Classification and separation of partial discharge signals by means of their auto-correlation function evaluation

Contin, A.; Pastore, Stefano

doi:10.1109/tdei.2009.5361581

Cited by 73 publications

(41 citation statements)

References 63 publications

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“…This jeopardizes an accurate representation of individual PD source and hence reduces the accuracy on the PD source classification. Over the past decades, considerable efforts have been made to apply various artificial intelligence techniques such as artificial neural networks, genetic algorithms, knowledge-based systems, fractal models and support vector machines (SVMs) to automatic PD source classification [28,31,39,53,55,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The applicability of these techniques is largely dependent on the extracted features.…”

Section: Chemical-based Methods Electrical-based Methodsmentioning

confidence: 99%

“…TF sparsity map proposed in this chapter assumes that different PD sources are characterized by different shapes of PD pulses [52, 53,66,182]. An example of PD pulses generated by several PD source models (refer to Section 3.2.2) is shown in Figure 7.1.…”

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

“…Then, PD pulses were identified and extracted. In the literature, extracting PD pulses was achieved by using sequential sampling [53,66]. In the sequential sampling approach, a pre-defined threshold is required to trigger the data acquisition process.…”

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

“…In the sequential sampling approach, a pre-defined threshold is required to trigger the data acquisition process. If the acquired signals are higher than the threshold, they are treated as PD pulses and then stored for further processing [50,66]. This approach assumes that the time period for storing PD pulses is long enough to capture the complete shapes of pulses.…”

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

“…As such, when a transformer has been in operation for a relatively long period, the dielectric strength of its insulation system may start to deteriorate. This can eventually reduce the remaining life of the transformer [16][17][18].There are various diagnostic methods available based on chemical, electrical and mechanical characteristics of insulation for assessing the conditions of transformer insulation as shown in [28, 31, 39, 53,55,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The applicability of these techniques is largely Introduction 5 dependent on the extracted features.…”

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Advanced signal processing techniques for online power transformer insulation diagnosis

Chan¹

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Power transformer is one of the most important assets in an electric utility. However, a large number of existing power transformers worldwide have already approached or even exceeded their designed lifetimes. Any failure of a transformer can be disastrous. Therefore, the conditions of transformers need to be continuously monitored and evaluated. Since a transformer's condition is largely dependent on its insulation system, a number of diagnostic methods have been developed for assessing transformer insulation conditions over the past decades. Among these methods, partial discharge (PD) measurement is widely adopted due to its capability of providing continuously online monitoring and diagnosis of a transformer without disturbing its normal operation.PD is a rather complicated phenomenon and stochastic in nature. Properly performing online PD measurements of a transformer, effectively analysing the measured PD signals, and subsequently making an informed condition assessment on a transformer's insulation system are still challenging.This thesis is aimed at developing advanced signal processing techniques for online PD monitoring and diagnosis of power transformer insulation systems.PD signals acquired at substation environments are always coupled with extensive noise, which exhibits different distribution properties. Therefore, PD signal de-noising is an essential process for accurately extracting PD signals from the acquired noise-corrupted signals before further analysis. In this thesis, advanced signal processing techniques, such as wavelet transform (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), blind equalization (BE) and pre-whitening, have been investigated for removing discrete spectral interference (DSI) that exhibits sinusoidal behaviour at various frequencies. Mathematical morphology (MM) has also been investigated for suppressing white noise by adaptively selecting threshold values. To remove stochastic noise, fractal dimension and entropy analyses have been investigated. Based on these techniques, several adaptive PD signal de-noising methods have been proposed in this thesis for removing different types of noise. A number of case studies using PD signals acquired from both laboratory experiments and online PD measurements of field transformers are presented. These case studies demonstrate advantages of the proposed PD signal de-noising methods over conventional methods in PD signal de-noising.In this thesis, phase-resolved pulse sequence (PRPS) diagrams and kurtograms have also been proposed for consistent representations of PD patterns after the noise has been removed. Case studies have been provided to prove a PRPS diagram's capability for accurately and consistently ii representing PD patterns. This representation can minimize influences of different types of PD sensors and measurement systems on PD pattern construction. Results are presented to demonstrate that kurtograms can be used to represent PD patterns even in the presence of extensive w...

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Section: Chemical-based Methods Electrical-based Methodsmentioning

confidence: 99%

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

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confidence: 99%

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Advanced signal processing techniques for online power transformer insulation diagnosis

Chan¹

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Partial Discharge Characteristics and Mechanism of Medium Voltage Power Cable Defects under Different Voltage Stresses

Wang,

Chen,

et al. 2023

IEEJ Transactions Elec Engng

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Off‐line partial discharge (PD) diagnosis, which requires an additional power supply has been accepted as an essential step in ensuring the integrity of power cable systems during commissioning testing. Many types of energizing voltage sources (e.g., alternating current resonance, damped alternating current, very low frequency [VLF] sine, and VLF‐cosine rectangular) have been developed, which are used in practice. However, choosing the optimal energizing voltage waveforms for PD testing is still controversial because their capability to motivate PD activities is still unclear. Accordingly, this paper compares the PD ignition capacity of the aforementioned power sources on six well‐designed PD defects in real cable joints. The influence of test voltage frequencies and waveforms are verified. The results show that the PD inception voltage and PD magnitudes in the cable joint defects do not change significantly as a function of the sinusoidal voltage frequency and the VLF‐cosine rectangular voltage exhibits excellent PD ignition capability with a low PD inception voltage and large PD magnitude. Moreover, the dynamic behavior of space charge in a PD defect caused by a gap is analyzed in order to explain the obtained results. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Automatic Estimation of Multiplicity in Partial Discharge Sources Using Machine Learning Techniques

Kaimal

Kulkarni

2022

Lecture Notes in Electrical Engineering

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Classification and separation of partial discharge signals by means of their auto-correlation function evaluation

Cited by 73 publications

References 63 publications

Advanced signal processing techniques for online power transformer insulation diagnosis

Advanced signal processing techniques for online power transformer insulation diagnosis

Partial Discharge Characteristics and Mechanism of Medium Voltage Power Cable Defects under Different Voltage Stresses

Automatic Estimation of Multiplicity in Partial Discharge Sources Using Machine Learning Techniques

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