A Comparative Study of Denoising Techniques for UHF Signals from Partial Discharge

Boya, Carlos; Rivera-Caballero, Omar; Ardila-Rey, Jorge Alfredo

doi:10.1109/iestec54539.2022.00099

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Wang et al [63] suggested a denoising method based on wavelet threshold and VMD optimization with a genetic algorithm (GA) for mining cable PD signals. Additionally, thorough comparative analysis has been published in [64,65] to evaluate the effectiveness of WT, SVD, and VMD, the three denoising approaches, for acoustic signals produced by PD sources. The WT for PD signal denoising was originally applied by researchers Ma et al and Zhou et al [52].…”

Section: Wavelet Transform-based Feature Extractionmentioning

confidence: 99%

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

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Section: Wavelet Transform-based Feature Extractionmentioning

confidence: 99%

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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“…Moreover, the wavelet interference suppression method is compatible with other filters and in combination with itself by varying parameters [18]. Besides methods for interference suppression of PD measurements using the discrete wavelet transform (DWT), there are comparable methods for interference suppression of PD measurement signals with the variational mode decomposition and the multiscale principal component analysis [19].…”

Section: B State Of the Art -Interference Suppression By Digital Sign...mentioning

confidence: 99%

“…This results in an SNR of 1 to 5. A monopole antenna of 5 cm length is used outside the test object in [19]. The PD signals have an SNR of up to −10 dB.…”

Section: B State Of the Art -Interference Suppression By Digital Sign...mentioning

confidence: 99%

“…For evaluation of interference suppression processes, besides the already mentioned signal to noise ratio (SNR), parameters such as the noise reduction level (RNL) [9] [17] or noise reduction ratio (NRR) [15] [19] are commonly used. Parameters such as the root mean square error (RMSE) [15] [17] [18] or mean square error (MSE) [10] and the correlation coefficient (Corr) [8] [15] [19] are used to evaluate the influence of the interference suppression process on the input useful signal.…”

Section: B State Of the Art -Interference Suppression By Digital Sign...mentioning

confidence: 99%

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Evaluation Study on Wavelet Denoising of Antenna-based PD Measurements in Strong Interference Environments considering a New Reliability Score of Pulse Detection

Friebe,

Jenau

2024

IEEE Trans. Dielect. Electr. Insul.

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Partial discharge (PD) interference suppression represents an important aspect in diagnosis and maintenance of equipment in the power transmission network and thus contributes to ensuring transmission reliability. PD diagnosis includes the detection of PD using suitable measurement methods like non-contact electromagnetic methods such as antennas. An important step of measuring PD with antennas is the interference suppression of the measurement signals for further processing. Especially in the case of contactless detection of PD using antenna structures, other signals are coupled in additional to the actual PD signal. This requires interference suppression processes for pulse extraction of the PD pulses from the disturbed signal as well as evaluation criteria that measure the interference suppression and the reliability of the PD pulse detection of these processes. In the context of this work, a suitability study is carried out to select the mother wavelets and estimation methods within the framework of wavelet denoising. This is done by a preliminary study with synthetic signals in which a real noise environment is added, which is several times stronger than the synthetic PD signal. The best combinations are then applied to real PD signals measured in a strong interference environment. In addition to the conventional evaluation criteria for noise reduction and signal distortion, a new evaluation criterion, the reliability score of impulse detection, is introduced. The methodical approach also reliably determines whether generated PD are isolated, as a transmission line-bound reflection-free decoupling of the PD signal takes place simultaneously.

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A Deep Learning Framework for Noise Elimination of Partial Discharge Signals

Kumar,

Ganguly,

Dey

et al. 2023

2023 IEEE 2nd International Conference on Industrial Electronics: Developments &Amp; Applications (ICIDeA)

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A Comparative Study of Denoising Techniques for UHF Signals from Partial Discharge

Cited by 4 publications

References 26 publications

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Evaluation Study on Wavelet Denoising of Antenna-based PD Measurements in Strong Interference Environments considering a New Reliability Score of Pulse Detection

A Deep Learning Framework for Noise Elimination of Partial Discharge Signals

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