Corona Partial Discharge Research Articles

Partial discharge type detection and identification based on its sources

Partial discharge (PD) is a symptom of initial damage to high voltage equipment insulators which if left for a long period will cause total damage to high voltage equipment. This study aims to detect and identify the type of PD based on the source of its discharge so that it can be useful in terms of monitoring and maintenance of high voltage equipment. In this research, the Hilbert fractal antenna sensor is used in the detection process of surface, cavity, and corona PD with different input voltage variables that successfully produce a total of 600 PD signal data on the oscilloscope. To reduce noise on the PD signal, the denoising process is done by utilizing the sym4 wavelet feature found in the MATLAB software. The denoising process generates new data so that the research data becomes 600 original PD signal data and 600 denoising PD signal data. With a statistical approach, all PD signal data is extracted successfully into the mean, skewness, kurtosis, and standard deviation parameters which are useful as input for the PD type identification process. From each of the PD signal statistical data, 450 data are used in the training data process and 150 data are used in the data testing process. The PD type identification process is performed using a back propagation neural network with a mean square error (MSE) level of 0.01. The identification results show that back propagation neural networks are able to identify PD types based on statistical input accurately. In addition, the denoising process also affects the accuracy of the identification results of the PD type that is 95.33% for the original discharge signal to 97.33% for the denoising signal.

Partial Discharge Recognition Based on Optical Fiber Distributed Acoustic Sensing and a Convolutional Neural Network

Fiber-optic distributed acoustic sensing (FDAS) with phase-sensitive optical time-domain reflectometry (Φ-OTDR) is a promising technique for high-sensitivity measurement. In this paper, an improved Φ-OTDR system with a weak fiber Bragg grating (wFBG) array for partial discharge (PD) detection in cross-linked polyethylene (XLPE) power cables is demonstrated; and an event recognition method based on a convolutional neural network (CNN) model is proposed to identify and classify different types of events, including internal PD, corona PD, surface PD, and noise. A multiscale wavelet decomposition and reconstruction method is used to extract PD signals and a two-dimensional spectral frame representation of the PD signals is obtained by the mel-frequency cepstrum coefficients (MFCC). The experimental results based on 1280 training samples and 832 test samples have demonstrated high values of precision, sensitivity, and specificity for each event (up to 96.3%, 96.4%, and 98.7%, respectively), which means that the combination of multiscale wavelet decomposition and reconstruction, the MFCC and CNN may be a promising event recognition method for the FDAS systems.

Correlation of hydrogen generation with sparking discharges in a mineral insulating oil

Dissolved gas analysis (DGA) of transformer oil is one of the key methods of assessing transformer health [1]. Transformers operate under multiple stresses, e.g., thermal, electrical, and mechanical. The hydrocarbon molecules of transformer oils can be degraded into combustible and incombustible gases under high thermal and electrical stresses, particularly under fault conditions. Different generated gases correspond to specific fault types and levels, e.g., hydrogen is the primary indicator of corona partial discharge, and acetylene is the primary indicator of sparking discharge and high-energy breakdown [2], [3]. DGA technology is therefore widely used to reveal incipient transformer faults by measuring the concentration of fault gases in the transformer oil.

Characterization and comprehension of corona partial discharge in air under power frequency to very low frequency voltage

For the partial discharge test of electrical equipment with large capacitance, the use of low-frequency voltage instead of power frequency voltage can effectively reduce the capacity requirements of test power supply. However, the validity of PD test under low frequency voltage needs to be evaluated. In order to investigate the influence of voltage frequency on corona discharge in the air, the discharge test of the tip-plate electrode under the frequency from 50 to 0.1 Hz is carried out based on the impulse current method. The results show that some of the main features of corona under low frequency do not change. The magnitude of discharge in a positive half cycle is obviously larger than that in a negative cycle. The magnitude of discharge and interval in positive cycle are random, while that in negative cycle are regular. With the decrease of frequency, the inception voltage increases. The variation trend of maximum and average magnitude and repetition rate of the discharge in positive and negative half cycle with the variation of voltage frequency and magnitude is demonstrated, with discussion and interpretation from the aspects of space charge transportation, effective discharge time and transition of discharge modes. There is an obvious difference in the phase resolved pattern of partial discharge and characteristic parameters of discharge patterns between power and low frequency. The experimental results can be the reference for mode identification of partial discharge under low frequency tests. The trend of the measured parameters with the variation of frequency provides more information about the insulation defect than traditional measurements under a single frequency (usually 50 Hz). Also it helps to understand the mechanism of corona discharge with an explanation of the characteristics under different frequencies.

A partial discharge–based health index for rotating machine condition evaluation

It is well known that partial discharge (PD) is a threat to electrical insulation in all electrical apparatus, but we also know that PD harmfulness, and thus damage, depends on the type of defect generating the PD. As an example, having corona PD in switchgear boxes, rather than surface PD on bushings or internal PD on cable terminations, will trigger completely different actions from maintenance managers, even if the amplitude of the first (corona PD) is much larger than those of the last (internal PD). Indeed, corona PD may not need any medium- to short-term action, surface PD can just require some careful periodic checking, and internal PD in cable joints/terminations will have to be addressed promptly.

PD Characteristics in Transformer Oil under AC Condition

Partial discharge (PD) detection is a technique widely used for high voltage equipment insulation condition assessment. The metal protrusion is a situation that often appear in transformer. Metal protrusion will cause the electric field concentration and lead to partial discharge. In this paper, PD characteristics in transformer oils are examined under AC conditions, using a needle-to-plane electrode system. The PD activity in transformer oil is confirmed as appearing in pulse burst form and the PD number and amplitude will increase with the applied voltage increase. The PRPD pattern display behavior typical of corona PD in oil.

Research on Classification of Partial Discharge of Switchgear Cabinets Based on a Novel Association Rule Algorithm

In order to assess switchgear insulation status, a novel association rule mining (ARM) algorithm is presented. It is used to recognize the severity of switchgear cabinet partial discharge. The algorithm uses fuzzy C-means clustering (FCM) to divide partial discharge feature interval, candidate sets meeting minimum support and minimum confidence are sought based on an improved Apriori algorithm. Multiple recursions and scans are performed on candidate sets to generate association rules library for classification. Fuzzy reasoning based on association rules are performed over multiple needle corona partial discharge signals sampled in 10KV switchgear cabinets. The results show that partial discharge classification rate using association rules is high and classification conclusions are accurate. It has provided theoretical basis and practical value for insulation status assessment of switchgear cabinets.

Transient Earth Voltage Measurement in PD Detection of Artificial Defect Models in $\hbox{SF}_{6}$

This paper mainly discussed the utilization of transient Earth voltage measurement (TEVM) in partial discharge (PD) detection of gas insulation switchgear. Four typical artificial defect models in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> such as spikes fixed on high voltage (HV) conductor, suspended metals, voids in dielectric, and metal particles on the surface of dielectric had been used as test objects in ac PD experiments with the aid of the synchronized PD signals sampled from a transient Earth voltage sensor, photomultiplier, Rogowski monitor, and PDCheck system. The measuring results of TEVM results, which included the phase distribution of PD, the relative amplitude, and the pulse number of different voltage thresholds, showed good consistency with three other methods and had better sensitivity of detection in spark or corona PD patterns. For further study, the frequency distributions of the three PD patterns were analyzed and discussed for understanding the different measurement sensitivities showed in different typical defects in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> . In addition, TEVM was applied to detect PD under oscillating impulse voltage. As an example, the needle-plane electrode with short gap was adopted under 613.2-kHz oscillating impulse, which showed the effectiveness in this kind of detection.