Fast Detection Method on Water Tree Aging of MV Cable Based on Nonsinusoidal Response Measurement

A MV power cable will exhibit nonlinear impedance when dendritic defects appear. To solve the long time-consuming measurement of pure sinusoidal excitation and realize rapid and accurate evaluation of insulation conditions, a measurement and evaluation method based on nonsinusoidal excitation is proposed in this paper. Firstly, a nonsinusoidal excitation test system is established, which can obtain the high voltage dielectric response in the frequency range of 0.01 Hz–1 kHz. For parameterizing dielectric response characteristics of MV XLPE insulation under nonsinusoidal excitation, a Hammerstein-Wiener model is proposed to realize system parameter identification under non sinusoidal excitation with different aging degrees of water tree. The time-domain current response waveform based on the H-W model is more accurate than the traditional linear model, and the resulting fitting degree of the frequency-domain dielectric loss factor curve between the calculated H-W model and the measured curve was higher than 95%. The results show that the rotation and distortion of the u-i hysteresis ellipse under nonsinusoidal excitation can be used to diagnose the insulation defects of XLPE MV cables. The response model and its detection method proposed in this paper can improve the efficiency and accuracy of the field tests of cable insulation.

[1]  Yong-June Shin,et al.  Detection and Assessment of I&C Cable Faults Using Time–Frequency R-CNN-Based Reflectometry , 2021, IEEE Transactions on Industrial Electronics.

[2]  A. K. Das,et al.  Estimation of Moisture Content in XLPE Insulation in Medium Voltage Cable by Frequency Domain Spectroscopy , 2020, IEEE Transactions on Dielectrics and Electrical Insulation.

[3]  J. Hao,et al.  Ageing state identification and analysis of AC 500 kV XLPE submarine cable based on high‐voltage frequency dielectric response , 2020, IET Science, Measurement & Technology.

[4]  B. T. Phung,et al.  Dielectric response measurement on service-aged XLPE cables: From very low frequency to power frequency , 2020, IEEE Electrical Insulation Magazine.

[5]  Jian Li,et al.  Morphological, Structural, and Dielectric Properties of Thermally Aged AC 500 kV XLPE Submarine Cable Insulation Material and Its Deterioration Condition Assessment , 2019, IEEE Access.

[6]  Maurizio Spadavecchia,et al.  Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry , 2019, IEEE Transactions on Instrumentation and Measurement.

[7]  Bin Zhang,et al.  Synchronous Online Diagnosis of Multiple Cable Intermittent Faults Based on Chaotic Spread Spectrum Sequence , 2019, IEEE Transactions on Industrial Electronics.

[8]  S. Dalai,et al.  Use of chirp excitations for frequency domain spectroscopy measurement of oil-paper insulation , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[9]  Y. Liu,et al.  Insulation performance evaluation of HV AC/DC XLPE cables by 0.1 Hz tan δ test on circumferentially peeled samples , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[10]  Fuchang Lin,et al.  Condition assessment of XLPE insulated cables based on polarization/depolarization current method , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[11]  Yoshimichi Ohki,et al.  Numerical simulation on molecular displacement and DC breakdown of LDPE , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[12]  Fabrice M. Guerout,et al.  Development of indentation techniques in support of cable condition monitoring programs , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[13]  Ayeley P. Tchangani,et al.  Distributed Sensor Fusion for Wire Fault Location Using Sensor Clustering Strategy , 2015, Int. J. Distributed Sens. Networks.

[14]  R. N. Hampton,et al.  Interpretation of dielectric loss data on service aged polyethylene based power cable systems using VLF test methods , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[15]  Feng Ding,et al.  Identification methods for Hammerstein nonlinear systems , 2011, Digit. Signal Process..

[16]  Roger A. Dougal,et al.  Health Monitoring of Power Cable via Joint Time-Frequency Domain Reflectometry , 2011, IEEE Transactions on Instrumentation and Measurement.

[17]  D. Maksimovic,et al.  System identification of power converters with digital control through cross-correlation methods , 2005, IEEE Transactions on Power Electronics.

[18]  Giovanni Sansavini,et al.  Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction , 2018, IEEE Transactions on Industrial Electronics.

[19]  J. Densley,et al.  Ageing mechanisms and diagnostics for power cables - an overview , 2001 .