A useful data mining not only be efficient, but also be effective. For data mining algorithms, especially for classification, cluster analysis, decision tree, etc., an interactive processing between machine and user is necessary. Applying parallel and distributed computing technology, the object of efficient data mining can be reached. Together with parallel visualization platform, an interactive interface is essential in interactive data mining. In this paper, a scheme of interactive data mining support system in parallel computing environment is described. The design of interface in the system is illustrated.
Conditioning is an efficient technology to improve vacuum gap insulation, which is a collection of a series of breakdown events. Each breakdown event contains and be represented by one breakdown waveform with voltage and current. This article aims to classify the vacuum breakdown during the conditioning based on deep learning with its advantages on image feature extraction and recognition. The differences among pulsed current induced breakdown (PB), field emission induced breakdown (FEBD), and particle induced breakdown (PBD) during the prebreakdown period and breakdown numbers during breakdown period in voltage and current could be extracted and recognited by deep learning. Thus, four kinds of breakdown waveforms PB, PBD1 (PBD with one or several breakdowns), PBD2 (PBD with continuous breakdowns), and FEBD are separated for deep learning to recognize and classify. The classification takes about 10 s for each breakdown waveform and its accuracy is above 84.7%. The validation is confirmed from different perspectives.
Abstract This study investigates plasma-activated water (PAW) production enhancement using a Dielectric Barrier Discharge (DBD) reactor integrated with graphene aerogel inserts. It explores the potential for thermoelectric energy recovery. The research focuses on optimizing PAW generation by leveraging graphene aerogel’s high surface area and porosity to improve plasma-water interaction and increase the dissolution of reactive oxygen and nitrogen species (RONS). Additionally, the study examines the integration of thermoelectric materials within the reactor system to recover and utilize the thermal energy generated during plasma activation. A rotating DBD reactor design facilitates dynamic mixing and enhances contact between plasma and water. Key parameters are systematically evaluated, including rotation speed, aerogel characteristics, discharge conditions, and thermoelectric material performance. Results indicate that graphene aerogel significantly boosts PAW production efficiency while including thermoelectric materials, contributing to energy recovery and making the process more sustainable. This dual advancement in PAW technology offers potential applications in biomedical treatments, environmental remediation, and energy-efficient industrial processes.
Nowadays, dielectric barrier discharge (DBD) has been widely used in many fields, such as material surface treatment, environmental protection, plasma etching and ozone synthesis, because of its moderate electron density, temperature and energy. To study the DBD in atmospheric air, a one-dimension fluid model is presented in this paper, which uses a mixture of nitrogen and oxygen to simulate the air environment. The model is used to numerically investigate the influences of the amplitude and frequency of sinusoidal AC voltage on the dielectric barrier discharge characteristics. The voltage amplitude range from 9 kV to 15kV, and the voltage frequency range from 8 kHz to 12 kHz. The results clearly show that the DBD in atmospheric air is strongly affected by the amplitude and frequency of applied voltage. The average electron volume density reaches 10 17 /m 3 , which is consistent with the experimental value of atmospheric pressure dielectric barrier discharge. In addition, when the applied voltage frequency is 10kHz, obvious discharge current peaks can be observed at 11kV, and the discharge current shows single peak every half cycle as well as apparent positive and negative asymmetry. The amplitude of discharge current peaks varies from tens of milliamps to a few amperes with a pulse width of hundreds of nanoseconds. However, if the frequency of the applied voltage is reduced, multiple peaks can be observed in each cycle of discharge current. Furthermore, this paper also studies their influence on the electric field strength of the air gap, the surface charge of the dielectric, and the distribution of charged particles in the discharge region. Finally, the formation mechanism of multi-peak discharge current is analyzed.
A filter is a mainly component applied to reduce the discharge current low frequency oscillation in the range of 10–100 kHz. The only form of the filter in actual use involves RLC networks, whose design originates from the 1970s, but even now, researchers are unaware of the actual primary motivations for the resistor’s presence [S. Barral et al., AIAA Paper 2008-4632, 2008]. Therefore, the role of the resistor in the filter is experimentally studied and discussed through the analysis of control system and electric circuit theory. Experimental results and analysis indicate that the presence of a resistor makes the filter having the phase compensation function. The proper phase-angle and amplitude provided by the filter would increase or decrease the ion mobility and be helpful to balance the ion production in the discharge channel and then to decrease the fluctuation of the plasma density and lower the low frequency oscillation.
The prevailing method based upon the frequency dielectric spectroscopy (FDS) for state evaluation of the transformer solid insulation is faced with several limitations. Therefore, the aim of this paper is to report an available approach to accurately predict the aging state and moisture content of transformer solid insulation. The present findings reveal that the method based upon the grey relational analysis (GRA) is one of the available approaches to realize the comprehensive evaluation of transformer oil-immersed insulation. In the present work, the pressboard samples with various insulating states are prepared so as to extract the dielectric response characteristic parameters. Afterwards, the database used for state evaluation is constructed by using the above parameters. Then, the proposed method based on the GRA technique is utilized for state evaluation. The conclusions indicate that the reported method is effective, and the evaluation results are considerable.
Practical features of dissolved gases analysis (DGA) are selected and proposed from 62 key gases combinations through maximal information coefficient (MIC) to minimize the influences of random errors and relative percentages variation for field application. Then the Pearson correlation coefficient is employed to filter and optimize the feature set to reduce the redundancy of the selected features. Lasso regression is proposed to build a multi-dimension linear model of the selected features. In the multi-dimension model, the position in which the parameter changes drastically is defined as a change point, which contains specific information on the transformer's operation status. The case analysis demonstrates that the variation of selected features under abnormal status can be figured out from that of normal status prior to fault occurrence. The change point detection based on Lasso regression shows the least number of days between change point and time of failure and standard deviation (SD), which accurately reflects the location of the transformer fault in most scenarios. Therefore, the proposed technique provides an available approach for the dynamic fault prediction based on the dissolved gas data, showing the advantage of robustness, data-free training, and early warning.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)