Self-organizing feature map based unsupervised technique for detection of partial discharge sources inside electrical substations

This paper proposes a new method to locate the partial discharge (PD) sources inside the high-voltage power transformers with ultra-high frequency (UHF) sensors, based on the time difference of arrival (TDOA) database. A TDOA database includes the TDOA information of possible PD sources inside a power transformer, which is established under the construction of the system of the tested power transformer and the high accuracy of the minimum distance between a possible PD source and a UHF sensor. We employ the tangent transmission model of PD signals and the shortest path algorithm to solve the distance problem in case of an obstacle obstructing the transmission of PD signals. In order to verify the feasibility and accuracy of this new TDOA database method, we carry out two experiments. One experiment was conducted with a 110kV power transformer and the other was carried out with a platform for PD of electric power equipment. The experimental research results which are composed of location coordinates, transmission path and TDOA information indicate that the TDOA database method can locate single PD source as well as multiple PD sources simultaneously with the location error less than 0.3m.

A novel partial discharge (PD) detection method for transformers on site adopting its component as ultra-high frequency (UHF) sensor is proposed. The bushing tap, iron core, and high-voltage (HV) winding can be used as internal UHF sensor for UHF PD detection. The experiments are carried out on an 80-kV transformer model to prove the validity of this method. The results show that the proposed method has good detection sensitivity and anti-disturbance performance, and can measure the PD signals without additional sensor. It solves the problem of the lack of available unused valves for internal UHF sensor installation while the external sensor is susceptible to interference. It provides a novel method with application prospects for PD detection of transformers in field.

Combined in-oil PD sensor with AE and UHF methods for PD detection in transformer

Partial discharges (PD) measurement is a well-known technique for judgment of the condition of power transformer offline. Phase-resolved PD measurement is a normalized technique for PD tests, but noise, especially by PD-measurements onsite is sometimes difficult to reject, and a post-processing of a pattern is necessary to separate different sources. Higher-frequency-detection devices and processing strategies are useful for PD classification and identification. Recently, ultra-high-frequency (UHF) detection by means of antennas has been shown to be a promising detection system for both offline and online PD-measurements. However, it is necessary to assess whether PD source separation and classification is possible by means of pulse analysis because some additional UHF noise sources are coupled to the detected signals, and it is not clear whether different sources produce different pulses. In this study, the attenuation effect of the metallic tank of a power transformer on the inner PD activity when measured outside the tank was studied. Additionally, experiments were conducted to detect and characterize two different PD sources (internal and external discharges) using two antennas measuring the same phenomenon inside and outside of a transformer. It will also be shown that broadband UHF signals are useful for PD recognition and that a deep study of frequencies below 500 MHz can separate PD occurring inside from those occurring outside when measured with an antenna outside the tank.

This paper presents a novel self-sensing partial discharge (PD) detection method for power transformers that leverages the transformer's iron core as an ultra-high frequency (UHF) sensor. The method aims to address the limitations of traditional PD detection techniques, which either require intrusive internal sensors or are susceptible to external interference. The experiments conducted on a 220kV transformer model with a suspension discharge defect model demonstrate the high detection sensitivity and anti-interference performance of the proposed method. The results show that the iron core can effectively sense PD UHF signals, providing a reliable and practical approach for on-site PD detection in transformers. This PD detection method solves the problem that the internal UHF sensor has security risks while the external sensor is susceptible to interference. It provides a new method for PD detection of transformers in field.

Partial Discharge (PD) measurement is a precise diagnosis to give the knowledge about the condition of the insulation. There are several methods to detect and measure the PD such as Electrical, Acoustic, Optical, Chemical and Ultra High Frequency (UHF) detection methods. Among all these methods, UHF sensor plays crucial role to detect the PD, efficiently with high sensitivity. PD pulses tend to appear only in the time of nano-seconds, which pulses can be detected without any power outage by using high bandwidth antenna i.e. UHF sensor. UHF sensor is conical in shape with base diameter 2.5cm and axial length 10cm. This sensor can detect the electromagnetic interference signals without any background noise in the range of 300 MHz to 1.5 GHz. In this paper, the experimental work was presented to study the characteristics of PD by using the designed UHF conical monopole antenna with the help of high bandwidth digital oscilloscope connected through co-axial cables in two distinct lengths. FFT analysis was applied to deduce the frequency response of the PD signal and sensitivity of the antenna.

The most common pre-breakdown phenomenon in high voltage (HV) equipment is partial discharge (PD). The presence of PD activity is a warning indicator that the insulation deterioration would worsen over time, leading to catastrophic failure if the associated defects are not addressed early. Ultra-high frequency (UHF) has been proposed as a non-conventional method for detecting PD because of its resilience to external noise, high sensitivity, and online measurement potential. The UHF detection approach uses sensors to detect electromagnetic waves emitted by the PD source. The sensor is thus one of the most important parts of UHF PD detection. Microstrip patch antenna is widely used as a UHF sensor due to its merits of simple structure, compact profile, low cost, and ease of fabrication. This project deals with the development of the microstrip antenna for PD detection. To evaluate the antennas' performance, five antennas are built and simulated using Computer Simulation Technology (CST) Microwave Studio software. Return loss, voltage standing wave ratio (VSWR), and surface current are among the simulated results. These five antennas operate in the range of at least 80% of UHF and have the acceptable size that can be installed on the dielectric window or mounted on the flange.

The measurement of Ultra High Frequency (UHF) electromagnetic waves originated from a partial discharge (PD) source has been used for the identification and location of defects in power transformers. This method is based on the time-difference-of-arrival (TDOA) of the PD electromagnetic radiation using UHF sensors installed on the transformer. Then, it is possible to estimate the original position of the measured signals. However, it is known that the propagation of electromagnetic waves is affected by the reflections and refractions that occur inside the transformer, causing delays in propagation time and generating errors in the localization algorithms. For this reason, the refractions and reflections of the PD radiation on power transformers was studied in order to evaluate their effects on the delay of the signals arrival time. For this, the CST Microwave was used to simulate a power transformer model and to evaluate the effects of the core and windings in the wave propagation. Finally, the PD source was located from the measured signals and the error in the original source location was evaluated.

Partial discharge (PD) is one of the major factors causing insulation damage of gas insulated system (GIS). The conventional PD detection methods are too susceptible to disturbances and unsuitable for on-line monitoring. The ultra-high-frequency (UHF) PD detection technique can detect and locate the PD sources inside GIS by detecting electromagnetic wave emitted from PD source. The narrow band UHF method was emphasized in this paper, because it can avoid the electromagnetic interference by selecting the appropriate frequency. A PD detection system using narrow band UHF method was designed. The typical defects that can cause partial discharge in GIS were simulated in laboratory. Using this detection system the PD waveform of frequency domain and time domain could be gotten. The results show that the time domain waveforms of the same defect have very similar distribution characteristics and those of different defect have different ones. So it would be very convenient for the recognition of PD types.

Partial discharge (PD) phenomenon in high-voltage (HV) apparatus is a warning symptom of insulation degradation that can eventually lead to equipment breakdown. The ultra-high frequency (UHF) or electromagnetic method has been widely adopted to monitor the insulation condition of HV power equipment such as power transformers. To detect PD occurrence on HV power transformers, the UHF method is preferable due to its high immunity to external interferences such as background noise and electromagnetic interferences from telecommunication. The UHF antennas are used in the UHF method to detect the electromagnetic waves emitted by PD events inside the power transformer tank. Many studies about the design of UHF antennas to detect PD on power transformers have been conducted previously. However, these UHF sensors are still facing noise disturbances especially for on-line partial discharge measurement, in presence of high noise levels. To address interference issues affecting the UHF sensor sensitivity, this paper presents a novel UHF PD sensor called rectangular microstrip patch antenna or simply patch antenna. This type of antenna has a simple structure, low cost for fabrication, and ultra-wideband (UWB) frequency enabling it to detect PD signals in a wide frequency range within the UHF range (300MHz-3GHZ). To design, simulate and optimize the UHF antenna, the HFSS software was used. The FR4-epoxy substrate having a thickness of 1.6mm, and a dielectric constant of 4.4 was used to design and simulate this rectangular patch antenna. The perfect electric conductor (PEC) was used on the rectangular patch, feedline, and ground plane of the designed patch antenna. The proposed antenna has a low reflection coefficient, low VSWR < 2, a wide bandwidth, as suitable gain, and directivity enabling this antenna to be a suitable and promising sensor for PD detection on power transformers.

This paper proposes an optimal placement model of ultra-high frequency (UHF) sensors for accurate location of partial discharge (PD) in gas-insulated switchgear (GIS). The model is based on 0-1 program in consideration of the attenuation influence on the propagation of electromagnetic (EM) waves generated by PD in GIS. the optimal placement plan improves the economy, observability, and accuracy of PD locating. After synchronously acquiring the time of the initial EM waves reaching each UHF sensor, PD occurring time can be obtained. Then, initial locating results can be acquired by using the Euclidean distance measuring method and the extended time difference of arriving (TDOA) location method. With the information of all UHF sensors and the inherent topological structure of GIS, the locating accuracy can be further improved. The method is verified by experiment, showing that the method can avoid the influence of false information and obtain higher locating accuracy by revising initial locating results.

Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of power apparatus with severe social and economic consequences. Electromagnetic emissions gathered over ultra-high frequency (UHF) range (0.5 to 3 GHz) during PD events is widely investigated for health monitoring of high voltage (HV) apparatus. However, large bandwidth of PD induced UHF signals requires high speed digitizer and large storage space which have restricted field deployment of UHF technique for online PD detection. In this work, we present UHF based PD pulse detection framework for remote monitoring and logging of faults in dielectric insulation using ThingSpeak<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> cloud. PD events captured by a calibrated UHF sensor from HV PD test cell using the proposed online PD detection framework indicated throughput as high as 83.8% compared with reference UHF sensor measurements gathered by a digital storage oscilloscope. PD detection throughput was observed to be limited by the envelope detection threshold.<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> (10 mV) and cumulative propagation delay <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pd</sub> (∼1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μs</i>) in the architecture implementation. The proposed UHF sensing technique is low cost compared to conventional approaches, detects a wide range of insulation defects, and scalable for smart monitoring of HV assets using Internet of Things (IoT).

GIS (Gas insulated switching gear) is the power equipment with excellent dielectric strength and with economical value in high confidence and stability. Recently the reliability of GIS equipment has been questioned for its long usage, and the importance of the partial discharge on-line diagnosis system has been recognized. A partial discharge (PD) detection system is an effective means for the monitoring and evaluating the dielectric condition of Gas Insulated switchgear (GIS). The ultra-high-frequency (UHF) PD detection technique can detect and locate PD sources from the inside of GIS by detecting electromagnetic wave emitted from the PD source. Therefore, a real-time diagnostic system using UHF detection method was developed for this application. However, the signal of partial discharge occurring in SF6 gas is very weak and susceptible to external noises which mainly consist of PD in the air. Thus, it is very important to distinguish signal from the external noise signals. Unfortunately, these external noise signals and the partial discharge signals have very similar characteristics. For this reason, the external noise signal is not easy to remove. Therefore to solve this problem, we need the signal processing method to distinguish partial discharge signals from the external noise signals for the improvement of signal to noise ratio (SNR) and sensitivity. In this paper, we proposed the internal signal processing method to remove the external noise signals with built-in preamplifier and frequency conversion circuit.

In order to ensure the safe operation of the gas insulated switchgear (GIS), on-site impulse withstand voltage test is becoming more and more important. Oscillating impulse voltage (OIV) is very suitable for the test due to its high efficiency. By detecting the partial discharge (PD) when conducting on-site impulse withstand voltage test, the insulation state of GIS can be known overall. The pulse current, ultrasonic, and ultra high frequency (UHF) are the mainly PD detection methods up to now. Among them, the UHF method is the most suitable method to detect the PD under OIV. However, there are electromagnetic interference signals generated by the impulse voltage generator which will influence the UHF detection results during the on-site impulse withstand voltage test. In this paper, the interference signals for the PD detection using UHF methods are analyzed. Based on this, an interference elimination method by using multiple UHF sensors for PD detection under OIV is introduced. This method is tested both in a lab environment and in field. The analyses show the interference signals for the PD detection using UHF methods under OIV is mainly related to the sphere-gap switch of impulse voltage generator. The results of lab experiments and field tests both show that the interference elimination method by using multiple UHF sensors is suitable for the PD detection under oscillating impulse voltage.

The development of printable ultrahigh-frequency (UHF) antennas as partial discharge (PD) sensors for high-voltage equipment has been extensively studied. However, achieving ultrawideband (UWB) UHF PD sensors frequently requires larger sizes, unsuitable for certain applications requiring compact sensors for dielectric windows in HV equipment. This research objective is to obtain PD sensors with a wider bandwidth (0.3–3 GHz) and a compact size fitting a less-than-100mm-length gas-insulated switchgear (GIS) dielectric window. A circular patch microstrip antenna (CPMA) was chosen for its small size and potential for UWB performance. This paper discusses the design modification of the CPMA to obtain a wider bandwidth for PD detection in GIS. Simulations and lab-scale experimental verifications were conducted to evaluate the optimized sensor. The modified sensor, with a size of 60 × 73 mm², achieved a bandwidth of 3.08–3.14 GHz, a reflection coefficient of -44 dB, and several resonant frequencies of 0.3–2.3 GHz. This is a seven-time wider bandwidth compared to earlier bowtie antennas while keeping a dimension of less than 100 mm². These properties allow for efficient PD detection in GIS and other insulating media. Experimental results indicate the sensor's capacity to reliably detect and analyze PD signals while responding appropriately to variations in voltage. Doi: 10.28991/ESJ-2024-08-05-03 Full Text: PDF