Application of time-frequency domain reflectometry for measuring load impedance

Kwak, Ki Seok; Choe, Tok Son; Park, Jin Bae; Yoon, Tae Sung

doi:10.1587/elex.5.107

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…the reference signal and the reflected signal cannot be obtained [3][4][5] and the method is significantly affected by noise. It also has limitations in terms of the resolution and accuracy of fault detection due to the rise/fall time [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, TFDR has been applied in various applications, such as coaxial cable [6][7][8][9], high-voltage power cable [10,11] and electrical power cable in nuclear power plants and ship power systems [12][13][14]. In [3][4][5], even though conventional TFDR (CTFDR) has been used to evaluate the load impedance, CTFDR has some weak points, such as long computation times, hard implementation and cross term interference because of the nonlinearity of Wigner-Ville distribution (WVD) [15]. Although WVD is a time-frequency analysis method, the computational complexity results in slow processing speed and the cross term leads to misinterpretation because the information of the cross term does not exist in the original signal [16].…”

Section: Introductionmentioning

confidence: 99%

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Measurement of load impedance in power cables using wavelet-transform-based time–frequency domain reflectometry

Lee¹,

Park²,

Choi³

2013

Meas. Sci. Technol.

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In this paper, wavelet-transform-based time–frequency domain reflectometry (WTFDR) is proposed for load impedance measurement. In order to measure the load impedance, the energy of the measured signal in the time–frequency domain, the phase difference between the reference signal and the reflected signal, the characteristic impedance, and the attenuation factor of the measured cable must all be known. Since the complex wavelet transform is composed of real and imaginary parts, the phase difference is easily obtained using the ratio of the real coefficient to the imaginary coefficient. In addition, the wavelet energy denotes the sum of the square of the modulus of the wavelet transform and describes the energy of the measured signal in the time and frequency domains. To accurately determine the characteristic impedance and attenuation factors, the power cable should be estimated as a coaxial cable. Using WTFDR with the complex mother wavelet and the estimated power cable, the load impedance can be obtained simply and accurately. Finally, real experiments for the evaluation of various load impedances are carried out to confirm the effectiveness and accuracy of the proposed method compared to the conventional time–frequency domain reflectometry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of load impedance in power cables using wavelet-transform-based time–frequency domain reflectometry

Lee¹,

Park²,

Choi³

2013

Meas. Sci. Technol.

Self Cite

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“…During the last decade, many techniques have been proposed to locate defects in cables [3][4][5]. Among them, reflectometry method is the most important one and still widely used today.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Faulty Cable Network Using Time-Domain Reflectometry

Zhang¹,

Zhang²,

Liu³

2013

PIER

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Abstract-Based on Time-Domain Reflectometry (TDR) technique, a novel method which could locate faults on the coaxial cable distribution network by using Support Vector Machine (SVM) is proposed in this paper. This approach allows the faulty network to be reconstructed by estimating the lengths of branches. A State-transition Matrix model is employed to simulate the TDR response at any port and evaluate the transfer function between two points. SVM is used to solve the inversion problem through training datasets created by the State-transition matrix model. Compared to the existing reflectometry methods, our proposed method can tackle multiple faults in the complex cable networks. Numerical and experimental results pointing out the performance of the SVM model in locating faults are reported.

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