Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

Version 1 : Received: 8 October 2021 / Approved: 11 October 2021 / Online: 11 October 2021 (11:05:51 CEST)

A peer-reviewed article of this Preprint also exists.

Ghanooni, P.; Habibi, H.; Yazdani, A.; Wang, H.; MahmoudZadeh, S.; Mahmoudi, A. Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers. Electronics 2021, 10, 2637. Ghanooni, P.; Habibi, H.; Yazdani, A.; Wang, H.; MahmoudZadeh, S.; Mahmoudi, A. Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers. Electronics 2021, 10, 2637.

Journal reference: Electronics 2021, 10, 2637
DOI: 10.3390/electronics10212637

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 uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of 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.

Keywords

Group Method of Data Handling Neural Network; High-Gain Observer; L1-Norm Criterion; Output Residual Generation; Small Fault Detection; Synchronous Generator

Subject

ENGINEERING, Control & Systems Engineering

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