Application of FWT (fast wavelet transform) for auto-detection system of partial discharge in power cables

Yasuda, Yoh; Matsuura, J.; Hara, Toshiaki; Chen, Min; Hirotsu, K.; Isojima, S.

doi:10.1049/cp:19990945

Cited by 4 publications

(6 citation statements)

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“…In order to speed up DWT processing, an algorithm for Eqs. (3) to (5) was written in C++ as a PC application. A typical PC processed one PD/noise waveform in about 0.01 second.…”

Section: Dwt Analysis Of Pd Waveformsmentioning

confidence: 99%

“…The authors have explored data processing (preprocessing) at the stage when the observational data are input to the neural network. The results obtained so far indicate that wavelet spectrum patterns, rather than just waveform time series or FFT spectra, should be used as input data in order to obtain correct detection [5]. The reason is that pulse attenuation and other temporal information is lost in the FFT analysis, while the wavelet transform, which produces data in the time-frequency domain, allows the extraction of intrinsic waveform features.…”

Section: Introductionmentioning

confidence: 99%

“…(3) and(5), the DWT requires pure matrix operations; in particular, when using the Haar basis, Noise waveform and its wavelet transform results. Expansion of wavelet spectrum in time-frequency space (data length 16).…”

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confidence: 99%

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Auto-detection of partial discharges in power cables by discrete wavelet transform

Yasuda

Hara

Urano³

et al. 2005

Elect. Eng. Jpn.

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SUMMARYOne of the serious problems that can occur in power XLPE cables is destruction of insulator. The best and conventional way to prevent this is ascertaining partial corona discharges occurring at small voids in organic insulators. However, there are some difficulties in detecting those partial discharges due to the existence of external noise in detected data, whose patterns are hardly identified at a glance. For this reason, there have been a number of researches into detecting partial discharges by employing a neural network (NN) system, which is widely known as a system for pattern recognition.We have been developing an NN system for auto-detection of partial discharges, and have input numerical data of the waveform itself and obtained appropriate performance. In this paper, we employed the discrete wavelet transform (DWT) to acquire more detailed transformed data in order to use them in the NN system. Employing the DWT, we were able to express the waveform data in timefrequency space, and achieved effective detection of partial discharges by the NN system. We present herein the results using DWT analysis for partial discharges and noise signals which we obtained. Moreover, we present results out of the NN system which dealt with those transformed data.

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“…In order to speed up DWT processing, an algorithm for Eqs. (3) to (5) was written in C++ as a PC application. A typical PC processed one PD/noise waveform in about 0.01 second.…”

Section: Dwt Analysis Of Pd Waveformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Auto-detection of partial discharges in power cables by discrete wavelet transform

Yasuda

Hara

Urano³

et al. 2005

Elect. Eng. Jpn.

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“…26 shows ∆(∆V) of corona pulses remain well within 0.1 Volts absolute and ∆(∆t) of the majority pulses remain in less than 1 ms absolute. Corona pulses clutter close to the origin in a round shape which is quite similar to the previous ΔVn-1 against ΔVn plot…”

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confidence: 87%

“…analysis can no longer be used as a single tool for processing the PD data. The potential application of Wavelet Transform to PD detection has been discussed in some publications [23,24,25,26]. However, the focus of these publications is on the use of DWT and not by the CWT.…”

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confidence: 99%

Partial discharge identification by using signal processing techniques

Chia¹

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Partial Discharge (PD) detection after denoising, characterization and identification are the three main signal processing requirements of PD analysis. Voluminous digital PD data are nowadays readily available with constant improvements in PD measurement techniques. Power Engineers may be able to detect prominent PDs using oscilloscope and existing couplers. But identification of the types of developing and random occurring PD is a real challenge to any practicing engineer. In this thesis, details on using wavelet transform in the form of either continuous wavelet transform or discrete wavelet transform with two methods to denoise, identify the location of PD and retrieve PD wave shape without magnitude distortion are presented. To identify the type of PD, some experimental studies and about six existing and developed signal processing methods are carried out. Laboratory experimental study provided reproducible data with enough number of sampled points on three types of pure PD and one multisources PD. The signal processing is done on PD random occurrence in 20 ms with multicycles, and is known as group PD analysis. In single PD analysis, the individual shape of PD is extracted and analysed. Group analysis includes the study of following distributions of PD: Φ-q, Φ-n and q-n, and the corresponding statistical operators Sk, Ku and CC, weibull parameters using qcumulative n and Φ-cumulative n distributions, cluster analysis using (Δt-ΔV), (ΔV n-ΔV n-1), (Δt n-Δt n-1) and (Δ(ΔV)-Δ(Δt)) distributions and Wavelet Transform

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