A Comprehensive Numerical Model of Steady-State Negative Corona for Coaxial Cylindrical Electrode in Atmosphere Air

Yi, Yong; Wang, Liming; Chen, Zhengying

doi:10.1109/tps.2017.2649680

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Continuity equations of the electrons, the positive ions and the negative ions [23][24][25] (10) In the process of solving the coaxial cylinder electrode model, the Runge-Kutta technique and the central finite difference method are used to calculate the equations. The relative error of the potential gradient and space charge density are the convergent conditions in the numerical iterative calculation.…”

Section: B Calculation Of Electric Field Features and Space Charge Fmentioning

confidence: 99%

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Chen

Wang

2020

IEEE Access

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Atmosphere ion mobility is an important electrical parameter related to the filamentary ion flow field of high voltage direct current (HVDC) transmission lines and other characteristics of the glow and streamer corona discharge. Many machine learning (ML)-based algorithms have already been widely used in the prediction of air discharge. This paper proposes a genetic algorithm (GA)-support vector regression (SVR) combined with kernel principal components analysis (PCA) to predict the ion mobility, involving dimensionality reduction, feature selection, parameter optimization of SVR. Kernel PCA could reduce the dimension of data and GA with adaptive probability parameters is employed to optimize the parameters of SVR model. An improved parallel-plates ion generator is employed to produce corona discharge and then measure the saturation ion current density and then obtain the training data and testing data of ion mobility. The prediction results show that the proposed algorithm outperforms the other methods in terms of mean relative error and mean squared error criteria. In addition, the parameters of model and data features have a major influence on the performance of the prediction algorithm. Based on the measured data and reference data, the prediction result obtained on ion mobility under different humidity shows a satisfactory generalization and effectiveness of proposed model for the prediction.

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Section: B Calculation Of Electric Field Features and Space Charge Fmentioning

confidence: 99%

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Chen

Wang

2020

IEEE Access

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“…Corona discharge plays a vital role in a wide range of industrial applications, such as electrostatic precipitator [1], plasma reactor [2], ionic wind [3], plasma propulsion engine [4], or plasma spectroscopy [5]. The corona discharge commonly appears or forms when the surface electric field of the electrode reaches the onset electric field, which is commonly realized by the rod-plane electrode or the coaxial cylindrical electrode [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

LSTM Neural Networks With Attention Mechanisms for Accelerated Prediction of Charge Density at Onset Condition of DC Corona Discharge

Chen

2022

IEEE Access

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The onset process of corona discharge is naturally nonlinear and dynamic. The conventionally physical-based onset model and numerical computation of onset charge distribution are hampered by the computational power and given time. Here, in order to efficiently model this highly nonlinear dynamic process, a long short-term memory (LSTM) neural networks with attention mechanisms is proposed for accelerated charge density prediction under different atmospheric conditions, which adaptively choose charge-related input variables at each time step and hidden states relating to charge density all time steps. Our results demonstrate that this well trained model could make instant predictions with high accuracy under given target atmospheric conditions. Results show that the proposed model substantially reduces the computing time compared to physical-based methods. This work provides insights into applying LSTM neural networks to the charge density prediction of other discharge modes as well. INDEX TERMSNeural networks, charge density, corona discharge, onset condition, prediction model, LSTM YONG YI received Ph.D. degree in electrical engineering from the

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Investigation of the Effects of Ion Diffusivity on the Ion Flow Field Simulation

Choopum

Techaumnat

2022

2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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A Comprehensive Numerical Model of Steady-State Negative Corona for Coaxial Cylindrical Electrode in Atmosphere Air

Cited by 3 publications

References 32 publications

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

LSTM Neural Networks With Attention Mechanisms for Accelerated Prediction of Charge Density at Onset Condition of DC Corona Discharge

Investigation of the Effects of Ion Diffusivity on the Ion Flow Field Simulation

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