Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network

Che, Qian; Wen, Hao; Li, Xinyu; Peng, Zhaoqiang; Chen, Keven P.

doi:10.1109/access.2019.2931040

Cited by 58 publications

(23 citation statements)

References 24 publications

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“…Another approach to increasing SNR over long lengths is the use of fiber with enhanced scattering. Recently fibers with weak fiber Bragg gratings (FBGs) [18], and continuous long-distance scattering enhancements [19,20], ultra-short FBGs [21,22], random FBGs [23][24][25], UV exposure of hydrogen loaded fiber [26] have been demonstrated in DAS systems. In this paper, we employ a continuously enhanced scattering fiber (OFS AcoustiSens®) in a direct detection Φ-OTDR system and show greater SNR and sensitivity to external vibrations.…”

Section: Introductionmentioning

confidence: 99%

Phase-Sensitive Optical Time Domain Reflectometry With Rayleigh Enhanced Optical Fiber

Lalam

Westbrook²,

et al. 2021

IEEE Access

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In this paper, a phase-sensitive optical time domain reflectometry (Φ-OTDR) based on Rayleigh enhanced optical fiber is proposed to improve the system signal-to-noise ratio (SNR). In the experiment, the vibration performance of the Rayleigh enhanced optical fiber is analyzed and compared with the standard single-mode telecom fiber. The results demonstrate that the measured SNRs of peaks in frequency spectra have been improved by 14 dB (at 1 kHz vibration frequency) at the end of the 2 km specialty sensing fiber. A wide range of vibration frequencies from 50 Hz to 15 kHz are demonstrated experimentally. The proposed method overcomes the inherent limitations of the conventional Φ-OTDR system and significantly enhances the vibration sensing performance.

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Section: Introductionmentioning

confidence: 99%

Phase-Sensitive Optical Time Domain Reflectometry With Rayleigh Enhanced Optical Fiber

Lalam

Westbrook²,

et al. 2021

IEEE Access

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“…In 2019, Q. Che et al [30] demonstrated a partial discharge (PD) detection in cross-linked polyethylene power cables using ANN-based Ø-OTDR system. The sensing fiber is composed of weak Bragg gratings (wFBGs) to enhance the Rayleigh backscattering signal.…”

Section: Rayleigh Based Distributed Fiber Sensorsmentioning

confidence: 99%

Recent development in artificial neural network based distributed fiber optic sensors

Lalam

2020

2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

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Distributed fiber optic sensors are promising technique for measuring strain, temperature and vibration over tens of kilometres by utilizing the backscattered Rayleigh, Raman and Brillouin signals.Recently, the use of an artificial neural network (ANN) has been adopted into the distributed fiber sensors for advanced data analytics, fast data processing time, high sensing accuracy and event classification. In this paper, the recent developments of ANN-based distributed fiber sensors and their operating principles are reviewed. Moreover, the performance of ANN is compared with the conventional signal processing algorithms. The future perspective view that can be extended further research development has also been discussed.

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“…In [13], stacked sparse auto-encoder (SSAE) is successfully applied to classify four defined PD severity states. In comparison with other deep learning algorithms, the model complexity and training difficulty of convolutional neural network (CNN) are relatively small owing to its parameter sharing and local connection, which makes CNN have superior performance in feature extraction of highdimensional data, especially suitable for image processing [14], [15]. Nowadays, CNN has been successfully applied in speech recognition, image recognition and other fields [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Partial Discharge Based on VMD-CWD Spectrum and Optimized CNN With Cross-Layer Feature Fusion

et al. 2020

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In order to improve the recognition accuracy of partial discharge (PD) by making full use of the time-frequency characteristics of PD signals and employing deep learning theory, a kind of PD pattern recognition method based on variational mode decompositon (VMD)-Choi-Williams distribution (CWD) spectrum and optimized convolutional neural network (CNN) with cross-layer feature fusion is proposed in this paper. Firstly, a PD signal is decomposed into several components by VMD algorithm, and the CWD analysis of the obtained components is carried out to obtain the VMD-CWD time-frequency spectrum. Secondly, the cross-layer feature fusion and optimization CNN (CFFO-CNN) is constructed by introducing cross-layer connection and optimization algorithm. Thirdly, the VMD-CWD is regarded as the input vector to train CFFO-CNN to learn and extract the intrinsic features of the spectrum. Finally, the trained network is used to recognize the PD types of the test samples. The proposed method is compared with traditional recognition methods such as BP neural network (BPNN) and support vector machine (SVM), as well as some commonly used deep learning algorithms. The experimental results indicate that the recognition performance of the proposed method is significantly better than that of existing recognition methods with accuracy up to 99.5%. It is proved that CFFO-CNN has superior feature extraction ability, which can extract the internal features of the VMD-CWD spectrum independently with higher recognition accuracy and wider application prospect. INDEX TERMS Variational mode decomposition (VMD), Choi-Williams distribution (CWD), feature fusion, convolutional neural network (CNN), partial discharge, pattern recognition.

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Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network

Cited by 58 publications

References 24 publications

Phase-Sensitive Optical Time Domain Reflectometry With Rayleigh Enhanced Optical Fiber

Phase-Sensitive Optical Time Domain Reflectometry With Rayleigh Enhanced Optical Fiber

Recent development in artificial neural network based distributed fiber optic sensors

Pattern Recognition of Partial Discharge Based on VMD-CWD Spectrum and Optimized CNN With Cross-Layer Feature Fusion

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