Partial Discharge Localization Inside Transformer Windings via Fiber-Optic Acoustic Sensor Array

Gao, Chaofei; Yu, Lei; Xu, Yue; Wang, Wei; Wang, Shijie; Wang, Peng

doi:10.1109/tpwrd.2018.2880230

Cited by 69 publications

(33 citation statements)

References 25 publications

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“…The FOAS structure using polymer film has better performance due to the large radius of the polymer film, thermal stability up to 150 o C makes it suitable for the harsh environment in the transformer. The FP interferometer is limited to use only TLS for PD detection [41]- [43]. This FOAS sensor is competent to utilize both TLS and BBS as its light sources.…”

Section: Figure 8 Fft Response Of Foas Using Tls With 25 CM Apart Bmentioning

confidence: 99%

Fiber Optic Acoustic Sensor Based on SMS Structure With Thin Polymer Diaphragm for Partial Discharge Detection

et al. 2020

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This paper proposes a fiber optic acoustic sensor (FOAS) based on a single-mode fibermultimode fiber-single-mode fiber (SMS) structure attached to a thin polymer film used as a diaphragm. The diaphragm was specially developed to enhance the sensitivity towards the acoustic pressure-wave resulted from the partial discharge (PD) events. The sensitivity and signal-to-noise ratio (SNR) characterizations of the FOAS without and with a thin polymer film were performed. Both time-resolved and phase-resolved partial discharge (PRPD) patterns measurements were carried out in air and oil media. The experiment was conducted with three-electrodes using FOAS in conjunction with the conventional PD measurement as per IEC 60270 standard. The sensor achieved a sensitivity up to-31.21 dBm and-30.8 (0 dBm is defined as 1V/µBar) using broadband and tunable light source, respectively. The discharge characteristics pattern of FOAS was verified with IEC 60270 standard, and the patterns of FOAS resembled IEC 60270 standard. The proposed FOAS was capable for detecting the PD using both broadband and tunable laser lights. The use of the thin polymer film had a significant impact on the acoustic sensitivity. With the simple, low-cost design structure and free from electromagnetic interference, FOAS is found to be suitable as an in-situ sensor for detecting the acoustic signals of partial discharge and can be utilized inside the transformer.

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Section: Figure 8 Fft Response Of Foas Using Tls With 25 CM Apart Bmentioning

confidence: 99%

Fiber Optic Acoustic Sensor Based on SMS Structure With Thin Polymer Diaphragm for Partial Discharge Detection

et al. 2020

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“…Most power grid failures are caused by insulation defects [1]- [3]. Partial discharge (PD) usually occurs when electrical equipment is defective in insulation [4]. As the primary equipment in the power grid, the insulation status of the transformer directly affects the safe operation of the power grid [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…In [26], an improved propagation route search (IPRS) algorithm has been proposed, which can obtain the fastest propagation route of the PD signal. In [27], the acoustic wave propagation inside the transformer has been discussed, and a fiber-optic acoustic sensor array has been designed for PD localization in transformer winding. The paper [28] has studied how to locate the PD in the transformer by the macro fiber composite sensors (MFCS).…”

Section: Introductionmentioning

confidence: 99%

A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering

Wang

Yin

et al. 2021

IEEE Access

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“…The security and reliability of the power system are closely related to the stable operation of transformers [1]. Therefore, status monitoring and fault diagnosis on transformers become research hotspots in the industry [2][3][4][5]. [6][7][8] indicate that loosing and deformation faults of transformer winding are caused by multiple impacts of short-circuit current, these factors can affect the insulation performance of winding, even cause the transformer to burn down, power failure and other major power accidents.…”

Section: Introductionmentioning

confidence: 99%

State diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters

Cao

Lin

et al. 2020

IET Electric Power Applications

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For diagnosing deformation fault and latent loosing status of winding, this study proposed a state diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters. The wavelet energy-matrix norm transformation method and reactance identification method were proposed to extract vibration and reactance parameters. The multi-information collection system of transformer winding deformation state was designed. Aiming at the S-11-M-500/35 transformer, short circuit current impact experiment was made and three deformation fault windings were designed. Status database of multi-information for normal, loosing, deformation and specific fault types of winding were established. State diagnosis experiment on normal winding, loosing winding after short circuit current impact and deformation fault winding was made. Per unit value-space vector transformation method was proposed to fuse vibration and reactance parameters, fusing eigenvector was established, and the winding deformation state was diagnosed by extracting fusing feature. On the basis of the above research, regarding deformation fault state winding, fault types were diagnosed based on the relevance vector machine. Results indicated that latent loosing, deformation fault and three deformation fault types of winding can be diagnosed accurately. Fusing diagnosis method proposed in this study was superior to the single vibration or reactance parameter diagnosis method.

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Partial Discharge Localization Inside Transformer Windings via Fiber-Optic Acoustic Sensor Array

Cited by 69 publications

References 25 publications

Fiber Optic Acoustic Sensor Based on SMS Structure With Thin Polymer Diaphragm for Partial Discharge Detection

Fiber Optic Acoustic Sensor Based on SMS Structure With Thin Polymer Diaphragm for Partial Discharge Detection

A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering

State diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters

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