A Novel AE Based Algorithm for PD Localization in Power Transformers

Mehdizadeh, Sina; Yazdchi, Mohammadreza; Niroomand, Mehdi

doi:10.5370/jeet.2013.8.6.1487

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In the cable partial discharge localization algorithm, the existence of noise is an important factor affecting the positioning accuracy [9][10][11][12]. In order to further verify the practicability of the proposed algorithm, the time-varying kurtosis method [29] was used to pick up the time when the partial PD pulse reached the sensor under high background noise.…”

Section: Measured Pd Pulse Detection For Arrival Time Assessment On Dmentioning

confidence: 99%

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

et al. 2019

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While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

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Section: Measured Pd Pulse Detection For Arrival Time Assessment On Dmentioning

confidence: 99%

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

et al. 2019

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“…Therefore, early detection of PD activity is very important for condition monitoring engineers to avoid any expensive and unexpected failure in a power system. Hence, condition assessment of these power utilities at regular intervals is essential in order to ensure an uninterrupted power supply [9][10][11]. The detection of PD is an important process to evaluate the insulation status of power equipment [12].…”

Section: Introductionmentioning

confidence: 99%

Effect of Hydrostatic Pressure on the Propagation of Partial Discharge Acoustic Signals in Transformer Oil

Ashraf¹,

Stewart

Solanki³

et al. 2021

IREE

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In this research article an attempt is made to investigate the effect of hydrostatic pressure on the propagation behavior of Partial Discharge (PD) acoustic emission (AE) signals in a simple experimental tank. A mathematical model has been developed in the form of partial differential equations (PDEs) for PD acoustic wave propagation in transformer oil in a steel tank. A Finite Element Analysis tool (FEMLAB), has been used to simulate the propagation of the acoustic waves. The simulation results show that the speed of the acoustic signal varies with the depth of the source location demonstrating hydrostatic pressure affects AE wave propagation. Furthermore, the effect of hydrostatic pressure was investigated and conformed experimentally. It is concluded that the propagation speed is not constant within the tank and increases with tank depth. If the effect of increased acoustic velocities due to hydrostatic pressure is ignored then the possibility of practical PD location algorithms inferring the incorrect location in high voltage assets, such as oil filled transformers, will be increased significantly.

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“…Acoustic emission method (AE) has become one of the most commonly used methods for few decades, and known as one of the most non-invasive methods that may be applied during normal operating conditions, without having to turn off the diagnosed device -contrary to other methods, which require temporary overhaul of the tested unit (Boczar et al, 2014;Cichoń et al, 2014;Glowacz et al, 2018;Olszewska, Witos, 2012. Furthermore, it is still the only method that is fully applicable for PD localization under on-site conditions (Coenen, Tenbohlen, 2012; Kraetge et al, 2013; Mehdizadeh et al, 2013). Despite the above-mentioned advantages, AE method also has some limitations, for example: no charge calibration has been possible so far (however, research in this field has been ongoing for several years (Witos, Gacek, 2005)), relatively low sensitivity which indirectly results in quite high relative sensibility to external disturbances (compering to e.g.…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Kunicki

2020

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This paper presents comparative analysis of various acoustic signals expected during partial discharge (PD) measurements in operating power transformer. Main purpose of the paper is to yield relevant and reliable method to distinguish between various acoustic emission (AE) signals emitted by PD and other sources, with particular consideration of real-life results rather than laboratory simulations. Therefore, selected examples of real-life AE signals registered in seven different power transformers, under normal operation conditions, within few years are showed and analyzed. Five scenarios are investigated, which represent five types of AE sources: PD generated by artificial sources, and next four real-life sources (including PD in working transformer, oil flow, oil pumps and core). Several different signal processing methods are applied and compared in order to identify the PD signals. As a result, an energy patterns analysis based on the wavelet decomposition is found as the most reliable tool for identification of PD signals. The presented results may significantly support the process of interpretation of the PD measurement results, and may be used by field engineers as well as other researchers involved in PD analysis using AE method. Finally, observed properties also provide a solid basis for establishing or improving complete classification method based on the artificial intelligence algorithms.

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A Novel AE Based Algorithm for PD Localization in Power Transformers

Cited by 7 publications

References 23 publications

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Effect of Hydrostatic Pressure on the Propagation of Partial Discharge Acoustic Signals in Transformer Oil

Archives of Acoustics

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