Partial Discharge Monitoring System Research Articles

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

Partial Discharge Monitoring of Power Transformers by Calibrated UHF Measurements

Partial discharge (PD) measurement is an established technique to detect local defects in the oil/paper insulation. They can be measured by electrical PD measurements according to IEC 60270 or by electromagnetic measurements in the ultra-high frequency range (UHF: 300 MHz – 3 GHz). The electromagnetic emissions of PD are recorded using an UHF sensor which is inserted into the transformer tank. The importance of PD measurements is reflected in the standard for charge-based electrical measurements (IEC 60270). Because of the standard, apparent charge, QIEC, became an essential factor for determining insulation quality in transformers. To be an accepted factor for both, quality testing in factory acceptance tests (FAT) or site acceptance tests (SAT) and continuous PD monitoring, the UHF technology must be as reliable and reproducible as the electrical method. To achieve this, a standardized calibration procedure is needed: It makes UHF measurement results from different systems comparable. Because this calibration procedure was lacking in the past, the UHF technology does not provide comparability between measurement systems and different sensors, yet. However, it shows advantages for on-site acceptance tests like SAT, continuous monitoring, and diagnostic purposes. E.g., regarding signal to noise ratios, the possibility for localization of PD sources and also for practical reasons like preparation times and accessibility on-site. This contribution presents a PD monitoring system which can be calibrated according to the calibration process described recently in Cigré TB 861 and two different types of UHF PD sensors for power transformers. One sensor type is for new transformers using dielectric windows and the other is for retrofitting transformers using their DN50/80 drain valves. A recommendation or strategy on where to place window-type UHF sensors at the tank of new power transformers is also provided in accordance with TB 861. Additionally, this contribution provides a description of the calibration process for UHF PD measurement systems consisting of two steps: 1) Calibration of the measurement instrument analogous to the calibration of the electrical PD measurement circuit using a defined reference signal. 2) Calibration of a UHF sensor using its characteristic antenna factor (AF). Furthermore, it shows three case studies from different power transformers equipped with UHF sensors and UHF PD monitoring systems.

Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers.

The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra's algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations.

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

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.

Deep convolutional neural network for partial discharge monitoring system

In the insulating material, the event of PDs takes place due to the air pockets, voids, or imperfections in such fields. In HV components like cables, the PD detection aids in ascertaining the insulating material condition. Consequently, the territory of PD estimation and conclusion is acknowledged as one of the most significant non-destructive methods for surveying the quality and specialized respectability of HV power mechanical assembly and links. In this work, a new data-driven model to recognize the condition of PD pulses of power cables is proposed with the aid of optimized Convolutional Neural Networks (CNNs). The two main stages of the developed framework are feature extraction and recognition. The data downloaded by VSB from the power cable is subjected to dimensionality reduction via Principal Component Analysis (PCA). Then, from these signals, the features are extracted with the aid of technical indicators like Rate of change (ROC), Relative Strength Index (RSI), Adaptive Moving Average (AMA), and Standard Deviation. Additionally, the features of the original power line data are also extracted. These features are put into an enhanced CNN as input. A new hybrid version known as the Combined Sealion-Swarm Optimization Algorithm (CS-SOA) optimizes the weight and activating function of CNN to increase the accuracy of the PD condition observation. From the result, it can be noticed that the accuracy of the proposed model, is 77.7%, 5.55%, and 3.335% higher than the existing models like SVM, CNN, and LSTM at TP= 80.

Requirements for ultra-high frequency partial discharge monitoring systems for gas-insulated switchgear

Requirements for ultra-high frequency partial discharge monitoring systems for gas-insulated switchgear

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.

Bi-Long Short-Term Memory Networks for Radio Frequency Based Arrival Time Detection of Partial Discharge Signals

Partial discharge (PD) monitoring of electrical substations could provide early warning of insulation failures. Among the various technologies, Radio Frequency (RF) based PD monitoring system could be a promising solution. The RF-based monitoring system detects PD sources in the substation and can also localise the PD sources. The time difference of arrival (TDOA) based PD localisation system primarily require arrival time (AT) of the impulsive RF signal. Though many localisation algorithms have been proposed in the recent past to overcome the TDOA estimation errors, less attention has been given to the accurate estimation of RF PD signal arrival time. This paper presents the AT's automatic labelling in the RF PD signal using Bi-Long Short-Term Memory (Bi-LSTM) network applied on the continuous wavelet transformed (CWT) signal. Further, it also shows PD signal augmentation to overcome the problem of limited representative training data set. The behavior of the radiated RF signals is influenced by many factors and has almost stochastic characteristics. The proposed system has been validated with laboratory-based experimental signals and the data set obtained from different electrical substations. The results show that the improved performance is obtained from the combination of a multilayer Bi-LSTM model and an augmented training set.

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.

Design and Implementation of Ultra-Wide Band Antenna for Partial Discharge Detection in High Voltage Power Equipment

Partial discharges (PD) are the most common and harmful threat to the health of electrical insulation of high voltage (HV) and high field (HF) equipment. The occurrence of PD activity in HV and MV equipment is at the same time a cause and a sign of insulation degradation that may eventually result in the breakdown of the HV power equipment. For the safe and reliable operation of HV power equipment, continuous PD monitoring needs to be conducted conveniently to prevent any unplanned power outages and damage to electrical power equipment. The ultra-high frequency (UHF) PD measurement method has been widely used as an effective technique to detect PD activity on HV power equipment due to its non-invasive principle. To enable PD detection accuracy, there is still a need for more sensitive PD sensors that can assist the UHF PD monitoring system by suppressing ambient background noise and low-frequency electromagnetic interferences from telecommunication such as GSM signals. To tackle the sensitivity issues, this article presents a new design of ultra-wideband (UWB) microstrip patch antenna that is capable of effective suppression of low-frequency interference signals. The proposed antenna, designed, simulated, and optimized using the CST Microwave Studio software, has a bandwidth of 3.3GHz, below -10dB, in the operating frequency range of 1.2GHz-4.5GHz. The prototype of this antenna, printed on an FR4 substrate of a thickness of 1.6mm and a dielectric constant of 4.4, featuring a compact size of 100mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times100$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times1.6$ </tex-math></inline-formula> mm, was implemented to detect PD through laboratory experiments. This paper shows that the designed UWB antenna has high sensitivity, good noise rejection and it is, therefore, a promising candidate sensor for PD detection on HV equipment.

Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System

The article presents an innovative system for non-contact high voltage (HV) measurement, which extends the measurement capabilities of a portable partial discharges (PD) monitoring system intended for diagnostics of power transformers. The proposed method and the developed measuring system are based on the use of a capacitive probe, thanks to which the high voltage measurement is safe (galvanic separation from the objects at ahigh potential). It is also flexible because the voltage ratio of this system can be configured in a wide range by changing the probe’s position. The proposed solution makes the portable PD monitoring system fully autonomous and independent of the substation systems and devices. The article presents both the concept of the non-contact HV measurement system and its practical implementation. The procedure for determining the voltage ratio and measurement uncertainty, which is at an acceptable level of 1–5% in laboratory conditions, was discussed in detail. In addition, the article discusses the digital filtering and wavelet de-noising methods implemented in the software of the monitoring system, which makes it possible to measure the voltage in the presence of strong electromagnetic disturbances occurring at the substation. Finally, the results of field tests carried out on a 250 MVA power transformer are presented, which confirmed the high accuracy of the HV measurement using a capacitive probe and the advantages of this technique.

A UHF Sensor Based Partial Discharge Monitoring System for Air Insulated Electrical Substations

A Partial Discharge (PD) monitoring system for Air Insulated Substations (AIS) has been developed for monitoring of partial discharge activities of various insulators and insulating media that are distributed over an electrical substation. The proposed system utilizes four UHF sensors, which are to be placed around the area of interest so that any impulsive UHF signal originated from PD activity can be captured and subsequently processed for detection and localization of multiple PD sources. To improve the localization accuracy and to improve the detection specificity, i.e. to eliminate false impulsive UHF signals originated from other sources, a novel nearest-neighbour based approach is proposed, which utilizes the physical layout of the AIS to identify most probable PD source locations. The proposed algorithm can accurately find the PD source locations even in the presence of high time-delay-of-arrival (TDOA) estimation error by making necessary corrections in the PD source location estimation. The proposed algorithm has been field trialed in many small and medium-size distribution substations. A detailed comparative study with the other algorithms in different extreme situations has been performed with the help of simulation and laboratory-based testing. The obtained results are worthwhile for supporting widespread implementation of the proposed method.

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.

Investigation of Electrical Tree Growth of XLPE Nano Composites using Time–Frequency Map and Clustering Analysis of PD Signals

Failure of underground cables due to electrical treeing phenomena, i.e. formation of electrical discharges in the imperfections of cable insulation, is a major problem faced by electrical utilities. Many research works on nanocomposites are being carried out to improve the electrical treeing resistance of the XLPE cable insulation material. Technological advancements in communication and data analytic systems have shown the way for implementing online continuous partial discharge (PD) monitoring systems for high voltage apparatus. Hence collection of PD database of XLPE nanocomposites in the laboratory during entire electrical tree growth process is important for implementing efficient condition monitoring systems and relatively little work has been published in this area. In this work, PD characteristics of XLPE nanocomposites with 1, 3, 5 and 10 wt% silica were investigated. Electrical tree growth and corresponding phase resolved PD (PRPD) pattern were analysed with respect to time. Cluster analysis of equivalent time–frequency mapping of PD signals was carried out with respect to tree growth time period. Statistical analysis was performed for the entire set of PD data. Results show that cluster analysis of T–F map of PD data is useful in estimating early failure of insulating material due to treeing. Addition of silica nano fillers in the range of 3–5 wt% concentration significantly improves the PD resistance and breakdown time of XLPE material.

Field verification of a novel and simple partial discharge detection method for generator applications

This study introduces a simple and novel partial discharge (PD) detection method for generator condition monitoring applications. The proposed method uses statistical operator ‘Variance’ which is proportional to the power of an electrical signal. It was validated by field measurement conducted at four in service hydro-generators: two having heavily aged windings and the other two having identical new. First, accuracy of detecting PD pulses was verified by comparing with wavelet-based method. The proposed method detected all the PD pulses with a ‘Precision’ higher than 90% and recall of 100%. As the second step phase resolved PD (PRPD) patterns generated using the proposed method were compared with commercial PD monitoring systems. It was found that the overall similarity was above 0.75. Finally, extracted PD pulses were used to calculate the polarity wise dissipated PD energies. The calculated energies were correlated with identified PD sources through series of off-line tests and visual inspections. It was observed that discharges due to loosen windings and corona activity were well correlated with estimated PD energies. All these validation processes indicated that the proposed method effectively and accurately detect PD pulses and can be used as a diagnostic tool for generator applications.

On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

The article presents a novel on-line partial discharge (PD) monitoring system for power transformers, whose functioning is based on the simultaneous use of three unconventional methods of PD detection: high-frequency (HF), ultra-high frequency (UHF), and acoustic emission (AE). It is the first monitoring system equipped in an active dielectric window (ADW), which is a combined ultrasonic and electromagnetic PD sensor. The article discusses in detail the process of designing and building individual modules of hardware and software layers of the system, wherein the most attention was paid to the PD sensors, i.e., meandered planar inverted-F antenna (MPIFA), high-frequency current transformer (HFCT), and active dielectric window with ultrasonic transducer, which were optimized for detection of PDs occurring in oil-paper insulation. The prototype of the hybrid monitoring system was first checked on a 330 MVA large power transformer during the induced voltage test with partial discharge measurement (IVPD). Next, it was installed on a 31.5 MVA substation power transformer and integrated according to the standard IEC 61850 with SCADA (Supervisory Control and Data Acquisition) system registering voltage, active power, and oil temperature of the monitored unit. The obtained results showed high sensitivity of the manufactured PD sensors as well as the advantages of the simultaneous use of three techniques of PD detection and the possibility of discharge parameter correlation with other power transformer parameters.

Pilot implementation of the on-line partial discharge monitoring system for heads of the high voltage cable lines

Pilot implementation of the on-line partial discharge monitoring system for heads of the high voltage cable lines

Partial discharge behavior of protrusion on high voltage conductor in GIS/GIL under high voltage direct current: Comparison of SF6 and SF6 alternative gases

Recent studies have demonstrated that fluoronitrile (FN) Novec™ 4710 and fluoroketone (FK) Novec™ 5110 show higher dielectric strength than SF 6 . These gases can be mixed with a buffer gas such as CO 2 and technical air to have suitable dielectric properties for high voltage insulation applications. With the rapid growth of HVDC networks, these gases may replace SF 6 in future HVDC apparatus such as GIS/GIL. This paper focuses on the study of the partial discharge (PD) characteristics of SF 6 and SF6 alternative gases with a protrusion defect on the high voltage conductor with DC voltage. The results show that, for calculated equivalent SF 6 dielectric strength under homogeneous electric field, a difference in terms of partial discharge inception voltage was noted for gas mixtures compared to SF 6 . This difference may be due to the influence of gas pressure under highly inhomogeneous electric field. FN-CO 2 and FK-Air mixtures showed the highest PD amplitude under positive polarity while it was higher under negative polarity for SF 6 . Moreover, PD repetition frequency is higher for SF 6 investigated alternative gases. The results can help for the design of SF 6 free HVDC GIS partial discharge monitoring system.

Development of smart online partial discharge monitoring system for medium voltage power cable

This paper presents, the development of smart online partial discharge (PD) monitoring system for medium voltage (MV) underground power cable. PD monitoring is the highly efficient tool to monitor insulation degradation for high voltage (HV) equipment in order to avoid failures or breakdown. Selection of improved technology and performance of PD detection sensor, effective measurement technique and smart user friendly of graphical user interface (GUI) system are contributed towards the development of efficient monitoring system. This paper addresses three main aspects which are needed in completing the monitoring system. They are, the use of Rogowski coil (RC) as detection sensor, processing unit using Alterra board and integrated with GUI PD monitoring system for underground cable using LabVIEW. The monitoring system is compared to the conventional method with the PD signal used is measured from the real on-site measurement in order to analyse its performance. The analysis is performed in MATLAB and LabVIEW software’s environment and the maximum peak of PD signal is enabled to view using GUI which complete with the location information of PD source. Furthermore, this paper has contributed to solve the problem in selection the simplified and practical approach for PD sensor and monitoring system. In the perspective of automated condition monitoring, this smart online PD monitoring system provides a complete solution towards latest industrial revolutions