Partial Discharge Detection using Developed Low-cost High Frequency Current Transformer

Noise has been a major problem in Partial Discharge (PD) measurement, hampering its effectiveness as a means of assessing insulation condition and of diagnosing faults in power plant insulation. The problem has become more evident in recent years as efforts are made to adopt on-line PD-detection for practical plant insulation condition monitoring. The majority of PD detection systems use Ultra High Frequency (UHF), High Frequency Current Transformer (HFCT), Acoustic Emission (AE) sensors or IEC60270 standard electrical contact systems. Despite great advances which have been achieved in hardware instrumentation over the past years, various forms of noise still affect PD measurements, irrespective of the type of measurement system employed. This paper presents the experience which the authors have accumulated in recent years dealing with noise in PD measurement using the four, aforementioned, measurement systems. Following a brief comparison of the requirement for signal analysis of the four PD measurement systems, the paper addresses the source of noise that often appears in practical applications. Denoising techniques such as the traditional digital filters, matched filters, discrete wavelet transforms and the second generation wavelet transforms will then be introduced and contrasted in terms of their distinct features, advantages and disadvantages. The most effective technique will then be proposed for the four PD measurement systems discussed. Real world data will be used to support the conclusions of the paper.

Partial discharge detection on power equipment using a magneto-resistive sensor

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

Increasing need of cost effective identical sensors for partial discharge (PD) location prompted us to take up this research study. Four high-frequency current transformers (HFCT) were assembled and its performances with a used commercial sensor were compared with the known type of PD signals. In this paper, the responses due to surface, oil corona and air corona are analyzed. PD occurrence in 20 ms period and the shape of single PD with more sampled data points are characterized using seven techniques. The study indicates that HFCT with biflar winding is more sensitive in identifying different types of known PD. The number of turns and gauge of the used wire in HFCT play a role in increasing the sensitivity of PD detection.

High frequency current transformers (HFCTs) are widely employed to detect partial discharge (PD) induced currents in high voltage equipment. This paper describes measurements of the wideband transfer functions of HFCTs so that their influence on the detected pulse shape in advanced PD measurement applications can be characterized. The time-domain method based on the pulse response is a useful way to represent HFCT transfer functions as it allows numerical determination of the forward and reverse transfer functions of the sensor. However, while the method is accurate at high frequencies it can have limited resolution at low frequencies. In this paper, a composite time-domain method is presented to allow accurate characterization of the HFCT transfer functions at both low and high frequencies. The composite method was tested on two different HFCTs and the results indicate that the method can characterize their transfer functions ranging from several kHz to tens of MHz. Results are found to be in good agreement with frequency-domain measurements up to 50 MHz. Measurement procedures for using the method are summarized to facilitate further applications.

This study aims to establish an insulation diagnosis technique using partial discharge (PD) detection for cast resin transformers (CRTr). In the previous paper, the frequency components of PD current in a PD test of a CRTr by applying AC voltage between lumped high voltage and low voltage terminals were identified and analyzed in EMTP using an equivalent circuit model. On the other hand, another PD test is the test with an inductive voltage application in which an AC voltage is applied to the low voltage side of the transformer and then a high voltage is applied between the high voltage terminals in a transformer. This paper presents investigation on dominant frequency of PD current detected with high frequency current transformer (CT) in the inductive PD test of a 6.6 kV cast resin voltage transformer 6.6 kV CRTr-VT. An attempt is also made to identify PD signal source, the signal propagation path of the PD current, as well as the dominant frequency components.

A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, "PD Time", associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain.

Partial discharge (PD) based condition monitoring has been widely applied to power cables. However, difficulties in interpretation of measurement results (location and criticality) remain to be tackled. This paper aims to develop further knowledge in PD signal propagation in power cables and attenuation by the PD monitoring system devices to address the localization and criticality issues. As on-line or in-service PD monitoring sensors commonly comprise of a high frequency current transformer (HFCT) and a high-pass filter, the characteristics of detected PD pulses depend on the attenuation of the cable, the HFCT used and the filter applied. Simulation of pulse propagation in a cable and PD monitoring system are performed, based on analyses in the frequency domain using the concept of transfer functions. Results have been verified by laboratory experiments and using on-site PD measurements. The knowledge gained from the research on the change in pulse characteristics propagating in a cable and through a PD detection system can be very useful to PD denoising and for development of a PD localization technique.

Recently, non-contact radio-frequency (RF) methods have shown promising potential to be used as a complement to the IEC 60270 standard method for the detection and localization of partial discharge (PD) in high voltage insulation. This paper reports the results of an experimental investigation on the characteristic of partial discharge in an air gap using the (RF) method combined with the high-frequency current transformer (HFCT) method. The experiments were carried out under DC and AC high voltages and the effect of voltage polarity, voltage magnitude, and locations of the RF sensor relative to the PD source were examined. Analysis of the measured signals was carried out to classify both the frequency content of the discharge and its duration. The results show that the emitted electromagnetic signals from the AC discharge typically has its energy focused within two dominant frequency bands, while those of the DC discharge showed one dominant frequency band. The results indicate that the location of the RF sensor relative to the PD source had significant influence on the magnitude of measured PD signals.

Dry-type transformers require little maintenance and are more environmentally friendly with respect to traditional oil-and-paper design. However, partial discharges (PDs) and thermal stress may lead to aging and—eventually—failure of the insulation. Diagnostics of their conditions—often performed by means of PD measurement—can be tricky, as PD activity depends on many parameters. In this work, a cast-resin power transformer is aged by means of 28 heating/cooling thermal cycles, to reproduce the thermal–electrical stress it may experience in operation. PD activity is monitored during the cooling branch of each cycle using a high-frequency current transformer (HFCT) to measure the current bled to the ground by the transformer shield, whose results are compared to the apparent charge measured by an IEC 60270-compliant commercial system installed on the medium-voltage (MV) winding. The effect of temperature and aging (number of cycles) is then investigated. The results show good agreement between the two measuring systems, in terms of both the phase-resolved partial discharge (PRPD) patterns and the average intensity trend over time. According to the intensity dependence on temperature, two PD series were identified: one series shows a loose dependence on temperature, and the other one a strong dependence. It was speculated that two types of defects may exist, whose geometry (and hence PD activity) may be affected by temperature to different extents (e.g., due to their position). Finally, a decrease in PD rate in time was observed, without a significant reduction of the average discharge intensity, which is supposedly due to different aging rates of different defects.

The principal cause of damage in underground power cable installations is partial discharge (PD) activity. PD is a localized non-linear phenomenon of electrical breakdown that occurs in the insulating medium sitting between two conducting materials, which are at different potentials. The damage to the insulating material is induced by the AC voltage to which the insulator is subjected during the discharge process, and it can be directly or indirectly measured by the charge displacement across the insulation and the cavity defect. Non-invasive detection techniques that help in identifying the onset of the discharge process are required as PD is a major issue in terms of maintenance and performance of underground power installations. The main locations of failure are the accessories at points of connection such as terminals or splices. In this article, a study of electrical detection of PD and image processing of thermal pictures is presented. The study was carried out by controllably inducing specific failures in the accessories of the installation. The temporal evolution of the PD signals was supported with thermal images taken during the test in order to compare the PD activity and thermal increase due to failure. The analysis of thermographic images allows location of the failure by means of intensity-based texture segmentation algorithms. This novel technique was found to be suitable for non-invasive detection of the PD activity in underground power cable accessories.

High voltage equipment can be damaged when there is a failure of the insulation. Failure would be indicated by the Partial Discharge (PD) phenomenon during the pre-breakdown condition. PD detection methods commonly used, such as thermography and acoustic emission (AE), still have some weaknesses. Based on these conditions, this study discusses the detection method of PD containing Ultra High Frequency (UHF) in air insulation using an antenna that can capture electromagnetic waves. The electrodes used are needle to plane. The variables used are the shape and distance of the electrode. Characteristics analyzed during Partial Discharge Inception Voltage (PDIV). The data retrieved would be processed so that it would display Phase Resolved Partial Discharge (PRPD). The results obtained from the antenna would be compared with the leakage current method using the High Frequency Current Transformer (HFCT). PDIV pattern can be observed in the PRPD analysis, and each electrode has the characteristics of PD at a specific phase angle. The dominant PD point electrodes occur at an angle of 331.2 ° to 342 °, the dominant PD box electrodes occur at an angle of 334.8 ° to 356.4 °, and the dominant PD blunt electrode occurs at an angle of 306 ° to 313.2 °. So, it can be concluded that the loop antenna can detect and observe PDIV patterns in air insulation.

Detecting possible faults in low-voltage cables is a complex task, because the construction of these cables allows various partial discharge sources to incept even at test voltage. Experiments have shown high partial discharge magnitudes and activity even in new cables. This paper describes the attempts to detect faults in 0.6/1 kV PVC insulated (NYCY type) cables by characterizing the PD phenomenon in new cables and comparing them with that in damaged cables. For this purpose the partial discharge signals were detected by a high frequency current transformer and recorded with a digital oscilloscope. Experiments were performed at different voltage levels and durations. The recorded signals were processed in order to examine several different properties like magnitude, repetition rate and statistical operators characterizing the phase resolved pattern and the pulse height distribution.

Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN.

Research on characteristics of acoustic signal of typical partial discharge models