Simultaneous Partial Discharge Research Articles

Simultaneous Partial Discharge Diagnosis and Localization in Gas-Insulated Switchgear via a Dual-Task Learning Network

Simultaneous Partial Discharge Diagnosis and Localization in Gas-Insulated Switchgear via a Dual-Task Learning Network

Influence of DBD treated nano‐Al2O3 on thermal and electrical insulation properties of epoxy nanocomposites at high temperatures

AbstractThis paper presents the effect of addition of dielectric barrier discharge (DBD) plasma‐treated nano‐Al2O3 particles on electrical treeing characteristics of epoxy resin (EP) at 30, 60, and 90°C, respectively. The electrical treeing and simultaneous partial discharge measurements, surface potential decay, scanning electron microscope, Fourier transform infrared spectrograms and differential scanning calorimetry are adopted to analyze the influence of plasma‐treated nano‐Al2O3 particles on the different insulation properties of EP and its composites. The results show that the high temperature adversely affects the electrical tree initiation, growth rate, and discharge activities of the EP nanocomposites. The DBD‐treated nano‐Al2O3 particles increase the electrical tree initiation voltage of the EP nanocomposites compared to the nanocomposite with the untreated nano‐Al2O3 particles. In contrast, the electrical tree growth rate decreases in the EP nanocomposite with the DBD treated nano‐Al2O3 particles at high temperatures. It is found that DBD treated nanoparticles affect the trap density and energy level of the composites by generating new chemical bonds, and then change their electrical properties. A charge carrier transport model is proposed to explain the obtained experimental results.

Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

A Passive RF Sensor for Detecting Simultaneous Partial Discharge Signals Using Time–Frequency Analysis

In this paper, a passive radio-frequency (RF) sensor has been presented for non-invasive radiometric detection of simultaneous partial discharge (PD) signals in high-voltage (HV) equipment. The sensor is a multi-resonator-based passive circuit with an antenna for capturing PD signals. The low-cost RF sensors are installed in HV sensing units for monitoring multiple units simultaneously. Each sensor captures PD signal and processes it with distinct spectral signatures as identification data bits and transmits the signal to a single sampling channel. From the captured RF signal, both the PD level and the source identification can be retrieved. Our proposed passive sensor addresses both aspects of PD monitoring, PD detection, and faulty source identification. Moreover, in identifying the data bits, the time–frequency analysis is utilized for superior detectability. This analysis enables the detection of multiple PD events separated by a defined time delay, although they occur at the same instance. Thus, the sensor can distinguish simultaneous PD occurrences from multiple sources, which is a novel contribution in PD detection. The proposed sensor system provides low-cost, passive condition monitoring of HV units in a distribution substation.

A hybrid algorithm based on s transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources

This paper presents a hybrid algorithm for separation of two simultaneous partial discharge (PD) sources of oil-paper insulation based on S transform (ST) and affinity propagation clustering (APC). Similarities between PD pulses are acquired by comparisons of the corresponding ST-amplitude (STA) matrices, which are input of APC to realize the PD pulses separation and obtain two sub-groups of PD pulses having similar time-frequency characteristics. A classification-based model for separation results validation are developed using a support vector machine with particle swarm optimization (PSO-SVM) classifier and 27 phase-resolved partial discharge (PRPD) statistical features. Artificial defect models are made to simulate two PD sources simultaneously active. Several PD data of different two simultaneous PD sources are acquired in laboratory and adopted for algorithm testing. It is shown that ST computes very fast and is suitable for online PD applications. The separation results of PD data produced in laboratory are verified by the developed validation model, which demonstrate that ST combined with APC can effectively eliminate pulse-shaped noises (PSN) and separate pulses of two simultaneous PD sources. Comparisons with typical separation methods from the state of the art provide better separation performance of the proposed ST combined with APC algorithm for two simultaneous PD sources. The obtained results in this work provide a solid basis for the data mining technique that can be used to facilitate PD diagnosis of transformers.

Simultaneous location of two partial discharge sources in power transformers based on acoustic emission using the modified binary partial swarm optimisation algorithm

One of the main methods for partial discharge (PD) source localisation in power transformers is acoustic emission measurements. This study describes a new method for detection and location of two simultaneous partial discharge sources in three-phase power transformer. In this method, acoustic signals are detected by sensors first and are then denoised using a wavelet transform. Finally, the two PD sources are localised using the modified binary partial swarm optimisation (MBPSO) method. To prove the efficiency of the two simultaneous PD localisations, the proposed algorithm is used to localise PD sources of the arc furnace transformer at Isfahan's Mobarakeh steel company. For this purpose, the PD localisation problem converts to an optimisation problem. To prove the efficiency of the MBPSO algorithm, its performance is compared with a genetic algorithm. The PD localisation results confirm the efficiency of the proposed method for the detection and location of PD sources.

On partial discharge measurement in dielectric liquids

Partial discharge detection in dielectric liquids requires particular PD measurement techniques and instruments. Simultaneous partial discharge (PD) measurements that are employed on insulating liquids, using dual narrow/wide band detectors, are described. Narrow band PD detectors, which are of the integrating type are suitable for the measurement of the total charge transfer in pC of the overall discharge event. Wide band measurement techniques permit the acquisition of the discrete PD pulses in dielectric liquids, that comprise the overall PD pulse burst signal. These PD pulse bursts are comprised of discrete high frequency pulses of usually ascending magnitude charge transfers. The time position of these pulses within the pulse burst is determined by the times at which the Paschen's minima occur across the expanding cavity due to the small size of the microcavities. In order for them to undergo discharge requires enormously high internal pressures (ca 10 MPa) to ensure a sufficient number of charge carriers. The life duration of these cavities is of the order of several's, with the cavity collapsing, when its size reaches dynamic instability.

Identification of two simultaneous partial discharge sources in an oil-pressboard insulation system using acoustic emission techniques

Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharge (PD) causes insulation degradation and premature failure of insulation. In power apparatus, more than one PD source may be active simultaneously. The nature of insulation degradation for multiple PD sources is different from that due to single PD source. Therefore, it will be helpful for severity assessment of insulation degradation, if the number of active PD sources are identified and classified. This paper presents a method for identification and classification of two simultaneously active PD sources using acoustic emission techniques. The acoustic emission (AE) signals are measured for laboratory simulated PD in an oil-pressboard insulation system for three different electrode systems. The measurements of partial discharge acoustic emission (PDAE) signals are carried out for single PD source and for two simultaneous PD sources. The measured signals are analyzed using discrete wavelet transform (DWT), box counting fractal dimension and lacunarity. Box counting fractal dimension and lacunarity are calculated for DWT decomposed signal of major frequency band. Energy distribution in different frequency bands of DWT decomposed signal along with box counting fractal dimension and lacunarity is used for classification of two simultaneous PD sources.

Retardation of electrical tree propagation by resin degradation

AbstractIn order to improve insight into the effect of delaminations between the conductor and the groundwall insulation on the ageing of high voltage rotating machinery insulation, the influence of the polymer degradation on tree growth has been studied. Simultaneous partial discharge measurements and photograhic observations have been conducted in order to investigate the effect of the degradation of the resin on electrical tree propagation. The photographic observation indicates that the degradation of the resin retards the tree propagation after the tree having failed to penetrate a mica barrier. As the resin is degraded and in part rendered conducting, the partial discharges change both in magnitude and in terms of phase resolved partial discharge patterns. These partial discharge measurements are promising to improve insight into the processes involved in winding insulation ageing, when photographic observations are unattainable. Moreover the differences found between various resins offer an explanation why various types of winding insulation have been found to differ in their capability to withstand thermal cyclic ageing. Copyright © 2006 John Wiley & Sons, Ltd.