Partial Discharge Detection Using a Spherical Electromagnetic Sensor

Passive Wireless Partial Discharge Sensors with Multiple Resonances

Partial discharge (PD) is the dominant insulating defect in Gas-Insulated Switchgear (GIS). The existing detection methods are mainly divided into built-in wire-connected disk antennas with destructive drilling and external ultra-high frequency antennas with poor anti-interference ability. This research introduces a passive wireless PD sensor implanted inside GIS on the observation window. The sensor is implemented by a sheeting branch-inductor with multiple resonances which is able to enhance detection sensitivity. A coaxially aligned readout circuit, positioned outside the GIS, interrogates the PD sensor to wirelessly obtain the PD signal. The proposed sensing scheme improves signal-to-noise ratio and ensures minimal disruption to the electric field distribution inside GIS. An experimental setup was established in a controlled laboratory environment to benchmark the multi-resonant sensor against the commercial UHF sensor. A 2.5-times enhancement of signal strength was observed. Since our sensor was implanted inside the GIS, a high signal-to-noise ratio (68.82 dB) was obtained. Moreover, we constructed a wireless calibration test to investigate the accuracy of the proposed sensor. The precision of the signal test was as high as 0.72 pC. The pulse phase distribution information was collected to demonstrate a phase-resolved partial discharge (PRPD) pattern. The experiment results validate the effectiveness of the proposed method and demonstrate excellent performance in PD detection.

Time Evolution of Partial Discharges in a Dielectric Subjected to the DC Periodic Voltage

Partial discharge (PD) detection can be considered one of the most useful tools for assessing the insulation conditions of the power apparatus in high-voltage systems. Under AC conditions, this analysis is widely employed in online and offline tests, such as type tests or commissioning, and can be carried out by applying the phase-resolved PD (PRPD) method, since the patterns can give information about the defect classification. Under DC voltages, the classic pattern recognition method cannot be performed, and the measurements show complexities related to the nature of the phenomena. For this reason, to date, a standard for PD measurements under DC does not exist. In previous papers, a new method for PD detection under DC stress voltages has been proposed by the authors. It is based on the application of a direct current periodic (DCP) waveform useful in obtaining PRPD patterns. The dependence of partial discharge inception voltage (PDIV) and PD repetition rate (PDRR) on the δ shape parameter of the DCP for different materials, as well as the capability to recognize different discharge phenomena, provided valid indications on the behavior of PD in the transition from AC to DC. The aim of this paper is to evaluate the time dependence of PD occurring in a dielectric by applying the DCP waveform. In our previous studies, the investigations were focused on the PD behavior under different values of the DC voltage periodic part. In another work, the DCP waveform with both positive and negative polarity was applied to several dielectric materials. In the proposed work, instead, the DCP waveform is applied for a long time in order to observe its effect on the PD behavior for 72 h. In this way, due to the space charge accumulation phenomenon, the aging effect, also due to the space charge accumulation phenomenon, is evaluated. The methodological approach was to acquire PRPD patterns over time and evaluate their trends in comparison with the sinusoidal case. The experimental results show that, with a DCP waveform having δ = 0.6, the aging effect similar to that provided by pure DC stress is observed, while the acquired PRPD patterns are easily interpretated, as in the AC case.

Review on Detection and Analysis of Partial Discharge along Power Cables

Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for PD analysis in power cables. Afterwards, PD detection is addressed in the aspects of off-line test, on-line test, and sensors. PD analysis is discussed in detail. Specifically, related quantities and algorithms for PD analysis are outlined. PD characteristics with affecting factors, e.g., dielectric type, load, and applied voltage are discussed. Experience on PD development trend with measurements in field is analyzed. Based on the comprehensive review, challenges of PD detection and analysis along a power cable are proposed.

An IoT Hardware Platform Architecture for Monitoring Power Grid Systems based on Heterogeneous Multi-Sensors

Partial discharge (PD) is a major indicator of various failures in power grid systems. PD exhibits a physical occurrence where a localized electrical discharge happens in insulation materials. This phenomenon causes damage to the insulating parts and, in various circumstances, leads to complete insulation breakdown. As a consequence, it can produce more costly outcomes such as abrupt outages or lost production. Therefore, PD detection plays a vital role in preventing insulation failure. In this work, we propose a system that utilizes heterogeneous sensors for the PD detection along with multi-sensor interface, real-time advanced denoise processing, flexible system operation, and Bluetooth-low-energy (BLE)-based ad hoc communication. Among the variety of heterogeneous sensors, several are developed by the application of nanomaterials and nanotechnology, thus outperforming the regular types. The proposed system successfully identifies the presence of PD from sensor signals using a microprocessor-based processing system and effectively performs an advanced denoising technique based on the wavelet transform through field-programmable-gate-array (FPGA)-based programmable logics. With the development of the system, we aim to achieve a solution with low cost, high flexibility and efficiency, and ease of deployment for the monitoring of power grid systems.

The Effect of the Axial Heat Transfer on Space Charge Accumulation Phenomena in HVDC Cables

To date, it has been widespread accepted that the presence of space charge within the dielectric of high voltage direct current (HVDC) cables is one of the most relevant issues that limits the growing diffusion of this technology and its use at higher voltages. One of the reasons that leads to the establishment of space charge within the insulation of cables is the temperature dependence of its conductivity. Many researchers have demonstrated that high temperature drop over the insulation layer can lead to the reversal of the electric field profile. In certain conditions, this can over-stress the insulation during polarity reversal (PR) and transient over voltages (TOV) events accelerating the ageing of the dielectric material. However, the reference standards for the thermal rating of cables are mainly thought for alternating current (AC) cables and do not adequately take into account the effects related to high thermal drops over the insulation. In particular, the difference in temperature between the inner and the outer surfaces of the dielectric can be amplified during load transients or near sections with axially varying external thermal conditions. For the reasons above, this research aims to demonstrate how much the existence of “hot points” in terms of temperature drop can weaken the tightness of an HVDC transmission line. In order to investigate these phenomena, a two-dimensional numerical model has been implemented in time domain. The results obtained for some case studies demonstrate that the maximum electric field within the dielectric of an HVDC cable can be significantly increased in correspondence with variations along the axis of the external heat exchange conditions and/or during load transients. This study can be further developed in order to take into account the combined effect of the described phenomena with other sources of introduction, forming, and accumulation of space charge inside the dielectric layer of HVDC cables.

Quantitative Analysis of the Sensitivity of UHF Sensor Positions on a 420 kV Power Transformer Based on Electromagnetic Simulation

With an increasing interest in ultra-high frequency (UHF) partial discharge (PD) measurements for the continuous monitoring of power transformers, it is necessary to know where to place the UHF sensors on the tank wall. Placing a sensor in an area with many obstructions may lead to a decrease in sensitivity to the UHF signals. In this contribution, a previously validated simulation model of a three-phase 300 MVA, 420 kV power transformer is used to perform a sensitivity analysis to determine the most sensitive sensor positions on the tank wall when PD activity occurs inside the windings. A matrix of UHF sensors located on the transformer tank is used to perform the sensitivity analysis. Some of the windings are designed as layer windings, thus preventing the UHF signals from traveling through them and creating a realistic situation with very indirect propagation from source to sensor. Based on these findings, sensor configurations optimized for UHF signal sensitivity, which is also required for PD source localization, are recommended for localization purposes. Additionally, the propagation and attenuation of the UHF signals inside the windings and the tank are discussed in both oil and air.

Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.