On-line Partial Discharge Monitoring Research Articles

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

A New Method for Detecting Partial Discharge in T-Type Terminals of Power Cable

Breakdown accidents in T-type terminals of power cables occur occasionally and abruptly, causing severe explosions and seriously threatening the reliable operation of the distribution grid. As we know, there haven't been any practical sensors to detect partial discharge online in the T-type terminals with a rated voltage of 10 kV up to now. A novel method for detecting partial discharge (PD) in T-type terminals is proposed in this manuscript. Keeping the original structure of the T-type terminal intact, a sensor is embedded inside the T-type terminal plug to measure the PD induced voltage signal. The equivalent circuit is built. The measurement mechanism of the proposed method is studied. Two kinds of defects are manufactured in the T-type terminals, which are the stress tube displacement and the lack of the stress tube. The discharge tests are carried out and the PD induced voltage pulse signals are detected by the proposed sensor. Although the aim of this manuscript is to validate the proposed method, the characteristics of the PD induced voltages with different defects are compared. It is found that compared with the defect of the lack of the stress tube, the initial discharge voltage is large for the defect of the stress tube displacement. The dominant frequencies and the increased rate of PD induced voltage are very different. The device can be deployed in the cable system to achieve the purpose of online PD monitoring.

On-Line Partial Discharge Monitoring System for Switchgears Based on the Detection of UHF Signals

Aiming at the phenomenon of partial discharge caused by the insulation fault of switchgears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.

Intelligent monitoring of EHV transformer bushing based on multi‐parameter composite sensing technology

AbstractIn order to monitor the state of bushing online, an intelligent monitoring system for transformer bushing was developed. A four‐in‐one sensor integrating hydrogen sensing technology using palladium nickel alloy, pressure sensing technology, wide range temperature sensing, and micro water measurement technology was developed. A three‐in‐one integrated sensor based on micro current detection technology was developed to realize online monitoring of bushing dielectric loss, capacitance, and partial discharge. The test results show the hydrogen measurement range of sensor is 0 to 10,000 μL/L, and the measurement uncertainty is lower than 10% or 10 μL/L. The pressure measurement range is 0 to 1.0 MPa, and the uncertainty is lower than 0.3%. The temperature measurement range is −40°C to 85°C, and the uncertainty is lower than ± 1°C. The micro water measurement range is 0 to 1000 μL/L, and the measurement uncertainty is lower than ± 5% or 10 μL/L. The dielectric loss and capacitance error increased by one order of magnitude compared to current standards. The resolution of partial discharge is 5 pC. The performance of the device fully satisfies the requirements for online monitoring of transformer bushing. It has been installed in dozens of 330 and 750 kV substations, providing a reliable guarantee for safe operation of transformer bushing.

A Smart Grid Overvoltage Identification System Associated with Partial Discharge Signal and Dielectric Loss Detection.

Capacitive equipment refers to its insulation design using the principle of capacitance of electrical equipment, mainly by a variety of different capacitive components in series. Most of the equipment in the substation is capacitive equipment. Once an insulation failure occurs, it will lead to extremely serious consequences. Monitoring grid overvoltage and insulation degradation of capacitive equipment is an effective means to ensure the stable operation of the power system. Therefore, in order to enhance the health management of capacitive equipment, including transformers, bushings, and current transformers, and to mitigate the risk of severe failures, it is imperative to conduct broad-spectrum frequency-domain online monitoring of overvoltages, dielectric losses, and partial discharge. However, the current monitoring work requires the utilization of multiple detection apparatuses. Aiming at the disadvantage that the existing inspection is not well integrated and requires a combination of multiple devices. This paper proposes a smart grid overvoltage identification system that utilizes partial discharge (PD) signals in correlation with dielectric loss detection. The system achieves synchronous detection of dielectric loss and high-frequency partial discharge by synchronously and in real-time acquiring four current signals from the power grid, enhancing the integration level of the hardware system.

Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research.

To meet the real demand for broadband full-band high-gain antenna sensors in the process of partial discharge (PD) Ultra-High frequency (UHF) detection test and online monitoring of power equipment, this paper builds a resonant cavity monopole UHF antenna sensor based on Fabry-Perot resonant cavity antenna technology, conducts the sensor Voltage Standing Wave Ratio (VSWR) optimization study using curved flow technology, conducts the sensor gain optimization study using slot dual resonant structure, and, finally, tests the sensor performance using the built PD detection test platform. The resonant cavity monopole antenna exhibits outstanding VSWR performance in the frequency range of 0.37 GHz-3 GHz, according to simulation and test data: the average gain in the frequency range of 0.3 GHz-3 GHz is 4.92 dBi, and the highest gain at the primary resonant frequency of 1.0 GHz is 7.16 dBi, with good radiation performance over the whole frequency spectrum. The electromagnetic pulse signal sensed by the UHF sensor developed in this paper can demonstrate the energy spectrum distribution characteristics of PD radiation electromagnetic wave signal more comprehensively, laying a firm technical foundation for thoroughly understanding the electromagnetic wave radiation characteristics of various types of PD insulation defects of various power equipment and the selection of a specific direction for its supporting optimization.

Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems

In modern power systems, condition based monitoring and diagnosis is essential to ensure the effective and reliable operation of different high voltage equipment (HVE). Compared to other monitoring techniques, partial discharges (PD) measurement is considered as a key method for assessing the insulation health condition. The benefits of PD condition monitoring of HVE can be extended by proper detection, identification, and interpretation of PD signal. Among both online and offline PD monitoring techniques, online PD monitoring is a very promising technique that assists in robust monitoring system which reduces the power failure incidents in power system components. Therefore, to understand recent developments and trends in theory and in practice, it is necessary to establish a holistic analysis of current online PD monitoring techniques for HVE in power systems. This paper presents an intensive literature review of current online PD monitoring techniques used for different high voltage electric components in power system. Finally, a smart PD monitoring techniques based on wireless sensor board is proposed. The proposed smart PD monitoring framework may be used to correctly estimate the insulation degradation in HVE and enhance the overall performance of power systems.

A Novel Denoising Method for Partial Discharge Signal Based on Improved Variational Mode Decomposition

Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance.

Partial Discharges Monitoring for Electric Machines Diagnosis: A Review

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

Influence of Voltage Harmonics on Partial Discharge Diagnostics in Electric Motors Fed by Variable-Frequency Drives

Typically, in partial discharges (PD) diagnostics tests carried out in a laboratory, harmonic content in a voltage waveform is ignored. However, during the operation of variable-frequency drives (VFD)-fed electric motors, the distorted voltage waveform is produced due to the addition of harmonics components in the sinusoidal voltage waveform. The distortion level increases when the VFD is operated at a lower speed ratio. The additional spectral components in the applied voltage waveform can have a significant impact on the PD behavior in the insulation of the electric motor. Therefore, during online PD monitoring, overlooking the harmonic regime in the applied voltage may lead to a false interpretation of PD data. This article investigates the influence of voltage harmonics distortion produced during the variable-speed operation of VFD on PD characteristics. For this purpose, online PD measurements have been carried out by performing a series of experiments on eight VFD-fed motors under variable operating conditions. The PD severity at different harmonic compositions is experimentally investigated by determining various PD characteristic parameters including PD inception voltage, accumulated apparent charge, average discharge current, discharge power, and quadratic rate. Also, two terms Gaussian models have been mathematically developed using 288 data samples to quantify the variations in PD characteristic parameters against distortion parameters. The article proposes a framework for properly evaluating harmonics impact in PD for electric motors and correctly estimating stator insulation degradation.

Intelligent Online Partial Discharge Detection and Sensor

In order to realize the online monitoring of partial discharge in solid switch cabinet, obtain the real partial discharge power and evaluate the insulation status of the switch cabinet, an online detection device based on partial discharge in solid switch cabinet was proposed. The ultrasonic sensor and resonant circuit used by the device to collect the high frequency signal generated when partial discharge occurs and the high frequency signal was converted into the voltage signal. The voltage signal was sent to the STM32 main control chip after data preprocessing and analog-to-digital conversion. Through the conversion of the collected electrical data, the local discharge quantity was obtained and displayed on LCD screen in real time. If the detected discharge power was greater than a set value, an alarm would be automatically issued to remind the on-site power personnel to pay attention to it and prevent major power accidents caused by insulation damage. From the experimental results, it was found that the value of intermittent partial discharge collected by the developed device was 63pC, which was basically consistent with the DMS data. The experimental results showed that the device had the characteristics of simple operation and signal processing speed, high testing data real-time, and low cost, which was suitable for the real-time monitoring of the high voltage equipment in internal partial discharge. It was convenient for operators to maintain the equipment and ensure the normal operation of the equipment, which was of great significance to improve the reliability of power supply.

Convenient Online Approach to Multisource Partial Discharge Localization in Transformer

Partial discharge (PD) online monitoring is an essential part of transformer insulation condition based maintenance and prognostics and health management in industrial power system. In order to realize online localization of multiple PD sources conveniently, an approach that uses single-channel mixed acoustic emission (AE) signal is proposed in this article. First, the combined method based on the singular spectrum analysis and the independent component analysis is used to calculate the number of PD sources and realize the separation of mixed AE signals. Next, a difference selection algorithm is proposed to solve the problem that the inherent disorder of signal blind source separation makes the time difference of arrival technology difficult to localize the multiple PD sources directly. Then, the simulation signals and measured signals have verified that the signal separation effect of the proposed single-channel-based method is no less than that of the multichannel-based method. Finally, the proposed approach is applied to a transformer. The disordered separated signals were successfully grouped to realize multisource PD localization.

DFT Insight to Ag2O Modified InN as SF6-N2 Mixture Decomposition Components Detector

In gas-insulated switchgear (GIS), partial discharge (PD) can be monitored by detecting sulfur hexafluoride-nitrogen (SF6-N2) decomposition components. In this paper, silver oxide (Ag2O) modification was introduced to improve the gas-sensing properties of graphene-like indium nitride (InN). The adsorption process of NO2, SO2F2, SOF2 and SOF4 on Ag2O-InN was simulated based on the first principles calculation and density functional theory (DFT). The gas sensing mechanism was revealed by density of states theory and molecular orbital theory. It is found that Ag2O doping greatly improves the adsorption properties of InN to NO2 and SOF2 molecules. The adsorption capacity of Ag2O-InN to the four gas molecules is: NO2 > SOF2 > SOF4 > SO2F2. All adsorptions can proceed spontaneously, and the gas molecules are electron donors and Ag2O-InN is an electron acceptor. Through the analysis of recovery time, it is found that NO2 is difficult to desorb from the substrate due to the significant adsorption energy of −2.201 eV, while SOF4, SOF2 and SO2F2 have a moderate adsorption energy of −0.185 eV, −0.754 eV and 0.173 eV and extremely short desorption time. The conductivity of the whole system changed after these four gases were adsorbed on the Ag2O-InN monolayer. In summary, Ag2O-InN can be used as NO2 adsorbent and gas sensors to detect SOF4, SOF2 and SO2F2. This paper provides a method for on-line monitoring of partial discharge in GIS.

Digital Tools Approach Based On ‘Gerun’ And Online Partial Discharge Monitoring Project To Resolve Alternator’s Failure

There is no online monitoring system and digital tools to monitor generator gas turbine healthiness led to generator failure caused un-planned outages resulted power import and penalty chargers. Maintenance program for generator is based on time-based method. Since 2016 to 2020, Asset Team spent millions of ringgit Malaysia for generator rectification and incurred power import cost due to the failure. To overcome the tripping issues, Generator Health Monitoring System was initiated as a research/pilot project as tools to monitor healthiness of Gas Turbine Generator. This project was categorized as digital transformation initiative (Revolution 4.0) as to enable online monitoring & performing diagnostic & prescriptive. The initiative consisted of 2 main pillars which are Generator Rotor Unhealthiness Notification (GERUN) and Online Partial Discharge Monitoring System (PD ONLINE). The aim of this paper is to share the experience in monitoring, economical way, diagnosis, condition evaluation, and possibility of predicting the performance of the generator. The methodology of the Generator Healthiness is to monitor the real time performance of the generator during running condition through rotor thermal losses, ratio of excitation current over the real output power and stator partial discharge pattern. This real time monitoring can improve the maintenance strategy and planning. As a result, plant’s OEE can be increased, reduce the MTBF and more importantly the repair cost. The parameters have been introduced to prevent the generator sudden failure event and bridge the gap condition monitoring especially on the generator part. Arriving to the objective, this project was categorized as digital transformation initiative (Revolution 4.0) as to enable online monitoring & performing diagnostic & prescriptive. The initiative consisted of 2 main pillars which are Generator Rotor Unhealthiness Notification (GERUN) and Online Partial Discharge monitoring system (PD ONLINE) project. Diagnose & prescriptive and provide asset owners with insights on alternator’s health, subsequently prevent unforeseen failure in real time and prescribe operational mitigations to prolong alternator’s life. Recommends targeted test and maintenance (3rd level maintenance predictive) addressing the exact health issue of the alternator value creation & cost saving by eliminating tripping, scope reduction for major inspection, non-dependable on FSR, internal troubleshooting and etc. The system has been implemented across gas processing and utilities Petronas Gas Berhad for 14 units of gas turbine generator. In view of the success implementation, this digital tool will be duplicate to others Petronas Operating Unit.

Study of Ultra High Frequency Measurement Techniques for Online Monitoring of Partial Discharges in High Voltage Systems

Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of the electrical equipment with severe social and economic consequences. It is therefore imperative that PD is detected at its early stages to permit repair or replacement, prior to total failure. The bulky non-planar construction of existing ultra-high frequency (UHF) sensors coupled with the lack of proper feeding techniques and the ultrawide signal bandwidth of PD signals which necessitates the use of digital storage oscilloscope have restricted field deployment of UHF technique for online PD detection. In this work, we present a compact ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.58 \boldsymbol {\lambda } $ </tex-math></inline-formula> ) wideband (0.5-3 GHz) dual arm Archimedean slot spiral with coplanar waveguide feed for PD detection, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\lambda } $ </tex-math></inline-formula> is the wavelength at center frequency. Numerical simulations of the antenna were compared with the measurements of the fabricated antenna. The fabricated wideband antenna was used to detect PD emissions from a mineral oil test cell connected to 10 kVA 50 Hz transformer. We also present different UHF signal measurement techniques ranging from PD pulse detection to reconstruction of the bandlimited PD signal in the digital domain for online structural health monitoring of high voltage systems. The proposed UHF PD measurement techniques are low cost compared to the conventional approach and indicate the feasibility of an embedded system for online PD monitoring using the fabricated UHF sensor.

DFB Fiber Laser Based on External Optical Feedback Theory Applied in Long-Distance Power Transformer Partial Discharge Detection

Active fiber optic acoustic emission (AE) sensors cause external optical feedback (EOF) in engineering applications, resulting in performance loss when used for long-distance partial discharge detection in power transformers. This work employs a distributed feedback fiber laser (DFB-FL) as a sensor to construct a partial discharge detection system. Furthermore, two schemes that improve the long-distance detection performance of DFB-FL are proposed by Suppressing EOF (S-EOF) and Introducing EOF (I-EOF). Then, partial discharge tests in an 80 kVA power transformer validate that both schemes can implement long-distance partial discharge detection. Experimental results indicate that the S-EOF system has a significant signal-to-noise ratio, which is 6.01 dB higher than the piezoelectric transducer (PZT). The S-EOF scheme is suitable for long-term online partial discharge monitoring. The I-EOF system has a high sensitivity for partial discharge detection, and its detectable discharge magnitude is lower than 65.8 pC. The I-EOF scheme is applicable for short-term systemic sampling and daily inspections.

The Positive Role of the Partial Discharge Monitoring System in the Partial Discharge Detection of GIS Equipment

When gas insulated metal enclosed switchgear and controlgear (GIS) has internal discharge, some faults are difficult to find during inspections due to the enclosed metal casing. This paper introduces the event of the floating potential discharge inside the GIS equipment of a 500kV substation found by GIS on-line monitoring system. Through on-site ultra-high frequency detection and precise positioning of the oscilloscope to determine the position of the discharge source at a certain switch, combined with the disassembly of the equipment, it is determined that the equipment defect is the discharge of the floating electrode of the switch fork.The results show that the on-line monitoring of GIS partial discharge has an important reference role in discovering the internal discharge of equipment during the test interval, and the monitoring and maintenance of GIS partial discharge on-line monitoring system should be strengthened in daily work.

Noise suppression in PD signal based on Prony time series energy spectrum

On-line partial discharge (PD) monitoring is related to the safe and stable operation of power grid. However, the measured signal is inevitably affected by noise. Among a variety of noises, white noise is the most difficult to suppress and ubiquitous. It mainly determines the Cramer–Rao lower bound that cannot be crossed by all methods. This article proposes the Prony time-series energy spectrum method to suppress white noise in a low signal-to-noise ratio environment. This method is divided into two parts: First, Prony time series energy spectrum is used to detect whether there is PD signals in the measured signal. Then, based on the least square method, the PD signal is estimated with the Prony model. In data analysis, this method can suppress -10dB white noise, while other methods have performed poorly or even failed under the same conditions. In general, this method has excellent anti-noise performance compared to several other algorithms.

Partial Discharge Pattern Recognition of Transformers Based on the Gray-Level Co-Occurrence Matrix of Optimal Parameters

The partial discharge (PD) is the most common fault of transformers, which is the main factor affecting the stable operation of transformers. Therefore, the PD should be monitored and identified timely to improve the reliability of the transformers. In this paper, a transformer PD pattern recognition algorithm based on the gray-level co-occurrence matrix of optimal parameters and support vector machine (GLCMOP-SVM) is proposed. Firstly, the GLCM of optimal parameters (GLCMOP) is proposed to be determined by calculating the proportion of the off-diagonal elements (PODE) in GLCM. The GLCMOP has the advantage of avoiding the subjectivity of parameter selection and simplifying the calculation process. Then, the phase-resolved partial discharge (PRPD) maps are used as the PD samples and are converted into the GLCMOP to extract the PD features. Moreover, the feature space of the GLCMOP is dimensionally reduced by screening out the features with high distinguishability, which can improve the generalization ability and recognition speed of the classifier. Finally, the SVM classifier is trained to sort the PD samples and recognize the PD types, which include the tip discharge, surface discharge, and air discharge PD types. Lab tests are performed to verify the accuracy and validity of the proposed methodology. Compared with the traditional algorithms based on GLCM, XGBoost (eXtreme Gradient Boosting) and artificial neural network (ANN), the performance of GLCMOP-SVM is better. The GLCMOP-SVM has less memory consumption and faster recognition speed, so it is very suitable for the online and real-time monitoring of PD occurred in the transformers.

Construction and Experimental Verification of Novel Online Partial Discharge Monitoring System Using Power Line Communication

In this research, we aim to construct a completely new partial discharge (PD) monitoring system that applies inexpensive power line communication (PLC). We have proposed a PD detection method and monitoring system (PDMS-PLC) on the basis of PLC signal transfer rare is affected by an error due to disturbance. An attempt is made to identify PD source from the quadrant dependence of information transmission rate (hereinafter referred to PHY rate [Mbps]) by selecting a pair of quadrants of applied voltage phase; i.e. 1st and 3rd quadrants, or 2nd and 4th ones. It was found that the PHY (Abbreviation for the lowest physical layer in the OSI (open systems interconnection) hierarchical model.) rate number distribution reflects the PD characteristics obtained by the phase resolved PD (PRPD) pattern. Environmental noise level is also measured at an actual substation using PDMS-PLC. Consequently, the applicability of PDMS-PLC to PD signal detection and defect type identification is suggested.