Time-lag properties of corona streamer discharges between impulse sphere and dc needle electrodes under atmospheric air conditions

Okano, Daisuke

doi:10.1063/1.4791587

Cited by 6 publications

(2 citation statements)

References 17 publications

(17 reference statements)

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“…etc. The streamer inception voltage and the influence of the voltage rise time on this inception voltage were studied in [10][11][12]. Briels et al [13] used time resolved optical measurements to investigate the inception of positive streamers in air.…”

Section: Introductionmentioning

confidence: 99%

Distribution of inception times in repetitive pulsed discharges in synthetic air

Mirpour

Martinez

Teunissen

et al. 2020

Plasma Sources Sci. Technol.

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Knowing which processes and species are responsible for discharge inception is important for being able to speed up, delay, or completely avoid it. We study discharge inception in 500 mbar synthetic air by applying 10 ms long 17 kV pulses with a repetition frequency of 2 Hz to a pin-to-plate electrode geometry with a gap length of 6 cm. We record inception times for hundreds of pulses by measuring the time delay between the rising edge of the high-voltage (HV) pulse and the signal from a photo-multiplier tube. Three characteristic time scales for inception are observed: (1) 20 ns, (2) 25 μs, and (3) 125 μs. To investigate the underlying processes, we apply a low-voltage (LV) pulse in between the HV pulses. These LV pulses can speed up or delay discharge inception, and our results suggest that the three time scales correspond to: (1) free electrons or electron detachment from negative ions close to the electrode, (2) a process that liberates electrons from (quasi)-neutrals, and (3) the drift of an elevated density of negative ions to the ionization zone. However, each of these explanations has its caveats, which we discuss. We present a theoretical analysis of the distribution of inception times, and perform particle simulations in the experimental discharge geometry. Some of the observed phenomena can be explained by these approaches, but a surprizing number of open questions remain.

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Section: Introductionmentioning

confidence: 99%

Distribution of inception times in repetitive pulsed discharges in synthetic air

Mirpour

Martinez

Teunissen

et al. 2020

Plasma Sources Sci. Technol.

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“…In the ionization process, there is a delay between the discharge voltage and the discharge current, named time-lag (OKANO, 2013). The use of voltage impulses shorter than the time-lag allows to reach very high discharges voltages and, therefore, obtain electric fields greater than the breakdown limit (3 MV/m), under atmospheric conditions.…”

Section: Theorymentioning

confidence: 99%

High Sensitivity Envelope Detector Assisted by Microwave Photonics

Gonçalves¹,

Ivo²,

Coutinho³

2022

Smart Systems: Theory and Advances

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Theory and Advances is licensed under CC BY 4.0.This license requires that reuses give credit to the creator. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.The content of the work and its data in its form, correctness and reliability are the sole responsibility of the authors, and do not represent the official position of Amplla Publishing. Downloading and sharing of the work is permitted as long as credit is given to the authors. All rights to this edition were ceded to Editora Amplla.

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A Novel Analytical Expressions Model for Corona Currents Based on Curve Fitting Method Using Artificial Neural Network

Fan¹,

Liu²,

Wei³

et al. 2015

PIER M

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Abstract-The analytical expressions for corona discharge currents are usually represented by the mathematic models based on curve fitting method. For the complex mechanisms, none of these currently models can describe a measured corona current with arbitrary waveforms. A novel curve fitting method using BP neural network (BPNN) technique is applied to describe the mathematic model of the corona currents in time domain. The analytical expressions for the currents can be established via extracting the weights and thresholds parameters of the trained BPNN. The expressions all have the same structure which has only four types of parameters, and the structure is independent of the corona current waveforms. The curve fitting for the measured corona currents with arbitrary waveforms by different models was carried out, and the results were analyzed, which indicate that the BPNN method performs best. Compared with the current expressions fitted by the double exponential function and Gaussian function, the expressions by BPNN can fit the current waveforms with the lowest mean square error (MSE) in time domain and the highest accuracy to spectra of the currents in frequency domain. The proposed method is suitable for establishing a unified analytical expressions model for corona currents with arbitrary shapes.

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Time-lag properties of corona streamer discharges between impulse sphere and dc needle electrodes under atmospheric air conditions

Cited by 6 publications

References 17 publications

Distribution of inception times in repetitive pulsed discharges in synthetic air

Distribution of inception times in repetitive pulsed discharges in synthetic air

High Sensitivity Envelope Detector Assisted by Microwave Photonics

A Novel Analytical Expressions Model for Corona Currents Based on Curve Fitting Method Using Artificial Neural Network

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