Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals

Ardila-Rey, Jorge Alfredo; Schurch, Roger; Poblete, Nicolas Medina; Govindarajan, Suganya; Munoz, Osvaldo; Castro, Bruno Albuquerque de

doi:10.1109/tim.2021.3121488

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…This may be due to the fact that, by having the waveform of the discharges, it is possible to separate the discharges from other sources by means of algorithms and, above all, separate the noise, a procedure that is explained in Section 2 and discussed in more detail in ref. [18]. Additionally, since the analysis is made on each pulse individually and not the distribution of the data, as in PRPD, it is not sensitive to errors in the phase measurement or in the sequence of the pulses; that is to say, the result would not substantially change in the case of a phase shift or if, for some reason, the measurement system skips several points in the sequence.…”

Section: Discussionmentioning

confidence: 99%

“…The other PD measuring system was the oscilloscope. A signal source separation process was applied to the measured waveform data to eliminate signals associated with noise, following the procedure described in [18]. This separation technique is based on the calculation of two parameters associated with the temporal and spectral behavior of PD signals.…”

Section: Methodsmentioning

confidence: 99%

“…The second parameter is the 'weighted maximum frequency' (F MW ), which characterizes the frequency spectrum of PD signals where their energy content is more relevant. With these two parameters, a 2D graph (map) is plotted [18]. The result is a map with two clearly identified clusters: one associated with PDs coming from electrical trees, and other associated with noise and perturbances.…”

Section: Methodsmentioning

confidence: 99%

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Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis

Schurch

Munoz²,

Ardila-Rey

et al. 2023

Polymers

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Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously—one to capture the sequence of PD pulses and another to acquire PD pulse waveforms—and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis

Schurch

Munoz²,

Ardila-Rey

et al. 2023

Polymers

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“…The chromatic technique (CT) was applied to characterize the frequency content of the two types of PD, considering the signals extracted from current and acoustic emission signals. CT is characterized as an effective pattern recognition tool, which allows feature extraction from complex signals [38,39]. The pattern recognition is performed by 3D maps that group similar features without the need for a supervised training process.…”

Section: Frequency Analysismentioning

confidence: 99%

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Castro

Santos

Lucas

et al. 2022

Sensors

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Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical–chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer’s shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition.

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Clustering by communication with local agents for noise and multiple partial Discharges discrimination

Boya¹,

Rivera-Caballero²,

Ardila-Rey³

2023

Expert Systems with Applications

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Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals

Cited by 11 publications

References 26 publications

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Clustering by communication with local agents for noise and multiple partial Discharges discrimination

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