An overview of state-of-the-art partial discharge analysis techniques for condition monitoring

Abstract:The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

Section: Partial Discharge Detection Techniquesmentioning

confidence: 99%

“…Depending on the voltage and the size of the transformer, the acceptable limits of PD for new transformers are in the range < 100 pC to < 500 pC [46]. Early detection and localization of PD is of utmost importance, it facilitates preventive repairs to avoid unforeseen breakdowns [56][57][58].…”

Section: Partial Discharge Detection Techniquesmentioning

confidence: 99%

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Fofana

Hadjadj

2016

“…Energies 2017, 10,1694 where xi is a specific feature value of the ith phase or amplitude window, N is th and f(x) is the function of PD distribution curve. These statistical moment characterizing PD distributions derived from different PD sources, therefore importance in single PD source recognition and thus become the most prevalent In addition, a study conducted by Mas'ud et al [87] demonstrated that ther distinction for different phase window size and amplitude window size on som moments, thus a special care has to be taken to maintain these window sizes fo PD pattern recognition.…”

Section: Weibull Parametersmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

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.

“…It has been used for de-noising of the ultra-high frequency signal and eliminating the corona from PD signal [10][11][12][13][14][15][16]. In addition, parameters derived from wavelet decomposition are implemented in the feature extraction for defect classification [17,18]. Satish and Nazneen studied the de-noising of PD signals buried in excessive discrete spectral, stochastic, and random interferences using the WT method.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Wang

Kil

et al. 2017

With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GISs), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase-resolved partial discharge (PRPD) signal generated in GIS spacers based on discrete wavelet transform (WT). Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded, whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.