Modelling of dielectric strength in long air gaps: application to a complex geometry

Konate, Lamine Boubacar; Béroual, Abderrahmane; Maciela, Frederic

doi:10.1088/1361-6463/ab642c

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The continuous leader propagation phase is an important physical process of long air gap discharge in the field of lightning attachment and external insulation of electric power systems [1,2], which could be attributable to two intertwined physical phenomena, namely, the leader channel and the streamer (corona) zone [2][3][4]. The streamer zone ahead of the leader channel is called the leader corona.…”

Section: Introductionmentioning

confidence: 99%

“…The streamer zone ahead of the leader channel is called the leader corona. Under the application of the external strong electric field, numerous electrons generated by ionization converged into the root of the streamers (the stem) [2], forming current with equivalent space charge left and heating the discharge channel till the critical temperature [3,4], the diffuse streamer zone is transited into the bright leader plasma channel, which forms the leader corona system and promotes further leader propagation [2,4]. In the long air gap discharge numerical models, the total space charge in the streamer zone defined by the vertex angle was calculated to simulate leader advancement based on the charge simulation method (CSM) [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Morphological characteristics of streamer region for long air gap positive discharge

Gu¹,

Huang²,

Fu³

et al. 2020

J. Phys. D: Appl. Phys.

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Knowledge of leader corona is the basis for long spark discharge modelling, which is critical to understand the underlying physics of lightning flashes and dielectric breakdown of long air gap widely utilized in power systems. This paper presents an accurate observation of the leader corona. The morphology of the leader corona region during the discharge was obtained using a high-speed camera and an image-enhanced charge-coupled device camera. Statistical results showed that the morphology of the streamer region varied at different discharge sub-phases. The first corona was a conical region with the average apex angle of 118.91°; the leader corona region behaved as a cone with an average vertex angle of 75.87° during the leader propagation process; the streamer area in the final jump was a combination of a cylinder and a cone with the average vertex angle of 82.61°. Noting that the obtained cone apex angle of the leader corona during the leader propagation phase was significantly smaller than that in photographical reports, and the observation results were related to the selected temporal resolution of the optical image apparatus. The discussion on the effect of the single-frame exposure time on observation results found that the average vertex angle of the conical streamer region gradually decreased with the decrease of the single-frame exposure time, and eventually tended to a stable value of about 75.96°. For comparison purposes, discharge experiments under two gap-distances demonstrated that on the premise of stable leader, the statistical distribution characteristics of the leader corona vertex angle were consistent, indicating the universal applicability to the simulation researches. Finally, it is found that the distortion of the electric field caused by the space charge explained the variation of the streamer area during the discharge process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Morphological characteristics of streamer region for long air gap positive discharge

Gu¹,

Huang²,

Fu³

et al. 2020

J. Phys. D: Appl. Phys.

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“…Insulation design of high voltage transmission line relies on the determination of air gap discharge voltage. With the development of extra-high voltage (EHV) and ultra-high voltage (UHV) transmission and transformation technologies [1,2] in the past few decades, numerous studies have been conducted on long air gap discharge characteristics [3][4][5], experimental observations of discharge phenomena [6][7][8], and theoretical modelling of air discharge process under switching or lightning impulse voltages [9][10][11]. These studies reveal the propagation and transition mechanisms of streamer, leader and final jump during long air gap discharge process, and also develop various physical and engineering models aiming at simulation of different discharge stages and prediction of 50% discharge voltage (U 50 ).…”

Section: Introductionmentioning

confidence: 99%

A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations

Qiu

Zhang

et al. 2022

IET Generation Trans & Dist

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It is a long‐term goal in external insulation studies to determine the discharge voltages of complicated engineering gaps by simulation methods. Based on the one‐to‐one correspondence between air gap structure and the static electric field (EF) distribution, this paper characterizes the transmission tower gap configuration by spatial EF features, which were used for machine learning to achieve switching impulse discharge voltage prediction. An interelectrode path and a conical zone between the energized sub‐conductor and the crossarm or the tower window were considered as EF regions strongly associated with gap breakdown, where 73 parameters were extracted to construct a feature set. Taking 15 extra‐high voltage (EHV) transmission tower gaps as training samples, with different gap distances and tower configurations, their EF features were input to a support vector machine (SVM) for model training to establish the relationships with discharge voltages. The trained SVM model was used to predict the impulse discharge voltages of 20 EHV and ultra‐high voltage (UHV) transmission tower gaps. The prediction results with different feature dimensions and various sizes of conical zones were compared to the experimental values, which demonstrate similar variation trends and acceptable errors. This study contributes to realize insulation strength calculation of engineering gaps.

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“…In order to reduce the costly and time-consuming experiments, many researchers tried to simulate the discharge evolution process and calculate air gap breakdown voltages by physical models [7][8][9][10]. These models were constructed on the basis of classical gas discharge theories like Townsend, streamer, and leader discharge theory, and simulate each stage of air discharge process including corona inception, streamer propagation, leader inception, and streamer-leader propagation, et al However, air gap discharge process is extraordinarily complex and affected by some random factors.…”

Section: Introductionmentioning

confidence: 99%

Sphere Gap Breakdown Voltage Prediction Based on ISSA Optimized BP Neural Network and Effective Electric Field Feature Set

Qiu

Song

2022

IEEJ Transactions Elec Engng

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Electric field distribution is a determinant factor of air gap breakdown voltage. This paper defined an effective electric field zone between the sphere gaps for feature extraction, and 29 parameters were extracted from this zone to reflect the electric field distribution. A data mining model was established by back propagation neural network (BPNN) for sphere gap breakdown voltage prediction, taking the electric field features as inputs. The maximum information coefficient (MIC) method was used to select features strongly correlated to the breakdown voltage, and a multi‐strategy improved salp swarm algorithm (ISSA) was applied to optimize the BPNN parameters to enhance the prediction performance. The standard sphere gaps provided in IEC 60052 were taken as training set and test set, which were divided according to the electric field nonuniform coefficients. The breakdown voltage prediction results of test sample sphere gaps demonstrate good agreements with the experimental values. With input features selected by the MIC method, the ISSA optimized BPNN model has a mean absolute percentage error of only 0.83% and a maximum relative error of 5.86%. By constructing the nonlinear mapping relationship between the effective electric field features and the insulation strength, this study provides a new approach to predict air gap breakdown voltage. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Modelling of dielectric strength in long air gaps: application to a complex geometry

Cited by 10 publications

References 15 publications

Morphological characteristics of streamer region for long air gap positive discharge

Morphological characteristics of streamer region for long air gap positive discharge

A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations

Sphere Gap Breakdown Voltage Prediction Based on ISSA Optimized BP Neural Network and Effective Electric Field Feature Set

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