Stator current signal crossing for fault diagnosis of self-excited induction generators

Fares Belynda; Radia Abdelli; Ahcene Bouzida

doi:10.14311/AP.2023.63.0293

Authors

Fares Belynda Laboratoire de Technologie Industrielle et de l’Information LTII, Faculté de Technologie, Université de Bejaia, 06000 Bejaia, Algeria
Radia Abdelli Laboratoire de Technologie Industrielle et de l’Information LTII, Faculté de Technologie, Université de Bejaia, 06000 Bejaia, Algeria
Ahcene Bouzida Faculty of Sciences and Applied Sciences, University Ali Mohand Oulhadj, Bouira 10000, Algeria

DOI:

https://doi.org/10.14311/AP.2023.63.0293

Keywords:

Self-Excited Induction Generator, wind energy, broken bars, inter-turn fault, dynamic eccentricity, FEM, diagnostic, Stator Currents Signal Crossing

Abstract

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.

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