Evaluation of the Effect of the Location of the Corona Measurement Devices on the Corona Inception Voltage Under HVDC Application

Sibanyoni, H.M.B; Walker, J. J.; Djeumen, Jules Simplice

doi:10.1007/978-3-030-31680-8_136

Cited by 2 publications

(1 citation statement)

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“…Research on predicting and diagnosing discharge plasma parameters using machine learning or artificial intelligence methods has attracted widespread attention [21][22][23]. In 2020, Sibanyoni [24] employed acoustic measurement instruments to diagnose a corona discharge device driven by highvoltage direct current.…”

Section: Et Almentioning

confidence: 99%

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

XIONG 熊,

LU 卢,

LIU 刘

et al. 2024

Plasma Sci. Technol.

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Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE=0.00571) and the highest R-squared value (R^2=0.93877). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

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