Partial discharge and noise separation by means of spectral-power clustering techniques

Ardila-Rey, Jorge Alfredo; Martínez-Tarifa, Juan Manuel; Robles, G.; Rojas-Moreno, M. V.

doi:10.1109/tdei.2013.6571466

Cited by 92 publications

(105 citation statements)

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“…Thus the feature extraction for multi PD source separation has become the most recently research hotspot. The scholars proposed a variety of feature extraction methods based on pulse waveform [65,[97][98][99][100][101][102][103][104][105][106][107], which were proved to be promising in multi PD source separation by on-site testing as well as laboratory experiments. Table 6 summarizes feature extraction methods presented in this chapter, the corresponding reference is attached.…”

Section: Discussionmentioning

confidence: 99%

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A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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Abstract:Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

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Section: Discussionmentioning

confidence: 99%

“…The technique of choosing features by Kuljaca et al was improved by Ardila et al [102]. They divided the spectrum of each PD pulse into three frequency bands, and calculated the power ratio (PR) of the two higher band as the feature to perform clustering analysis.…”

Section: Frequency Parametersmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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“…Finally, the power spectral density of each signal is calculated and normalized to unit area before the analysis with the OCSVM (Figure 4). More details regarding the acquisition system can be found in [11,20,33]. Five different test objects were used to generate the training and test sets for the OCSVM:…”

Section: Methodsmentioning

confidence: 99%

“…However, since PD pulses are a consequence of low energy phenomena, in real industrial environments the signal to noise ratio (SNR) can be low, so the classical classification techniques, such as phase resolved partial discharge (PRPD) patterns, may not provide a clear discrimination by themselves, being necessary the application of new techniques to complement the results. In order to face this issue, high bandwidth detectors, capable of capturing as much information as possible from each signal for further processing are widely used [7,10,11]. Thus, the parametrization of pulses has been carried out in order to filter noise and classify the discharge source [7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…In order to face this issue, high bandwidth detectors, capable of capturing as much information as possible from each signal for further processing are widely used [7,10,11]. Thus, the parametrization of pulses has been carried out in order to filter noise and classify the discharge source [7,[11][12][13]. Following this research trend, several techniques, such as, time-frequency (T-F) maps [14], wavelet filtering [15] and power ratios (PR) maps [11] have succeeded in grouping the detected pulses in clusters based on information that is extracted from each signal.…”

Section: Introductionmentioning

confidence: 99%

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Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

et al. 2018

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This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.

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