Partial Discharge Pulse Signal Research Articles

Defects Detection of Onboard Cable Termination in EMUs Using Partial Discharge Measurement and SDP-DL Framework

Termination is the most important part of on-board cable, which plays a vital role in ensuring the continuous and reliable power supply to EMUs (electrical multiple units). The maintenance time of EMUs is limited, so the time left for PD (partial discharge) measurement of on-board cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this paper proposed an SDP (symmetrized dot pattern)–DL (deep learning) framework to detect the insulation defects using time series PD pulse signal. First, a PD measurement platform and experimental samples were prepared in laboratory to obtain the time series PD pulse signals of four typical insulation defects. Then, the time series PD signals were converted into SDP images using the proposed parameter optimization method. Finally, the SDP-DL framework was proposed, and three specific and typical methods were utilized, i.e., CNN (convolutional neural network), SAE (stacked auto encoder) and DBN (deep belief network). The results show that the performance of SDP-DBN method is the best, and the insulation defects can be detected with accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time series signal.

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

Credibility-Based Fault Fusion Diagnosis Method for Power Transformer

The fault causes and phenomena of power transformers are complex; different monitoring parameters can reflect its state from different dimensions. In view of the diversity of monitoring data and the characteristics that the acquisition time of different monitoring data is not completely simultaneous, the power transformer fault fusion diagnosis method is proposed based on credibility theory in this paper. First, the monitoring data, including dissolved gas analysis data, partial discharge pulse signal, ultrasonic signal and vibration signal, are diagnosed, respectively. Then the credibility method is used to fuse the single diagnosis results to obtain the comprehensive state of transformers. The case analysis shows that the proposed method can not only make full use of various monitoring data to enhance the accuracy of transformer fault diagnosis but also apply to the fault diagnosis of transformers with different monitoring data types. This provides a new idea for the fusion diagnosis of power transformers.

Current sensor optimization based on simulated transfer function under partial discharge pulses

The time measurement efficiency of the partial discharge (PD) relies on the signal-to-noise ratio (SNR) and gain of the high-frequency current transformer (HFCT) sensor. However, the PD's time measurement efficiency decreases with the noise coupled to the HFCT in onsite measurements. To overcome that setback, this paper proposes one pre-processing, through modelling and simulation, considering the physical effects, features of the electrical circuit and coil construction parameters of the HFCT. The main goal is to reach reasonable high SNR under the strong influence of background noises. This investigation aims to validate the hypothesis of improvement or deterioration of the HFCT signal response through a transfer function optimization. This research effort's contributions are threefold: 1. Generation of PD pulse signal and noise addition; 2. HFCT modelling, simulation, and frequency response analysis; and 3. Models performance evaluation and validation of hypothesis. In conclusion, the pre-processing approach stands out as a means to robustify and provide freedom to the electric utility, making up for an eventual need to redefine the physical and geometrical parameters of the HFCT sensor under specific background noise for maintenance tests purpose. According to a cyber-physical system framework, experiments corroborate the project's goals to contribute to the PD pattern monitoring in onsite measurements and incorporate robustness to signals with low SNRs.

A Study on the Mobile Partial Discharge Locating System

This paper studies on the Partial Discharge (PD) Locating System based on a mobile array of ultra-high frequency (UHF) antennas and a vehicle-mounted PD locating system is established. The system consists of omni-directional antenna array for receiving UHF PD signals, a pre-processing circuit for signal amplification and filtering and the high- speed acquisition and control unit of PD pulse signals. The developed locating system is able to simultaneously record the PD pulse signals received by the antenna array. By assessing the time difference of arrival (TDOA), the two-dimensional hyperbolic locating model quickly locates the PD source. Based on the software developed by LabVIEW, it is also possible to display, store and further analyze the acquired signals. Through the simulation of PD signals and the locating experiments with the system, it is proved that the PD locating system possesses the features of rapidity and precision in determining the bearing of PD source.

Random sampling and data processing for PD-pulse height and shape analysis

A measuring system for the digital acquisition of partial discharge (PD) pulse signals, based on the last generation of oscilloscopes, has been developed in order to perform both PD pulse shape and PD pulse height analysis. Wide-band, fast sampling rate and individually-triggerable memory blocks are available for the acquisition of PD pulse signals. However, a problem of sampling the population of PD pulses arises from the limited available on-line storage memory. Four sampling techniques are investigated and evaluated in order to record an amount of PD pulses which enables PD stochastic inference for a minimum of the on-line memory use. Some statistical indexes based on the pulse-height and pulse-phase distributions are used to compare performances of the different techniques. It is shown that a technique based on the Poisson law provides the most accurate sampling of PD pulses, while minimizing the memory, particularly in the presence of two simultaneously active PD phenomena. The developed procedure enables an accurate analysis of the shape of a large number of PD signals, but also stochastic processing of height distributions, which is becoming a reference for PD pattern analysis.

Novel Partial Discharge Measurement Computer-Aided Measuremnet Systems

A computer-aided measurement system has enabled novel interpretation of partial discharge (PD) signals. We take into consideration voltage phase-angle at which each PD pulse takes place, and consequently need simultaneous measurement of the pulse height q and the phase angle ?. According to the analysis of PD pulse signals, close relation between PD pulse signals and insulation degradation or defects was found. New PD characteristics such as 4-q distribution profile (average pulse height versus phase angle) can detect the growth of electrical trees in insulation caused by PDs. The skewness of the ?-q distribution was found to be a good measure for insulation degradation by PDs and for electrical trees. The skewness of the ? -q distribution was found to be an indicator of the shape of a void in which PDs occur. A skewness plane or an S-plane is proposed to monitor electrical treeing degradation. Furthermore, an effective noise reduction system is devised for practical b application of the new profile for insulation diagnostics.