Breakdown Voltage Prediction Research Articles

AC and Lightning Breakdown Voltage Prediction Based on PD Measurements

AC and Lightning Breakdown Voltage Prediction Based on PD Measurements

Powernet: SOI Lateral Power Device Breakdown Prediction With Deep Neural Networks

The breakdown performance is a critical metric for power device design. This paper explores the feasibility of efficiently predicting the breakdown performance of silicon on insulator (SOI) lateral power device using multi-layer neural networks as an alternative to expensive technology computer-aided design (TCAD) simulation. In this work, we propose the first breakdown performance prediction framework, PowerNet, for SOI lateral power devices, based on deep learning methods. The framework can provide breakdown location prediction and breakdown voltage (BV) prediction by utilizing a two-stage machine learning method. In addition, it demonstrates 97.67% accuracy on breakdown location prediction and less than 4% average error on the BV prediction compared with TCAD simulation. The proposed method can be used to measure changes in performance caused by random variability in structural parameters during manufacturing process, allowing designers to avoid unstable structural parameters and enhance design robustness. More importantly, it can significantly reduce the computational cost when compared with the TCAD simulation. We believe the proposed machine learning technique can significantly speedup the design space exploration for power devices, eventually reducing the overall product-to-market time.

Modelling and experimental verification of barrier effect on breakdown voltage of transformer oil using Box-Behnken Design

Barriers play an important role in enhancing the dielectric strength of the transformer insulating oil since they block the contaminated particle movement and prevent space charge formations that incept the oil breakdown. The effect of barrier factors (gap space (d), the barrier location relative to the high voltage electrode (a/d) %, and the barrier diameter (D)) was demonstrated concerning the insulation performance of the transformer oil for a hemisphere-hemisphere gap under AC voltage. The breakdown voltage (VBD) prediction due to the barrier effect was developed to cut the experiments cost. The Box-Behnken Design (BBD) mathematical model was developed with only 15 experiment runs that were enough to build the relationship between (d, (a/d) %, and D) and VBD. The optimal values of d, (a/d) %, and D that achieved the highest value of VBD was provided. The optimal values of (d), (a/d) %, and (D) were 3 cm, 25% and 10 cm respectively that gave the highest value of the VBD 106.87 kV. In this study, The BBD mathematical model was used and compared with a central composite face (CCF) model. The results indicated that the mathematical model using BBD gave minimum prediction errors compared with CCF model.

Switching impulse discharge characteristics of UHV transmission line air gaps

The design of external insulation for transmission lines is usually based on real‐type experiments. With the increasing voltage level of the transmission lines, in order to better design the experiments and reduce the experiment workload in the future, this paper studies the switching impulse discharge characteristics of air gaps of an ultrahigh‐voltage (UHV) transmission line. A long air gap switching impulse breakdown voltage prediction method based on the leader discharge mechanism is proposed in this paper, and a continuous leader inception model is fitted for the air gaps in UHV transmission lines by calculating the factor R of the conductor–plane gaps. The method and the model can effectively predict the air gap breakdown voltage of transmission lines. The influence of tower width and conductor structure on air gap breakdown characteristics of UHV transmission lines is studied by the method and the model proposed this paper. The results show that, as the width of the tower increases, the breakdown voltage decreases, and as the gap length increases, the influence of the tower width on the breakdown voltage decreases. The conductor structure has no obvious influence on the discharge characteristics of transmission line air gaps. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Feature Set for Structural Characterization of Sphere Gaps and the Breakdown Voltage Prediction by PSO-Optimized Support Vector Classifier

Air insulation strength relates closely to the electrostatic field distribution of the gap configuration. To achieve insulation prediction on the basis of electric field (EF) simulations, the spatial structure is characterized by a feature set including 38 parameters defined on a straight line between sphere electrodes. A support vector classifier (SVC) with particle swarm optimization (PSO) is used to establish a prediction model, whose input variables are those features. The EF nonuniform coefficient f of each sample gap is calculated and used for training sample selection according to the ranges off values. Trained by only 11-sample data, the PSO-optimized SVC model is employed to predict the power frequency breakdown voltages of 260-sphere gaps with a wide range of structure sizes. The predicted values coincide with the standard data given in IEC 60052 very well, with the same trend and minor relative errors. The MAPEs of the five predictions with different training sets are within 2.0%. The model is also effective to predict the breakdown voltages of Φ9.75-cm sphere-Φ6.5-cm sphere gaps, whose MAPEs are within 2.6%. The results demonstrate the effectiveness of the EF feature set and the generalization ability of the SVC model under the case of limited samples. This paper lays the foundation for estimating the dielectric strength of other air gaps with similar structures.

Electrical breakdown at high pressures: a Paschen law function and compressible gas dynamics

A search is ongoing for compressed gas insulants of comparable performance with sulphur hexafluoride, which will avoid its severe global warming potential. This work will be assisted by a generic understanding of the factors that limit the high voltage withstanding strength of compressed gases. Most available experimental data relate to high-pressure SF6 breakdown, but no quantitative physical model for breakdown voltage prediction has been developed other than empirical algorithms. The paper shows that a new form of the classical Paschen law can account for these data, which will provide a useful guide for the economical testing of candidate insulants and for the general design and physical limitations of equipment using compressed gases. The paper also examines the transient changes in compressible gas density that arise from shock wave development, which may influence high-pressure breakdown and determine the breakdown voltage.

Sensitivity of modeled microscale gas breakdown voltage due to parametric variation

Device miniaturization increases the importance of understanding and predicting gas breakdown and electrical discharge thresholds. At gap sizes on the order of ten microns at atmospheric pressure, field emission drives breakdown rather than Townsend avalanche. While numerical and analytical models can demonstrate this transition, a quantitative understanding of the relative importance of each parameter remains unclear. Starting from a universal model for gas breakdown across the field emission and Townsend avalanche regimes [A. M. Loveless and A. L. Garner, Phys. Plasmas 24, 113522 (2017)], this paper applies the concept of error propagation from ionizing radiation measurements to determine the relative impact of each factor on the predicted breakdown voltage. For limits of both large and small products of the dimensionless ionization coefficient, α¯, and gap distance, d¯, the electrode work function has the largest relative effect on the predicted breakdown voltages with a deviation of 50% in the work function resulting in an uncertainty in the calculated breakdown voltage of ∼84% for both α¯d¯≫1 and α¯d¯≪1. This quantifies the significance of nonuniformities in material surfaces and changes in the surface structure during multiple electric field applications and help predict the breakdown voltage for small gaps, motivating better electrode characterization both initially and during repeated operation.

Electrostatic field features on the shortest interelectrode path and a SVR model for breakdown voltage prediction of rod–plane air gaps

Electrostatic field distribution has a decisive influence on the strength of air insulation. To predict air-gap breakdown voltages according to electric field calculation results, a set of electrostatic field features is defined on the shortest interelectrode path of the rod–plane gap. These features are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the output breakdown voltage. Trained by small-sample data randomly selected according to the distribution ranges of the electric field non-uniform coefficient, the SVR model is applied to predict the breakdown voltages of rod–plane gaps with the rod diameter of 20, 25 and 30 mm, and the gap distance ranging from 1 to 9 cm. The predicted results agree well with the experimental data, while the mean absolute percentage errors of the six predictions are within 2.5%. Furthermore, the Φ27 mm rod–plane gap breakdown voltages are also predicted, and the results fall in the range between those of Φ25 and Φ30 mm rod–plane gaps, which conform to the actual law. The results validate the validity of the electrostatic field features and the generalisation performance of the SVR model.

Breakdown in short rod-plane air gaps under positive lightning impulse stress

Prediction of withstand voltages in air-insulated systems are made on the basis of empirical models that are not sufficiently accurate for complex geometries. Better understanding of the spatiotemporal development of electrical discharges is necessary to improve the present models. Discharges in lightning impulse stressed 20–100 mm rod-plane gaps are examined using a highspeed camera, photo-multiplier tubes (PMTs) and a highbandwidth current measurement system. The images and measurements of gaps larger than 20mm show a fast initial streamer discharge with a current rise time of some tens of ns, followed by a dark period of a few μs and a propagation of a slower leader-type channel leading to breakdown. The breakdown mechanisms in the shortest gaps are faster and geometry dependent, probably occuring by heating of initial streamer channels. Different light filters used with the PMTs indicate that all parts of the leader-type discharge development emit light over a spectrum from UV to IR. The initial discharges emit low amounts of warm light and IR compared to the leader-type channel. Finally, it is suggested that empirical breakdown voltage prediction models should be interpreted in light of the leader-type breakdown mechanism.

Breakdown in short rod-plane air gaps under positive lightning impulse stress

<p>Prediction of withstand voltages in air-insulated systems are made on the basis of empirical models that are not sufficiently accurate for complex geometries. Better understanding of the spatiotemporal development of electrical discharges is necessary to improve the present models. Discharges in lightning impulse stressed 20–100mm rod-plane gaps are examined using a highspeed camera, photo-multiplier tubes (PMTs) and a highbandwidth current measurement system. The images and measurements of gaps larger than 20mm show a fast initial streamer discharge with a current rise time of some tens of ns, followed by a dark period of a few μs and a propagation of a slower leader-type channel leading to breakdown. The breakdown mechanisms in the shortest gaps are faster and geometry dependent, probably occuring by heating of initial streamer channels. Different light filters used with the PMTs indicate that all parts of the leader-type discharge development emit light over a spectrum from UV to IR. The initial discharges emit low amounts of warm light and IR compared to the leader-type channel. Finally, it is suggested that empirical breakdown voltage prediction models should be interpreted in light of the leader-type breakdown mechanism.</p>

Generalization of Microdischarge Scaling Laws for All Gases at Atmospheric Pressure

The failure of traditional breakdown theories to predict breakdown voltage $V_{b}$ for microscale gaps motivates studies for more accurate approaches. We generalize scaling laws previously derived for argon at atmospheric pressure to model any gas at atmospheric pressure for gap distances from 100 nm to $30~\mu \text{m}$ and perform a matched asymptotic analysis to derive simple analytic equations for $V_{b}$ at critical limits. We obtain excellent agreement between numerical solutions, analytic equations, particle-in-cell simulations, and experimental data. Furthermore, we assess the relative contributions of field emission and Townsend discharge for gaps larger than 100 nm, demonstrating that the field emission contribution exceeds the Townsend contribution for gap distances smaller than several microns. For example, with argon, nickel electrodes, and a field enhancement factor of 55, the field emission contribution exceeds the Townsend contribution below $10~\mu \text{m}$ .

A method for breakdown voltage prediction of short air gaps with atypical electrodes

The most determinant factor for the dielectric strength of an air gap is the inhomogeneity of the electric field. This paper attempts to establish the internal relation between the electric field distributions of air gaps with different electrode geometries, and so as to predict the breakdown voltage of the air gap with atypical electrodes. A classification model is established based on support vector machine (SVM), which considers that the breakdown or withstand of an air gap under an applied voltage can be viewed as 1 or -1. 28 features are defined and applied to characterize the electric field distribution of an inter-electrode air gap. Taking these features as input parameters of the SVM model, the grey correlation between the air gap breakdown voltage and its electric field distribution can be trained by some training samples, which are selected from typical air gaps like sphere-sphere and rodplane gaps according to the electric field distribution similarity along the shortest discharge path. The SVM model is applied to predict the breakdown voltages of atypical air gaps including the serial gaps, the ring gaps and the stranded conductor gaps. The predicted results are in good agreement with the experimental data, and the mean absolute percentage errors of these gap arrangements are within 5.4%. This method provides an alternative for the breakdown voltage prediction of air gaps with complex geometries, and therefore helps minimizing the required test work.

Hybrid prediction of the power frequency breakdown voltage of short air gaps based on orthogonal design and support vector machine

This paper attempts to establish the grey correlation between the breakdown voltage of air gap and its energy storage status before discharge inception. A new method is presented for the breakdown voltage prediction of short air gaps. This method is based on an orthogonal design array and support vector machine (SVM). A hybrid model is established to predict the power frequency breakdown voltage of three typical air gaps including sphere-sphere, rod-plane, and rod-rod gaps. The input data are fifty parameters extracted from the static electric field distribution of the air gap, and the output of the model is whether the gap will breakdown under a given voltage. This model is trained by 9 samples selected by L 9 (33) orthogonal array and applied to predict the breakdown voltage of 90 test samples. With effective feature dimension reduction and parameter optimization of the SVM model, the predicted results coincide well with the experimental data, and the mean absolute percentage error (MAPE) is only 3.9%. The SVM model is also applied to predict the breakdown voltage of some atypical short air gaps including sphere-plane gaps, rod-sphere gaps, sphere-sphere gaps with different diameters, and rod-plane-sphere gaps. The MAPEs are respectively 4.3, 3.2, 4.2, and 6.4%, which verifies the validity and generalization performance of the proposed method. This model provides a new way to gain the critical breakdown voltage of air gaps, and therefore contributes to reducing the required test work.

A prediction method for breakdown voltage of typical air gaps based on electric field features and support vector machine

Breakdown voltage of the air gap is of vital importance for the design of the external insulation in high-voltage transmission and transformation projects. In this paper, a new prediction method for the breakdown voltages of typical air gaps based on the electric field features and support vector machine (SVM) was proposed. According to the finite element calculation results of static electric field distribution, the electric field values in the whole region, discharge channel, surface of the electrode and the shortest path were extracted and post-processed, which constituted the electric field features characterizing the gap structure. Then, the breakdown voltage prediction model of the air gap was established by using electric field features as the input parameters to SVM, and whether the gap breakdown would happen as the output parameters of SVM, which changing the regression problem to a binary classification problem. This model was applied to predict the power frequency breakdown voltages of different short air gaps including sphere-sphere gaps, rod-plane gaps, sphere-plane gaps and sphereplane- sphere gaps. The power frequency breakdown voltages of longer air gaps which are affected by corona, and the 50% positive switching impulse breakdown voltages of long sphere-plane gaps and rod-plane gaps were predicted as well. The predicted results agree well with experimental values and simulated results of the published models, which validate the effectiveness of the proposed model. This method supplies a new possible way to obtain the breakdown voltage of air gaps.

Prediction of partial discharge and breakdown voltages in CF4 for arbitrary electrode geometries

In literature a model was described for the simulation of the breakdown behaviour of SF6. As input parameters, results from swarm experiments—the critical field strength and the effective ionization coefficient—and thermodynamic properties are sufficient. Recently it was shown that it is possible to transfer this model to another strongly attaching gas. In this contribution the model is applied to tetrafluoromethane (CF4), which has a smaller critical field strength and is less attaching than SF6. Furthermore, the breakdown field strengths of alternating and lightning impulse voltages of different field configurations—from homogeneous to inhomogeneous fields—are predicted for CF4. A comparison to experimental results from the literature shows good agreement. Thus, the model is applicable not only to predict partial discharges and breakdown voltages in strongly attaching gases but also to gases less attaching than SF6.

Characterization and Modeling of Breakdown Probability in Sub-Micrometer CMOS SPADs

This paper presents the characterization of two different single-photon avalanche diodes fabricated in a standard 0.15 μm CMOS process and the modeling of their breakdown probability. The first device is based on a p+/nwell abrupt junction, while the second one has a pwell/n-iso diffused junction. Breakdown voltage and breakdown probability are modeled using both local and dead-space models. While the two models are in agreement for the prediction of breakdown voltage, it is shown that the local model largely underestimates the breakdown probability with respect to experimental results. On the contrary, the voltage and wavelength dependence of breakdown probability can be correctly predicted using the dead-space model.

CFD Simulation of Transonic Flow in High-Voltage Circuit Breaker

A high-voltage circuit breaker is an indispensable piece of equipment in the electric transmission and distribution systems. Transonic flow typically occurs inside breaking chamber during the current interruption, which determines the insulating characteristics of gas. Therefore, accurate compressible flow simulations are required to improve the prediction of the breakdown voltages in various test duties of high-voltage circuit breakers. In this work, investigation of the impact of the solvers on the prediction capability of the breakdown voltages in capacitive switching is presented. For this purpose, a number of compressible nozzle flow validation cases have been presented. The investigation is then further extended for a real high-voltage circuit breaker geometry. The correlation between the flow prediction accuracy and the breakdown voltage prediction capability is identified.

An Equivalent Lumped Circuit Model for Thin Avalanche Photodiodes With Nonuniform Electric Field Profile

A staircase approximation method is deployed to model nonuniform field in the multiplication region and its surrounding ambient of a thin avalanche photodiode (APD). To the best of our knowledge, this is the first instance of introducing an equivalent circuit model that is taking the effect of the electric field profile in a thin APD's multiplication region and its surroundings into account. This equivalent circuit model that is developed from the carriers' rate equations also includes the effect of the tunneling current. The tunneling current that can be induced as a small current injected into the multiplication region results in an enhanced model behavior at high reverse bias voltages near breakdown. The output current obtained from the proposed model is compared with available experimental data. This comparison reveals excellent model accuracy, in regard to the current levels and prediction of breakdown voltages for both photo and dark currents. Moreover, simulations demonstrate ability of the present model for gain-bandwidth analysis.

AC Breakdown Voltage Simulation of SF 6 /N 2 Mixture in Non-Uniform Field and Its Comparison with Experimental Values

is the most commonly used insulating gas in electrical systems. But In these days mixtures and alternative gas has been studied because of global warming. so although many studies have been carried out about binary gas mixtures with , few studies were presented about breakdown characteristics of mixtures. At present study the breakdown characteristics of mixtures in Non-uniform field was performed. In this paper, The simulation value are compared with experiment values. Streamer breakdown criterion was used for predicting breakdown voltage. For accurate simulation this simulation apply utilization factor using CST(computer simulation technology) EM program. AC breakdown experiments in non-uniform field was performed to compare with the breakdown simulation values. The pressure range of gas mixtures was 0.4 MPa to 0.7 MPa. The rod-plane was used and mixture ratio is 20% : 80%. The gap lengths are 10mm to 70mm. As the pressure increase, this simulation value does not correspond to the experiment value. So this simulation need surface roughness factor. As a result of applying surface roughness factor this simulation decrease a relative error (|experiment value - simulation value| /simulation value).

Comparison of 4H-SiC impact ionization models using experiments and self-consistent simulations

We report comparisons of measured photocurrent versus voltage curves of avalanche photodiodes (APDs) with those calculated using different 4H-SiC hole and electron impact ionization coefficients. As the published impact ionization coefficients result in ionization rates that differ greatly in magnitude, the predicted breakdown voltages using these models vary by many volts. To this end, we investigate the breakdown voltage prediction capability of three prevailing impact ionization models in conjunction with several experiments. To obtain APD performance numerically, we developed a device simulator, which shows that the inclusion of proper electric field-dependent impact ionization rates can accurately predict a variety of measured current-voltage curves, breakdown voltages, and current multiplication rates.