Partial Discharge Monitoring System on High Voltage Equipments using Electroacoustic Technique

Partial discharge (PD) monitoring system is considered as one of the most promising solutions for monitoring and detecting possible faults. PD is able to diagnose faults within the system in the most fundamental and simplest way. PD monitoring and measurement of high voltage equipment substations panel and power transformers have not gained the same attention in the electrical community as compared to those of rotating machines. PD tests are conducted on-site to verify the insulation of bushings, termination box and windings. The cost of an unexpected outage may be considerably more significant for a high voltage substation panel and power transformers system than just for a single motor failure. In this research, the electroacoustic PD monitoring technique is used, which is a combination of internal PD detection and ultrasound or surface PD detection was used. The testing equipment which will be used in this research is UltraTEV Plus+ equipment from EA Technology. Result shows the transient earth voltage (TEV) PD magnitude fluctuates with time. Significant high value of PD magnitude (> 19 dB) was detected and recorded at several switchgear panel and transformer. However, for the ultrasound or surface PD, from the monitoring and measurement record, no detection of PD signal was recorded at the UltraTEV Plus+ testing equipmen

Application to Real Power Networks of a Method to Locate Partial Discharges Based on Electromagnetic Time Reversal

This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant under Agreement 838681.The paper presents an experimental validation of a method to locate partial discharges (PDs) on power distribution and transmission networks. The method is based on electromagnetic time reversal (EMTR) theory, and it uses a Transmission Line Matrix (TLM) model to describe the propagation of the PD signals in the reversed time. Since PDs are regarded as a symptom of insulation degradation, on-line PD location is considered an important approach to monitoring the integrity of a power distribution network, with the aim of detecting and preventing faults and improving network reliability. In this paper, the EMTR-based method is described and its effectiveness in PD localization using only one measurement point is demonstrated in three real 33 kV power lines. Its effectiveness is proved with and without an on-line electromagnetically noisy environment, and its accuracy is evaluated with respect to different signal-to-noise ratio (SNR) levels of the networks. The validation shows that the method is able to locate PDs with an error of 0.14% with respect to the total length of the line in the absence of noise, and with an error that is always lower than 0.5% for an SNR down to -7 dB

Partial discharge denoising for power cables

Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising.Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising