Automated wavelet selection and thresholding for PD detection

Ma, Xiandong; Zhou, Chengke; Kemp, I.J.

doi:10.1109/57.995398

Cited by 227 publications

(179 citation statements)

References 13 publications

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“…The mother wavelet will not only determine how well the original signal is estimated in terms of the shape of the PD spikes, but also affect the frequency spectrum of the de-noised signal [11]. The choice of mother wavelet can be based on eyeball inspection of the PD spikes or it can be selected based on correlation γ between the signal of interest and the wavelet de-noised signal [12], or based on the cumulative energy over some interval where PD spikes occur. Being well aware of this issue, a number of mother wavelets have been examined and it was finally found that the Daubechies wavelet was the most suitable for treating PDs.…”

Section: Suitable Selection Of the Mother Waveletmentioning

confidence: 99%

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

Al-geelani¹,

Piah²,

Saeh³

et al. 2016

IJECE

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A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures. Keyword:Acoustic INTRODUCTIONOutdoor insulation represents an important component of electric power transmission and distribution systems, given that a single insulator failure can result in an excessive outage of the power system. Different insulator designs and materials are employed by power corporations and their behaviours are investigated and tested in laboratories and field tests as well as during service conditions. Specimens (rods and plates) are also tested when researchers focus on investigating certain phenomena of surface activity or material performance without being influenced by the insulator design [1]. The performance of insulators is strongly linked to local conditions, especially to the accumulation of pollutants and the wetting mechanisms present.The weak parts of the insulation are the cavities, since the gas breakdown strength is lower than that of the solid insulation. On the other hand, the electric field in the cavities is much stronger than in the big insulation parts due to lower gas permittivity [2]. Hence, the PD is limited inside the cavities and does not penetrate through the solid material to reach the electrodes. Initiation of a PD in a cavity needs two major conditions; essentially the cavity electric field should be more than that of the gas, a condition called the inception voltage level. To start an electron avalanche, a free electron must be present in the cavity [3]. The extinction voltage level may depend on the actual voltage at which a discharge starts, since presumably a higher inception voltage yields a higher initial temperature in the streamer channel. In this study, the adaptability of the Daubechies wavelets of orders 2 has been evaluated, and results have shown the superiority. It is befitting to select a suitable number of breakup levels based on the nature of the signal.

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Section: Suitable Selection Of the Mother Waveletmentioning

confidence: 99%

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

Al-geelani¹,

Piah²,

Saeh³

et al. 2016

IJECE

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“…Detecting the starting point of acoustic signal is very important for localization; therefore, every signal except PD acoustic wave is interpreted as a noise even an acoustic PD signal that is captured through the tank wall and not directly. So others have introduced some methods to de-noise AE produced by partial discharge [3,10,13] and [17][18][19][20][21]. Also some methods for localization such as peak criterion are proposed in [12,18,22] and [25].…”

Section: Introductionmentioning

confidence: 99%

A Novel AE Based Algorithm for PD Localization in Power Transformers

Mehdizadeh¹,

Yazdchi²,

Niroomand³

2013

Journal of Electrical Engineering and Technology

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-In this paper, a novel algorithm for PD localization in power transformers based on wavelet de-noising technique and energy criterion is proposed. Partial discharge is one of the main failures in power transformers. The localization of which could be very useful for maintenance systems. Acoustic signals due to a PD event are transient, irregular and non-repetitive. So wavelet transform is an efficient tool for this signal processing problem that gives a time-frequency demonstration. First, different wavelet based de-noising methods are analyzed. Then, a reasonable structure for threshold value determining and applying manner on signals is presented. Evaluated errors are good evidences for choices. Next, applying the elimination low energy frequency bands is discussed and developed as a de-noising method. Time differences between signals are used for PD localization. Different ways in time arrival detection are introduced and a novel approach in energy criterion method is presented. At the end, the quality of algorithm is verified through the different assays in lab.

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“…It is difficult to achieve the desired results by using the traditional frequency domain method. Therefore, the method of wavelet analysis with consideration of both time domain and frequency domain characteristics has been widely adopted [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Suppression of Pulse Interference in Partial Discharge Measurement Based on Phase Correlation and Waveform Characteristics

Gao¹,

Wang²,

Gui³

et al. 2015

JEPE

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Partial discharge measurement is one of the most effective methods to find insulation defects and early failure of high voltage power equipments. The accuracy is significantly reduced by the interference in the partial discharge on-site detection or on-line monitoring, especially by the pulse interference. This paper studies the phase correlation of some types of typical partial discharge pulses and their characteristics in time domain and frequency domain. By collecting enough partial discharge pulse data, the correlation coefficient can be calculated based on both phase correlation and waveform similarity. The type of pulse will be determined by the scope of the calculated correlation coefficient. The pulses with very strong correlation will be identified as periodic pulse interference. The pulses with very weak correlation will be identified as random pulse interference. Only the pulses whose correlation coefficients fall into a specific range will be identified as partial discharge signals. In laboratory, simulated pulse interference is injected into measurement circuit, and typical partial discharge pulses are sampled by a high-speed acquisition system. The pulse interference can be effectively separated from partial discharge signals by correlation coefficient.

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Automated wavelet selection and thresholding for PD detection

Cited by 227 publications

References 13 publications

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

A Novel AE Based Algorithm for PD Localization in Power Transformers

Suppression of Pulse Interference in Partial Discharge Measurement Based on Phase Correlation and Waveform Characteristics

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