study is presented, and precise state and fault reconstruction are shown for nominal, delayed, faulted, and worst-case scenarios. The proposed scheme is compared with a quantized H∞ estimator under actuator faults, time-varying delays, and quantization. The peak deviations of |Δω|, |ΔPtie|, and |ΔPmech| are reduced approximately 33%, 50%, and 27%, respectively, while maintaining bounded system states and neural weights.

Keywords
Fault estimator
Distributed observer
Networked control systems
State estimator design
Radial basis function neural network
Power system
Funding
None.
Conflict of interest
The authors have no relevant financial or nonfinancial interests to disclose.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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