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Plasma extraction transit time (PETT) oscillation will cause electromagnetic interference, which is harmful to the device operation. Thus, it is important to estimate the PETT oscillation frequency accurately to suppress this oscillation. While the existing formula for PETT oscillation frequency is approximate due to its divergent problem, in this letter, a modified formula for PETT oscillation frequency is proposed for insulated gate bipolar thyristor devices. The modified formula considers the acceleration process, which is complex but does not change with the electric field. Therefore, the divergence of the formula can be well solved by introducing the modified term. Moreover, the modified formula can be obtained straightforwardly and well match the measured oscillation frequency, which verifies the accuracy of the proposed formula.

Plasma extraction transit time (PETT) oscillation occurs when insulated gate bipolar transistor (IGBT) devices turn off , whose frequency can reach hundreds of MHz. This high-frequency oscillation can induce electromagnetic interference problems and may exceed relevant IEC limits for electromagnetic emission. As one of the electromagnetic disturbance sources from internal IGBT, the PETT oscillation has not attracted much attention in the electromagnetic compatibility (EMC) problems of application and development of high-voltage IGBT devices yet. In this article, the detailed characteristics of PETT oscillation, i.e., oscillation frequency, duration time, and oscillation peak, in high-voltage IGBT devices are systematically investigated by experiments. From the results, oscillation frequency increases with the increase of reverse voltage, whereas decreases with the increase of the temperature. Then, more complex dependencies of the PETT oscillation characteristics on IGBT's commutation conditions are presented. It is shown that its characteristics vary greatly under different reverse voltage, forward current, and temperature. The hazard of PETT oscillation to electronic components is discussed as well. PETT oscillation occurs during the turn- on process, which is first reported. Based on the experiment results, the EMC test related to PETT oscillation is suggested to be performed under different commutation conditions.

Electrical insulation capability is one of the most critical challenges for press-pack insulated gate bipolar transistors (IGBTs), but the knowledge of its insulation failure mechanism is much less mature. To understand the insulation failure mechanism, partial discharge (PD) measurement and analysis in press-pack IGBT (PPI) submodule were performed in this study. A dedicated PD measurement system for PPIs was designed and fabricated, and fast oscilloscope, PD detector and ultraviolet (UV) image camera were employed to observe the PD. PD-induced insulation failure phenomenon in the submodule was observed under direct current voltage, and UV images indicated that the discharge occurred at the periphery of the die. Phase-resolved PD (PRPD) analysis method for PPIs was developed and three elementary structures were used to simulate the PD in the electric field reinforcement areas identified by electric field analysis. According to PRPD patterns, the observed PD in the submodule was considered to be mainly caused by a mismatch between the electric field and critical electric field on the surface of passivation layers of the die. The inference was verified at different gas pressures because a decrease of PD inception voltage in the submodule and the bare die was observed at lower gas pressure.

In this paper, the broadband dielectric characteristics of PEEK material used in press pack IGBT devices are measured by the method of frequency-domain dielectric spectroscopy under temperatures from −40 °C to 180 °C and frequencies from 10 mHz to 1 MHz. The mechanism of the broadband dielectric response of PEEK material is analyzed and can be accurately described by dual-relaxation Cole-Cole model. The effects of temperature on broadband dielectric characteristics of PEEK material are analysed with thermal analysis results of Differential Scanning Calorimeter. When the temperature is above the glass transition temperature (153.5 °C), the complex relative permittivity of PEEK material increases sharply. The results of this paper can provide useful references for insulating packaging design of press pack IGBT devices under electrothermal composite stress.

The corona-generated space charge may vary in different air pressures, and the quantitative law between the corona-generated space charge density and air pressure has never been investigated. This work utilised computational approaches to explore the role of air pressure in the variation of negative corona-generated space charge. A negative fluid model in which the air pressure could be varied was built up to calculate the charged-particle density and electric field under different applied voltages. The continuity equations were solved by flux corrected transport algorithm and the Poisson equation was computed by the successive over relaxation method. The variations of important parameters due to air pressure were analysed. The particles densities of electrons, positive ions and negative ions at standard and low atmosphere pressures were computed, which increased markedly due to the decrease of atmosphere pressure. At fixed applied voltages, the exponential decay law between the maximal values of electron/positive ion densities at the head of the streamer and air pressure could be obtained. The quantitative law between the maximal values of electron/positive ion densities at the head of the streamer and the applied voltage, relative air density was also achieved.

Hybrid DC circuit breaker used in higher voltage flexible HVDC project is the current development trend. With the increase of the breaking current level of the DC circuit breaker, the magnetic field interference intensity of the surrounding secondary equipment is gradually enhanced. In this paper, the DC field of DingHai converter station of Zhoushan flexible DC transmission project is taken as the simulation object. Considering the commutation process of DC breaker breaking large current, the transient magnetic field interference value of the secondary equipment in the DC field is calculated when the bipolar short circuit occurs on the DC side. The results show that when the DC circuit breaker breaks the 15 kA current, the maximum value of the magnetic field interference of the secondary equipment near the positive and negative DC circuit breakers in the DC field are 700 A/m and 382 A/m, respectively. The peak value of the magnetic field interference can be included in the test level proposed in the electromagnetic compatibility standard "Test and Measurement Technology: Pulse Magnetic Field Immunity Test", but the duration time of the pulsed magnetic field waveform to be evaluated is recommended to be maintained at 3 ms.

The failure of sub-module of press-packed devices in course of high temperature reverse bias (HTRB) test was investigated in this paper. A high temperature reverse bias test platform for press-packed devices was built, which was capable of monitoring and collecting continuous leak current data of devices during the test. Through the analysis of the static characteristics of the chip before and after the high temperature reverse bias test, a method that uses the combination of IV and CV characteristic curves to characterize high temperature reverse bias failure was proposed. By means of finite element analysis to locate the reasons for the change of static characteristics, the accumulated charges around the terminal of chip were considered to be the primary source for the failure of sub-module under the HTRB test.

Space weather forecasting is very important, and the prediction of space weather, especially for solar flares, has increasingly attracted research interests with the numerous recent breakthroughs in machine learning. In this study, we propose a novel convolutional neural network (CNN) model to make binary class prediction for both ≥C-class and ≥M-class flares within 24 hr. We collect magnetogram samples of solar active regions (ARs) provided by the Space-weather Helioseismic and Magnetic Imager Active Region Patches (SHARP) data from 2010 May to 2018 September. These samples are used to construct 10 separate data sets. Then, after training, validating, and testing our model, we compare the results of our model with previous studies in several metrics, with a focus on the true skill statistic (TSS). The major results are summarized as follows. (1) We propose a method of shuffle and split cross-validation (CV) based on AR segregation, which is the first attempt to verify the validity and stability of the model in flare prediction. (2) The proposed CNN model achieves a relatively high score of TSS = 0.749 ± 0.079 for ≥M-class prediction, and TSS = 0.679 ± 0.045 for ≥C-class prediction, which is greatly improved compared with previous studies. (3) The model trained on 10 CV data sets is considerably robust and stable in making flare prediction for both ≥C class and ≥M class. Our experimental results indicate that our proposed CNN model is a highly effective method for flare forecasting, with quite excellent prediction performance.

We propose a hybrid Convolutional Neural Network (CNN) model (Model 2) and modify a popular CNN model (Model 1) to predict multiclass solar flare occurrence within 24 hr. We collect samples of solar active regions provided by the Space-weather Helioseismic and Magnetic Imager Active Region Patches data from 2010 May to 2018 September. These samples are categorized into four classes (No-flare, C, M, and X), containing 10 separate data sets. Then after training, validating, and testing our models, we compare the results with previous studies in forecast verification metrics with an emphasis on the true skill statistic (TSS). The main results are summarized as follows. (1) This is the first time that the CNN models are used to make multiclass predictions of solar flare. (2) Model 2 has better values of all statistical scores than Model 1 in every class. (3) Model 2 achieves relatively high average scores of TSS = 0.768 for No-flare class, 0.538 for C class, 0.534 for M class, and 0.552 for X class, which are the best results from the existing literatures. (4) Model 2 also can be used to make binary class flare predictions for ≥M-class major flares, and the performance yields a TSS with 0.749 ± 0.079. (5) Model 2 obtains fairly good scores in other metrics for both multiclass flare predictions and ≥M-class major flare predictions. We surmise that some crucial features extracted automatically by our models may have not been excavated before and could provide important clues for studying the mechanism of flare.