Search Results (191)

In this article, thermal investigation methods for electrically insulating and thermally conductive substrate materials will be presented. The investigations were performed in their real-world application environment, i.e., in the form of IGBT (insulated gate bipolar transistor) module substrate plates. First, the overall thermal resistance and thermal structure function of the system in a multivariable parameter space were revealed using CFD (computational fluid dynamics) simulations. Afterwards, thermal transient testing was performed on real samples, with the help of which the thermal resistance values of the modules were determined using the thermal dual interface test method. The presented tests are not intended to determine material parameters, but to rank different substrate materials based on their thermal performance. Full article

A reliable prediction of the remaining useful life of critical electronic components, such as insulated gate bipolar transistors, is necessary for preventing failures in many industrial applications. Recently, diverse machine-learning techniques have been used for this task. However, they are generally focused on capturing the temporal dependencies or on representing the probabilistic nature of the degradation of the device. This work proposes a neural network architecture that combines long short-term memory and mixture density networks to address both targets simultaneously when modeling the remaining useful life. The proposed model was trained and evaluated using a real dataset of insulated gate bipolar transistors, demonstrating a high capacity for predicting the remaining useful life of the validation devices. The proposed model outperformed the other algorithms considered in the study in terms of root mean squared error and coefficient of determination. In general terms, an average reduction of at least 18% of the root mean squared error was obtained when compared with the second-best model among those considered in this work, but in some specific cases, the root mean squared error during the prediction of remaining useful life decreased up to 21%. In addition to the high performance obtained, the characteristics of the network output also facilitated the creation of confidence intervals, which are more informative than solely exact values for decision-making. Full article

Packaging equipment plays a crucial role in the semiconductor industry by enhancing product quality and reducing labor costs through automation. Research was conducted on IGBT module packaging equipment (an automatic pin insertion machine) during the pin assembly process of insulated gate bipolar transistor (IGBT) modules to improve productivity and product quality. First, the manual pin assembly process was divided into four stages: feeding, stabilizing, clamping, and inserting. Each stage was completed by separate cams, and corresponding step timing diagrams are drawn. The profiles of the four cams were designed and verified through theoretical calculations and kinematic simulations using a seventh-degree polynomial curve fitting method. Then, image algorithms were developed to detect pin tilt defects, pin tip defects, and to provide visual guidance for pin insertion. Finally, a pin insertion machine and its human–machine interaction interface were constructed. On-machine results show that the pin cutting pass rate reached 97%, the average insertion time for one pin was 2.84 s, the pass rate for pin insertion reached 99.75%, and the pin image guidance accuracy was 0.02 mm. Therefore, the designed pin assembly machine can reliably and consistently perform the pin insertion task, providing theoretical and experimental insights for the automated production of IGBT modules. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. To solve this problem, an intelligent diagnosis method is proposed to integrate the modal time–frequency diagram and FFT-CNN-BiGRU-Attention. By selecting the phase current and bridge arm voltage as the core fault parameters, the particle swarm algorithm is used to optimize the Variational Modal Decomposition parameters, and the fault signal is decomposed and reconstructed into sensitive feature components. The reconstructed signals are further transformed into modal time–frequency diagrams via continuous wavelet transform to fully retain the time–frequency domain features. In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. Simulations show that the proposed method achieves 98.63% accuracy in locating faulty insulated gate bipolar transistors (IGBTs) in the sub-module, with second-level real-time response capability. Compared with the recently published scheme, it maintains stable performance under complex working conditions such as noise interference and data imbalances, showing stronger robustness and practical value. This study provides a new idea for the intelligent operation and maintenance of power electronic devices, which can be extended to the fault diagnosis of other power equipment in the future. Full article

This paper presents a comprehensive review of High-Voltage Direct-Current (HVDC) systems, focusing on their technological evolution, fault characteristics, and advanced signal processing techniques for fault detection. The paper traces the development of HVDC links globally, highlighting the transition from mercury-arc valves to Insulated Gate Bipolar Transistor (IGBT)-based converters and showcasing operational projects in technologically advanced countries. A detailed comparison of converter technologies including line-commutated converters (LCCs), Voltage-Source Converters (VSCs), and Modular Multilevel Converters (MMCs) and pole configurations (monopolar, bipolar, homopolar, and MMC) is provided. The paper categorizes HVDC faults into AC, converter, and DC types, focusing on their primary locations and fault characteristics. Signal processing methods, including time-domain, frequency-domain, and time–frequency-domain approaches, are systematically compared, supported by relevant case studies. The review identifies critical research gaps in enhancing the reliability of fault detection, classification, and protection under diverse fault conditions, offering insights into future advancements in HVDC system resilience. Full article

Insulated Gate Bipolar Transistors (IGBTs) are widely deployed in power electronic systems due to their superior performance. However, at the same time, they are one of the most critical and fragile components in electronic systems. The failure prediction of IGBTs can precisely forecast the potential risk to guarantee system reliability. In this paper, Bayesian-optimized CEEMDAN is adopted to extract fault features efficiently, and a prognostic model named Performer-KAN is proposed for IGBT failure prediction. The proposed model combines the efficient FAVOR+ mechanism from the Performer with the flexible spline-based activation of the Kolmogorov–Arnold Network (KAN), enabling improved nonlinear approximation and predictive precision. Comprehensive experiments were conducted using the IMFS, which were decomposed by BO-CEEMDAN. The model’s performance was evaluated using key metrics such as MAE, RMSE, and R². The Performer-KAN demonstrates superior prediction accuracy while maintaining low computational overhead, compared to six representative deep learning models. The results demonstrate that the proposed method offers a practical and effective solution for real-time IGBT health monitoring and fault prediction in industrial applications. Full article

The contact interfaces of a press-pack insulated-gate bipolar transistor (PP-IGBT) module under fluctuating thermal stress will undergo minor friction and mutual sliding during service, which results in damage to the contact surface and a decline in the thermal performance of the contact interface. Therefore, the temperature inside the module will continue to increase, leading to eventual failure. In this work, a life prediction method based on thermal resistance degradation within a PP-IGBT module is established. The junction temperature can be determined via power loss and a resistance-capacitance (RC) thermal network model, and a life prediction model of the PP-IGBT module is developed based on thermal resistance degradation. The method considers the service quality under power cycling conditions and the influence of the self-accelerating effect of damage accumulation at the contact interface of the PP-IGBT module on fatigue life. The experimental results verify that the proposed PP-IGBT module life prediction method can effectively predict service life under power cycling conditions. Full article

A method considering thermal boundary conditions and thermal coupling effects is proposed to estimate the junction temperature of double-sided cooling insulated-gate bipolar transistor (IGBT) modules. Traditional methods, which rely on negative temperature coefficient (NTC) measurements, often overlook mutual thermal interactions among chips, leading to inaccuracies under varying cooling boundary conditions. In this paper, a Foster thermal network model incorporating chip thermal coupling is developed to estimate the junction temperature of double-sided cooling IGBT power modules. The thermal model parameters are extracted through a combination of finite element simulation and experimental analysis. The effects of different cooling boundary conditions on the thermal model and the module’s heat channeling behavior are examined, and compensation strategies for various cooling boundaries are proposed. Experimental and simulation results indicate that the estimated junction temperature error of the proposed method remains within 5 °C under different operating conditions. Full article

Faults of a Modular Multilevel Converter (MMC)-type converter valve significantly impact the reliability of flexible DC transmission systems. This paper analyzed the impact of ongoing short circuit faults in submodules on the power transmission of the MMC-type converter valve of which redundant submodules had been depleted. First, MMC’s working principle and its submodules’ possible operational states were investigated. Then, fault mechanisms for intra-submodule Insulated-Gate Bipolar transistor (IGBT) short circuits and inter-submodule short circuits were modeled to infer changes in power transmission during submodule faults. To quantify the impact of submodule faults on the energy transfer efficiency of the converter valve, an energy transfer efficiency index was proposed to obtain analytical expressions for energy transfer efficiency in the case of intra-submodule and inter-submodule short-circuit faults. Finally, the effectiveness of the proposed analytical model was verified through Simulink simulations. Simulation results indicate that ongoing intra-submodule and inter-submodule short circuits increase the input power of the converter valve, reducing energy transfer efficiency. Moreover, the energy transfer efficiency continues to decline with an increase in faulty submodules. Full article

This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes take time to reach the sensors. To address this, this study proposes a junction temperature estimation method using RC curve fitting and a thermal impedance model. This model represents the thermal behavior of IGBTs and diodes using a Foster thermal network that considers the resistance and capacitance of the heat transfer path. In particular, transient temperature estimation considering thermal coupling enables the prediction of temperature changes in IGBTs and diodes. To verify the proposed temperature estimation method, experiments were conducted to build the model based on data measured with an infrared thermal camera and NTC sensors. The model’s estimated results were compared with actual values across 25 operating regions, achieving a maximum MAE (Mean Absolute Error) of 2.26 °C. A comparative analysis of first-, second-, third-, and fourth-order Foster networks revealed that, while higher orders improve accuracy, gains beyond the second order are minimal relative to computational demands. This study contributes to enhancing not only the reliability of power semiconductor modules but also minimizing the temperature margin for inverters by estimating the junction temperature with better dynamic performance than that achieved by NTC sensors. Full article

To effectively diagnose open-circuit (OC) faults in the insulated gate bipolar transistor (IGBT) of a cascaded H-bridge rectifier (CHBR) in real-time, this paper uses a single-phase three-cell CHBR as an example. Through mechanism analysis, the variation patterns of the capacitor voltage and grid current due to OC faults are defined. Based on this, the DC capacitor voltage threshold (VT) and the grid current coefficient of variation (CCV) are introduced as fault diagnosis indices, and a real-time OC fault diagnosis method for CHBR is established. The robustness, accuracy, timeliness, and universality of the proposed method are validated through simulations. The results show that the proposed method exhibits strong robustness when the grid voltage fluctuates, either dropping from 3 kV to 2.85 kV or rising from 3 kV to 3.15 kV. Compared to existing diagnostic methods, the proposed approach requires less diagnostic time, with the faulty IGBT being identified in as little as 3.09 ms under optimal conditions. Additionally, the diagnostic performance remains unaffected by changes in control strategies, making it universally applicable for OC fault diagnosis in CHBR under various control strategies (such as dq current decoupling control, PR current control, and transient current control), with comparable diagnosis results and speeds. Full article

Converters play a critical role in wind power generation systems, with their reliability directly impacting system stability and operational efficiency. To address the challenges posed by increased thermal load fluctuations due to solder layer aging in insulated gate bipolar transistor (IGBT) modules in converters, this paper proposes an aging-aware multi-objective thermal management (AAMO-TM) strategy to enhance the performance of aging modules. An improved junction temperature estimation model is developed, incorporating coordinated control of switching frequency and gate drive resistance to account for the dynamic thermal behavior of IGBT modules during aging. Pareto and hierarchical optimization techniques are employed to resolve the multi-objective problem of excessive junction temperature suppression, junction temperature fluctuation smoothing, and power quality improvement. Experimental results demonstrate that our proposed AAMO-TM strategy outperforms a competing strategy at temperature fluctuation by a large margin (up to 59.4%). Our proposed strategy significantly enhances the thermal stability of aging IGBT modules while effectively suppressing grid-connected current harmonics. This study provides valuable theoretical insights and practical guidance for achieving the stable operation of wind turbines and delivering high-quality power output. Full article

Modular multilevel converter (MMC) circulating current suppression is an effective method in improving power conversion quality. However, due to the complex composition of circulation under a three-phase imbalanced state, as well as the limitations of existing sliding mode circulation suppression methods that can easily cause high-frequency oscillation in the system, the circulation suppression effectiveness still needs to be further improved. Firstly, this article clarifies the phase sequence law and key control factors of the circulation by separating the MMC circulation phase sequence and decoupling the model. Secondly, the generalized proportional integral sliding surface and the hyperbolic tangent convergence law are introduced into sliding mode control to improve the system’s ability to flexibly suppress the circulating current. Then, the proposed method is evaluated using the system power quality and insulated-gate bipolar transistor (IGBT) junction temperature amplitude. The results show that the proposed method reduces the total harmonic distortion of the bridge arm current to 1.28% and 1.03%, respectively, under three-phase balanced and imbalanced states, and effectively smooths the IGBT junction temperature fluctuation of submodules. It also improves the stability and robustness of MMC system to circulation suppression gain variations, sudden load changes, and switching failures. This article provides an effective method for the synchronous implementation of MMC circulation suppression and IGBT junction temperature smoothing under complex and variable operating conditions. Full article

This study aims to conduct an assessment of the dynamic characteristics of a proposed 6.6 kW bidirectional bridgeless three-leg interleaved totem-pole power factor correction (PFC) boost converter developed for the front-end stage of electric vehicle onboard charger applications during load cycles. This proposed PFC boost converter integrates the self-developed silicon carbide (SiC) power MOSFET modules for achieving high efficiency and high power density. To assess the switching transient behavior, power loss, and efficiency of the SiC MOSFET power modules, a fully integrated electromagnetic-circuit coupled simulation (ECCS) model that incorporates an electromagnetic model, an equivalent circuit model, and an SiC MOSFET characterization model are used. In this simulation model, the impact of parasitic effects on the system’s performance is considered. The accuracy of the ECCS model is confirmed through comparing the calculated results with the experimental data obtained through the double pulse test and the closed-loop converter operation. Furthermore, a comparative study between the interleaved and non-interleaved topologies is also performed in terms of power loss and efficiency. Additionally, the performance of the SiC MOSFET-based PFC boost converter is further compared with that of the silicon (Si) insulated gate bipolar transistor (IGBT)-based one. Finally, a parametric analysis is carried out to explore the impact of several operating conditions on the power loss of the proposed totem-pole PFC boost converter. Full article