Application of Acoustic Signals for Rectifier Fault Detection in Brushless Synchronous Generator

• Autorzy: Rahnama Mehdi , Vahedi Abolfazl

Identyfikatory

DOI

10.24425/aoa.2019.128490

• Języki publikacji: EN

Abstrakty

EN

One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.

Słowa kluczowe

EN

acoustic emission; wavelet transform; K-Nearest Neighbours; fault detection; brushless generator

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2019
• Tom: Vol. 44, No. 2
• Strony: 267--276
• Opis fizyczny: Bibliogr. 23 poz., fot., rys., tab., wykr.

Twórcy

autor

Rahnama Mehdi

• Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology (IUST), Narmak 16846, Tehran, Iran

autor

Vahedi Abolfazl

• Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology (IUST), Narmak 16846, Tehran, Iran

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).