A Frequency-Domain Location Method for Defects in Cables Based on Power Spectral Density

Tang, Zhirong; Zhou, Kai; Meng, Pengfei; Li, Yumei

doi:10.1109/tim.2022.3189636

Cited by 25 publications

(2 citation statements)

References 36 publications

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“…[17], characteristic time-domain waveform recovery technology is proposed based on the frequency-domain reflection method to achieve calculation of the characteristic time-domain waveform recovery, and then the impedance change at the cable impedance mismatch point is determined to identify the fault type. Although this method can identify the impedance changes in short cables well because the test frequency is affected by the actual test equipment and cable length, effective test frequency band data account for a relatively small proportion of the total, resulting in the distortion of the recovery waveform in the feature time domain and making it difficult to judge its polarity [18]. The time-frequency-domain reflectometry (TFDR) method extracts the advantages of FDR and TDR in defect detection and has been widely used in defect detection in power cables [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform

Chen,

Yang,

Song

et al. 2024

Energies

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The time–frequency-domain reflection method (TFDR) based on the Wigner–Ville distribution (WVD) is confronted with the problem of cross-term interference in existing methods to locate power cable defects. Therefore, a new method of locating cable defects based on Gaussian narrow-band envelope signals and the S-transform is proposed in this paper. In this method, the wide-band cable transfer function is obtained by adjusting the parameters of the Gaussian narrow-band envelope signal because the Gaussian narrow-band envelope signal has a good frequency-adjusting ability and time–frequency characteristics. Then, the time–frequency of the cable signal is transformed by the generalized S-transform, and the time delay of the modular matrix of the transformation matrix is estimated by the generalized cross-correlation algorithm to complete the accurate detection of the cable defect’s location. Compared with traditional methods, the proposed method can adaptively adjust the analysis time width according to the frequency change and provide intuitive time–frequency characteristics without cross-term interference. Finally, the effectiveness and practicability of the proposed method are verified in MATLAB 2017_a by simulating a 40 m/10 kV medium-voltage power cable and submarine cable with a length of 32 km.

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Section: Introductionmentioning

confidence: 99%

A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform

Chen,

Yang,

Song

et al. 2024

Energies

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“…The FDR method uses a swept sinusoidal signal as the incident signal of the cable and determines whether a local defect occurs in the cable based on the distortion characteristics of the reflected signal spectrum of the cable. In [ 8 , 9 , 10 ], based on the FDR method, swept−frequency signals were used to diagnose cable defects, and the method was more accurate in localization because the signal contained more high−frequency energy, but it failed to identify different types of cable faults. Meanwhile, single domain analysis faces difficulties in diagnosing cable faults and defects, due to the resolution limitation and weak anti−interference capability of reflected signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

Wan

Yuan

et al. 2023

Sensors

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To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

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A defect location method for power cable based on Burg power spectral

Tang,

Zhou,

et al. 2024

Engineering Reports

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The frequency‐domain reflection (FDR) has been demonstrated to be a trustworthy technique in solving the defect location of power cable by field experiments. However, the location spectrum of the FDR requires manual window smoothing and can be disturbed by spurious peaks. Aiming at these shortcomings of FDR, a new method of cable defect location based on Burg power spectral (BPS) is introduced in this paper. The idea of this method is to use linear difference variance to fit the distribution of reflection coefficient spectrum and build an auto‐regressive (AR) model. The Burg algorithm is employed to estimate the coefficients model and calculate the power distribution of the AR model. Then, the cable defects will be located by BPS with high precision and resolution. In this method, the fast Fourier transform (FFT) with windowed function is replaced by an AR model without windowed function. This suppressed the impact of spurious peaks or spectrum leakage in FFT on the localization defects, and the localization resolution is higher. Finally, we validate the feasibility and effectiveness of BPS through experiments conducted on a 500 m laboratory cable and a 9.6 km submarine cable.

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A Frequency-Domain Location Method for Defects in Cables Based on Power Spectral Density

Cited by 25 publications

References 36 publications

A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform

A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

A defect location method for power cable based on Burg power spectral

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