Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers

P. Ghanooni; H. Habibi; A. Yazdani; H. Wang; S. MahmoudZadeh; A. Mahmoudi

doi:10.3390/electronics10212637

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Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers

Journal article

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Peer reviewed

Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers

P. Ghanooni, H. Habibi, A. Yazdani, H. Wang, S. MahmoudZadeh and A. Mahmoudi

Electronics, Vol.10(21), Article 2637

2021

DOI: https://doi.org/10.3390/electronics10212637

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Abstract

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Details

Title: Rapid detection of small faults and oscillations in synchronous generator systems using GMDH neural networks and high-gain observers
Authors/Creators: P. Ghanooni (Author/Creator) - Islamic Azad University, Mashhad
H. Habibi (Author/Creator) - University of Luxembourg
A. Yazdani (Author/Creator) - Murdoch University
H. Wang (Author/Creator) - Murdoch University
S. MahmoudZadeh (Author/Creator) - Deakin University
A. Mahmoudi (Author/Creator) - College of Science and Engineering
Publication Details: Electronics, Vol.10(21), Article 2637
Publisher: MDPI
Identifiers: 991005543458707891
Copyright: © 2021 by the authors
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.18 Power Systems & Electric Vehicles; 4.18.1055 Fault Detection
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied
ESI research areas: Engineering