Application of the local polynomial Fourier transform in the evaluation of electrical signals generated by partial discharges in distribution transformers

Rojas, Herbert E.; Forero, María Carolina; Cortes, Camilo A.

doi:10.1109/tdei.2016.005910

Cited by 9 publications

(3 citation statements)

References 60 publications

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“…A more ideal concept would be to use the raw pulses registered by the sensor, as some authors have done, using a single pulse waveform or several consecutives pulses recorded over time, or transforming the pulses into a spectrogram image. Other representations could also be investigated to avoid limitations imposed by the STFT in the spectrogram representation, as the Local Polynomial Fourier Transform [32] or a scalogram [33].…”

Section: Discussionmentioning

confidence: 99%

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios¹,

Buldain

Comech

et al. 2019

Energies

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This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

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Section: Discussionmentioning

confidence: 99%

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios¹,

Buldain

Comech

et al. 2019

Energies

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“…STFT is a widely used time-frequency tool for studying nonstationary signals and it has been proven to be effectively used in this field [33]. The discrete STFT of the discrete signal x can be written as:…”

Section: A Time-frequency Transform Via Stft (Step 1)mentioning

confidence: 99%

An Automatic Tool for Partial Discharge De-Noising via Short-Time Fourier Transform and Matrix Factorization

Yan

Trinchero

Stievano

et al. 2022

IEEE Trans. Instrum. Meas.

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This paper develops a fully automatic tool for the denoising of partial discharge (PD) signals occurring in electrical power networks and recorded in on-site measurements. The proposed method is based on the spectral decomposition of the PD measured signal via the joint application of the short-time Fourier transform and the singular value decomposition. The estimated noiseless signal is reconstructed via a clever selection of the dominant contributions, which allows us to filter out the different spurious components, including the white noise and the discrete spectrum noise. The method offers a viable solution which can be easily integrated within the measurement apparatus, with unavoidable beneficial effects in the detection of important parameters of the signal for PD localization. The performance of the proposed tool is first demonstrated on a synthetic test signal and then it is applied to real measured data. A cross comparison of the proposed method and other state-of-the-art alternatives is included in the study.

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“…Multiple signal processing methods can be applied to PD signals, such as Fourier transform, wavelet transform, shorttime Fourier transform, and Hilbert-Huang transform (HHT) [10][11][12][13][14]. These methods can filter noise for a clearer PD signal and then transform the signal into a phase-resolved partial discharge (PRPD) pattern or time-frequency image pattern.…”

Section: Introductionmentioning

confidence: 99%

Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus

2023

IET Science Measure & Tech

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Partial discharge (PD) detection is used to evaluate the insulation status of high-voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this study applied the visual geometry group-19 (VGG-19) model to gas-insulated switchgear (GIS) PD image recognition. A high frequency current transformer and an LDP-5 inductive sensor measured PD electrical signals emitted by 15-kV GIS. Next, the Hilbert energy spectrum was obtained by Hilbert transform in the time and frequency domains. Compared with a phase-resolved PD pattern, the Hilbert spectrum can represent the energy and instantaneous frequency with the time variable. Finally, the VGG-19 model was applied for PD pattern recognition. For validation, its recognition performance was compared with that of a fractal theory by using a neural network method. The VGG-19 method is straightforward and has a high PD recognition rate, thereby enabling equipment manufacturers to quickly verify the insulation of GIS during assembly or operation.

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Application of the local polynomial Fourier transform in the evaluation of electrical signals generated by partial discharges in distribution transformers

Cited by 9 publications

References 60 publications

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

An Automatic Tool for Partial Discharge De-Noising via Short-Time Fourier Transform and Matrix Factorization

Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus

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