Parameters of Guaranteed Self-Excitation of an Induction Generator for Autonomous Electric Power Sources

Chenchevoi, Volodymyr; Zachepa, Iurii; Chencheva, Olga; Yatsiuk, Rostyslav

doi:10.1109/paep49887.2020.9240851

Cited by 12 publications

(1 citation statement)

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“…To function as a generator, the squirrel cage induction machine must be driven at a speed greater than its no-load synchronous speed ω 1 = f 1 /p 1 and supplied with reactive power to generate and maintain its magnetic field. This reactive power can be generated by synchronous capacitors (Figure 1) [20]. The voltage V s and frequency f 1 of the SEIG are primarily determined by machine parameters, capacitors C, and speed ω.…”

Section: Squirrel Cage Induction Generator Self-excitationmentioning

confidence: 99%

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

Belynda,

Abdelli,

Bouzida

2023

Acta Polytech

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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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Section: Squirrel Cage Induction Generator Self-excitationmentioning

confidence: 99%