Ultra High Frequency Partial Discharge Detection Research Articles

A Flexible Planarized Biconical Antenna for Partial Discharge Detection in Gas-Insulated Switchgear

This letter proposes a new ultrahigh frequency (UHF) flexible planar biconical antenna design method for partial discharge (PD) detection in gas-insulated switchgear. Continuous transverse branching increases the electrical length to expand the bandwidth and miniaturize the antenna. For comparison of the effects of the lateral branch structure model, the optimized dimensions of both designed antennas are 150×150 mm, and both use polydimethylsiloxane, flexible material with a thickness of 0.5 mm, as the substrate. The results show that the S11 of the antennas is less than −10 dB in the bands of 0.3–0.5, 0.64–1.25, and 1.4–3.0 GHz, and the planar biconical antennas still maintain good bandwidth and gain under different bending radii of 0, 150, 350, and 500 mm. The PD test platform was built to test the antenna. The experimental data show that the two flexible antennas designed in this letter can detect the PD-radiated electromagnetic wave signal with a discharge of 15 pc before and after the bending deformation and meet the UHF partial discharge detection requirements.

Research on UHF PD detection method based on improved DBN

With the development of power grid technology and the widespread application of gas insulator switchgear (GIS) equipment, the power supply reliability of the power system has been greatly improved, but the problem of partial discharge (PD) faults in GIS has always been prominent, seriously affecting the safe and stable operation of the power grid. How to quickly determine the type and cause of GIS discharge is the key to online PD detection. In this paper, in order to deal with the very complicated data processing of ultra-high frequency (UHF) PD, the time-consuming and low efficiency of manually judging the type of PD, a classification model of UHF PD system based on deep confidence network (DBN) is established and an automatic classification method for UHF PD based on improved DBN is proposed; the activation function Sigmoid is improved to effectively prevent the occurrence of the gradient disappearance problem; the optimized DBN parameters are used to train and classify data of different PD types. The classification accuracy rate of the test results reached 96.7%, realizing the rapid classification evaluation of UHF PD types.

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (I): Preprocessing and Localization Approaches

Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.

A Novel Partial Discharge Detection Method for Power Transformers On Site Adopting Its Component as Ultra-High Frequency Sensor

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.

Radiometric localisation of partial discharge sources inside Air‐insulated electrical substation

Monitoring partial discharge (PD) activities inside electrical substation using few ultra high frequency (UHF) sensors mounted around the periphery of the substation could be a useful and economical tool. In this work, an experimental study has been conducted in the laboratory and in substations to investigate the localisation ability of the UHF PD detection system. For localisation, one needs to calculate time difference of arrivals (TDOAs) between the UHF waveforms obtained from the multiple sensors mounted around the substation. After that these TDOAs are used for localisation of the PD sources. In this paper a novel joint entropy (JE)-based TDOAs calculation method is proposed and also compared with the existing techniques. For localisation of PD sources, a modified direction of arrival (DOA)-based method for the near field is proposed, as found to be suitable when the sensors are mounted around the substation. Obtained results in TDOA estimation using the proposed JE method is in line with other technique; however, it was found to provide consistent results and is useful in an extreme situation. Modified DOA-based localisation algorithm found to provide improved performance due to the elimination of approximation as required in previously reported far-field-based localisation system for the air-insulated substations.

Design of Ultra-Wide Band Metal-Mountable Antenna for UHF Partial Discharge Detection

This paper presents an ultra-wideband metal-mountable antenna used in the ultra-highfrequency (UHF) partial discharge (PD) detection. The designed antenna combines a coplanar waveguide antenna and multilayer substrates to effectively broaden its bandwidth when mounted on a conductive surface. These substrates consist of two layers of FR-4 dielectric substrates and two layers of the absorbing gasket, which is adapted to absorb the reflected electromagnetic signals. The parametric study is conducted to optimize antenna performance. A prototype is fabricated to confirm the design, and the measurement results show good agreement with the simulation. When mounted on the metal plate, the proposed antenna covers the UHF bandwidth of 400 MHz-3 GHz for S 11 <; -10 dB with a compact size (282 mm × 242 mm × 8.75 mm). As the thickness of the antenna is only 8.75 mm, the safety distance of electric insulation in electrical equipment will not be affected. A PD experiment has been carried out, and a reference antenna is used in the experiment for comparison. Compared with the conventional antenna, the proposed antenna has a better performance when mounted on the metal plate. It is shown that the designed antenna is a very good candidate for UHF PD detection inside of high-voltage equipment.

Novel Simulation Technique of Electromagnetic Wave Propagation in the Ultra High Frequency Range within Power Transformers.

Diagnoses of power transformers by partial discharge (PD) measurement are effective to prevent dielectric failures of the apparatus. Ultra-high frequency (UHF) method has recently received attention due to its various advantages, such as the robustness against external noise and the capability of PD localization. However, electromagnetic (EM) waves radiated from PD tend to suffer attenuation before arriving at UHF sensors, because active part of the transformer disturbs the EM wave propagation. In some cases, that results in poor detection sensitivity. To understand propagation and attenuation characteristics of EM waves and to evaluate the detection sensitivity quantitatively, a computational approach to simulate the EM wave propagation is important. Although many previous researches have dealt with EM wave simulation for transformers, validations of those simulations by comparing with the experimental ones have seldom been reported. In this paper, cumulative energies, signal amplitudes and propagation times of EM waves were measured using a 630 kVA transformer. EM wave propagation was computed using the time-domain finite integration technique and the results were compared with the experimentally obtained ones. These simulation results showed good agreement with the experimental ones. The results can serve as guidelines to improve the efficiency of UHF PD detection and offer the possibility to achieve optimal placement of UHF sensors in power transformers.

A combined preprocessing method for UHF PD detection based on kurtosis features

Ultra-high-frequency (UHF) partial discharge (PD) detection has been proved as a powerful tool to evaluate the status of electrical equipment. Unfortunately, on-site UHF detection suffers from intensive noise interference and great computational burden, which have long been the key bottleneck for its wide utilization in the field environments. In order to provide more valuable information for subsequent processing like PD location, pattern recognition, or severity assessment, a combined preprocessing method for UHF PD detection is proposed in this paper. This scheme integrates a novel Ensemble Empirical Mode Decomposition-based signal reconstruction method and a kurtosis-based pulse window extraction method. To demonstrate the effectiveness of the presented technique, both simulated and laboratory measured PD data are obtained and further tested. Results reveal that this preprocessing method can accurately detect and extract the PD pulse from raw data, which may lessen the computational burden for any further processing. In addition, more accurate and reliable feature information can be retained.

A time-domain characterization method for UHF partial discharge sensors

The performance of ultra-high frequency (UHF) sensors has a significant impact on the accuracy of UHF partial discharge (PD) detection. In this paper, a method is presented for the time-domain characterization of UHF sensors to provide a comprehensive evaluation of their performance in the time domain. The peak envelope value and the envelope width are adopted in the method as two characterization parameters for predicting the output waveforms of UHF sensors. However, due to the ill-posedness of deconvolution, the measurement of these parameters is highly sensitive to noise. Therefore, an automated optimization scheme is devised based on the multi-parameter filter technique to solve the problem. Two UHF sensors are then investigated using the proposed method. The measured characterization parameters are found to be dependent on the orientation of the sensors, which is discussed from the perspective of sensor geometry. Further, PD experiments are carried out using the same UHF sensors to validate the method. The actual performances of the sensors are in good agreement with their time-domain characterization parameters. The proposed method would be complementary to existing frequency-domain characterization methods and is useful for the comparison of different UHF sensors, especially when their output waveforms are utilized for PD detection.

UHF PD Experiments on Real Power Transformer and its Frequency Characteristics Results

A number of methods have been used in partial discharge (PD) detection and recognition. Among these methods, ultra-high frequency (UHF) detection and recognition based on a single signal have attracted much attention. In this paper, a UHF PD detection system is built, and samples are acquired through experiments on a real power transformer. The received signal is decomposed into different frequency ranges through wavelet packet decomposition (WPD). In each frequency range, a pattern recognition neural network is built, and then the relationship between the information in that frequency range and PD type is described. By comparing the recognition accuracy of these networks, frequency range selection is optimized. In this specific case (the specific transformer, PD sources, and UHF sensors), results show that low frequency (156.25 MHz to 312.5 MHz) and high frequency ranges (1093.75 MHz to 1250 MHz) contain the most information for recognition. If a PD detection recognition system is to be designed, then the performance around these frequency ranges should be given attention.

Application of On-Line Ultrasonic and UHF Partial Discharge Detection in 1000kV GIS

This paper introduces the detecting principle、range of application and interferencerejection of Ultrasonic And UHF Partial Discharge Detection In GIS(Gas Insulated Switchgear).In view of the advantages and disadvantages of ultrasonic and UHF detection,this paper proposesa quick joint detection method, which uses UHF (Ultra High Frequency) method to online detectthe suspected discharge range in real time, adopts ultrasonic method to determine the specificdischarge points, finally through a concrete instance in a 1000KV UHV(Ultra High Voltage)Substation verified the feasibility of the joint detection method.

Simulation Analysis Based on Finite-Difference Time-Domain Algorithm for GIS Partial Discharge Detection

Gas insulated switchgear (GIS) has been widely used in global power grids currently. However, there are several disadvantages of GIS which will cause partial discharge (PD) in the insulation and then make GIS shut down. In this paper, the application of the ultra high frequency (UHF)method for partial discharge monitoring is discussed in detail, including the placement of the UHF sensors , detection system and the different characteristics of the different defects in the PD signals diagram are analyzed as well. Finally, some relevant conclusions are drawn based on Finite-Diffrenee Time-Domain algorithm (FDTD) for simulation of the electromagnetic waves by UHF PD detection technology.

A combined noise-rejection method for UHF PD detection on-site

An important condition monitoring technology of power apparatus is ultra-high-frequency (UHF) partial discharge (PD) detection. This has been widely used in gas insulated substations, power transformers, etc. It benefits from its high sensitivity and low overlap with noise frequency. However, lots of field practices indicate that some interference, such as the mobile phone communication signals, the PD signals propagated from adjacent equipments, or some pulse-type interferences, do mix with the to-be-detected PD signals and generate great difficulties in UHF diagnosis. Based on various field experiences, this paper presents a combined UHF noise rejection method, which integrates the waveform correlation analysis, the time domain windowing method based on external noise signals, and the clustering method based on the magnitude ratio from two consecutive sensors. Experimental results show that this method can distinguish real PD signals from environmental noises from various sources effectively, and can separate different PD signals within the same apparatus in test.