Search Results (411)

One of the main causes of failure in Insulated Gate Bipolar Transistor (IGBT) modules used in high-power conversion applications is thermal-stress-induced degradation. In this paper, an experimental testing setup for thermal stress and real-time degradation monitoring, as well as a deep neural network (DNN)-based lifetime prediction of IGBT modules under thermo-electrically stressed inverter operation, is proposed. A two-level SVPWM inverter is implemented to create a hybrid power cycling test platform that imposes well-defined junction-temperature swings representative of real-world operation by combining controlled electrical loading and active induction heating with water cooling. Throughout the aging process, on-state voltage and module temperature are constantly monitored to identify degradation precursors associated with thermo-mechanical fatigue. A physics-based Coffin–Manson lifetime model is fitted using failure datasets to characterize temperature-dependent lifetime behavior. An offline deep neural network (DNN) is trained on degradation trajectories derived from on-state collector–emitter voltage (V_ce,on) to predict remaining useful lifetime. This approach uses partial degradation histories for accurate early-life prediction. The proposed DNN model for competitive and computationally efficient lifetime prediction is validated experimentally on several IGBT modules under different thermal stresses, and its accuracy is compared with other prediction methods. Full article

A novel modular multilevel converter (MMC)-based spark-gap trigger generator for high-voltage pulsed-power applications has been developed and presented in this work. It fully exploits the inherent modularity of MMC topology to generate high-voltage trigger pulses in a flexible and scalable manner. A prototype based on insulated gate bipolar transistors (IGBTs) was constructed to effectively trigger the breakdown of the spark gaps of a Marx Bank consisting of four capacitors charged to 50 kV. It is characterized by a fast rise time and produces pulses of 15 kV with a duration of ~200 ns. Using semiconductors and foil capacitors, the new trigger generator successfully replaces the thyratron-based generator. Full article

Modular multilevel converters (MMCs) for high-voltage direct current (HVDC) transmission require substantial submodule (SM) capacitance to limit capacitor voltage ripple, resulting in bulky and costly converter valves. The integrated gate-commutated thyristor (IGCT), with its higher voltage rating and lower conduction loss compared to the insulated-gate bipolar transistor (IGBT), enables a significant reduction in the number of SMs per arm, offering a pathway toward compact converter design. This paper investigates how the reduced SM count of IGCT-based MMCs affects the feasibility and benefit of operating with elevated capacitor voltage ripple to further decrease SM capacitance. An analytical framework is developed to evaluate the modulation boundary under increased ripple, explicitly accounting for the voltage ripple coupling (CVR) effect and circulating-current suppression. A ripple-tolerance coefficient κ is introduced, and its optimal value is determined by identifying the inflection point beyond which the achievable AC voltage output begins to decline. For a ±500 kV/2000 MW IGCT-MMC case study using 6.5 kV devices with 250 SMs per arm, the proposed method reduces the per-unit energy storage requirement by up to 39.4% compared with conventional-ripple operation. Simulation and prototype experimental results on a 400 V, 3 kW, 4-SM/arm test bench validate the analytical predictions and confirm the practical feasibility of the approach. Full article

In current IGBT reliability assessment methods, there is a lack of modeling for overstress failures and insufficient consideration of epistemic uncertainty. To address this, this paper proposes a novel reliability assessment method based on belief reliability theory and uncertainty theory. By establishing an IGBT reliability domain model and an external-stress model, a margin-evaluation framework integrating multi-operating-condition characteristics is constructed. Furthermore, a first-order information-based belief reliability calculation algorithm is developed. This method, for the first time, incorporates overstress failures into a quantitative assessment framework and overcomes the inaccuracy of traditional methods under small-sample testing scenarios, providing a technical basis for IGBT device selection and operational reliability assurance in power electronic systems. Full article

Effective online bond wire degradation assessment of power modules is crucial for ensuring long-term stability. However, its electrical aging indicators are often influenced by junction temperature ($T_j$), and conventional $T_j$ monitoring methods are also affected by the aging process itself, creating a contradiction. This paper proposes a thermal–electrical co-modeling method designed to reduce reliance on accurate $T_j$. A major challenge of the method is the traditional thermal network models, which rely on case temperature ($T_c$). These models are affected by thermal coupling and have a slow dynamic response, making them difficult to integrate with electrical models. To overcome this, a $T_j$ monitoring method based on in situ sensor fabrication is employed to shorten thermal conduction path and simplify thermal network. This method results in a much faster dynamic process and is unaffected by thermal coupling, as confirmed through both theoretical analysis and finite element simulation. To validate the proposed method, bond wire degradation assessment is conducted using the on-state voltage drop ($V_{ce}$). Tested in practical circuits, this design successfully enables online evaluation of bond wire degradation, which is unaffected by $T_j$. Full article

To address the aging and failure issues that arise during the long-term operation of insulated gate bipolar transistors (IGBTs), this paper proposes a method for predicting their remaining useful life (RUL). The proposed method utilizes a genetic algorithm to optimize a hybrid model that combines a convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) network. First, based on the failure mechanism of IGBTs, various commonly used RUL prediction methods are analyzed and compared. Considering that CNNs are particularly effective at extracting spatial features, while LSTMs excel at capturing long-term dependencies in time-series data, a hybrid CNN-BiLSTM model is developed for RUL prediction, with hyperparameters, including the initial learning rate, optimized using a genetic algorithm. Experimental results demonstrate that the proposed CNN-BiLSTM model achieves superior performance across all metrics compared with benchmark algorithms, and the genetic algorithm significantly accelerates the parameter optimization process and enhances the overall training efficiency. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

Insulated-gate bipolar transistor (IGBT) power modules suffer efficiency degradation at elevated operating junction temperatures. The thermal sensitivity of the collector–emitter saturation voltage (V_CEsat) induces thermal stress imbalance, constraining system efficiency and reliability. A multi-resistor cascade network model for carrier-storage trench-gate IGBTs (CS-IGBTs) is established. The simulation results agree with the measurements within 10% error. The model decomposes the temperature coefficient contributions of individual structural regions. Analysis reveals that the drift region resistance dominates the V_CEsat temperature coefficient. Based on this finding, a co-doping strategy is proposed through simultaneously increasing the doping concentration in the carrier-storage layer and P⁺ collector. This approach reduces the temperature sensitivity of carrier mobility in the drift region, thereby optimizing V_CEsat’s temperature sensitivity. For the fabricated 1200 V/40 A CS-IGBT, the V_CEsat temperature coefficient decreases from 2.38 mV/K to 1.76 mV/K over 300 K to 450 K, which represents a 25.4% reduction. The total switching loss at 450 K decreases from 9.32 mJ to 8.70 mJ, achieving a 6.7% improvement. This device-level optimization suppresses V_CEsat’s temperature sensitivity and switching losses, enhancing efficiency in high-temperature power module applications. Full article

While sustainability is set as a goal by a broad range of international organizations, its definition varies, and there is still a lack of practical criteria for product designers to evaluate the degree of (un)sustainability in the design phase. Life cycle assessment (LCA) can allow quantification of the environmental impacts of a product but is often carried out post-design, when the manufacturing process is already settled. Finally, while significant advances have been made towards standardizing LCA calculations by providing product category rules, large uncertainties remain in the calculation results due to a lack of transparency regarding the choices of databases, system boundaries, allocation, cut-off rules, and level of data granularity. A practical way to improve in those areas is to share with the semiconductor community a parametrizable life cycle inventory (LCI) model based on a target device to (1) identify knowledge gaps in LCA methods for such products, (2) identify the main process variables, and (3) provide a starting point for LCA calculations by the designers themselves. With this aim, a parametrizable cradle-to-gate manufacturing LCI model was developed based on the peer-reviewed process flow of a trench field-stop silicon insulated gate bipolar transistor (IGBT) semiconductor power device. The model allows computation of the environmental impacts of the IGBT manufacturing process based on different tunable parameters such as die size, wafer diameter, manufacturing yield, abatement efficiency, wafer fab throughput, wafer fab location, and associated electricity mix. Embedding a high level of data granularity, it helps identify, at elementary process levels, key environmental hotspots and associated technical levers for their reduction. Analysis of the IGBT manufacturing process tends to demonstrate the importance of an impact assessment approach considering multiple environmental categories, going beyond the sole focus on greenhouse gas emissions and accounting for potential transfers of impact. With an open-source mindset and in a continuous improvement prospective, the manufacturing inventory model and its associated tools are freely available from a public GitHub repository and open for comments and consolidation from users. Full article

This paper presents a signal-processing methodology for assessing thermal stress and fatigue damage in IGBT modules. This study utilizes junction temperature data from operational frequency converters at a belt conveyor station rather than conventional approaches. These in situ measurements ensure that thermal profiles accurately reflect actual loading conditions. A reliability framework based on mission profiles assesses the contribution of each operational regime. We examine transient overloads, steady-state operation, and periods of low load specifically. We apply Miner’s rule and rainflow counts to the analyzed temperature profiles. This enables the assessment of accumulated damage in each operational segment. The primary finding indicates that a minimal duration of operational time constitutes the majority of total lifetime utilization. This disproportionate impact is attributable to transient overloads. This study quantitatively evaluates this phenomenon using Rainflow analysis to disaggregate mission profiles. The proposed framework enhances the precision of reliability engineering. It provides a valuable foundation for enhancing maintenance planning and control strategies in practical scenarios. Full article

The use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodological synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the dependence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prognosis tools for SiC MOSFETs remains an ongoing research challenge. Full article

The increasing penetration of converter-interfaced generation raises critical concerns for power system stability, especially during rapid transients and system split events that are not yet adequately addressed in current grid code compliance tests. This paper assesses the resilience of a Virtual Synchronous Machine (VSM) in comparison with a grid-following photovoltaic (PV) inverter through a combined framework of standardized benchmark tests and realistic system split scenarios. In benchmark testing, the VSM provided synthetic inertia by delivering a transient-power burst from a 0.30 p.u. setpoint to 0.545 p.u. (on a 20 MVA base, representing 54.5% of rated capacity) under a $-0.4$ Hz/s frequency ramp, corresponding to an equivalent inertia constant of approximately 15 s. With the limited frequency-sensitive mode–underfrequency (LFSM-U) function enabled, it sustained additional active power up to 0.61 p.u. once the frequency fell below 49.8 Hz. The PV inverter, by contrast, demonstrated compliance with conventional grid requirements: it curtailed power through LFSM-O during overfrequency conditions and injected 0.25 p.u. of reactive current during a fault ride-through (FRT) event at 1.129 p.u. voltage. In system split tests, the VSM absorbed surplus PV generation, stabilizing frequency after a transient rise to 52.8 Hz and containing voltage excursions beyond 1.2 p.u. During imbalance stress, it absorbed 1.266 MW against its 1.0 MW rating for approximately 2–3 s, corresponding to a 26.6% overload that falls within typical IGBT transient thermal capability but would require supervisory intervention (e.g., PV curtailment or load management) if sustained. These results demonstrate that while the PV inverter contributes valuable voltage support, only the grid-forming VSM maintains frequency stability and ensures secure islanded operation. The novelty of this study lies in integrating standardized compliance tests with system split scenarios, providing a comprehensive framework for evaluating grid-forming controls under both regulatory and resilience-oriented perspectives and informing the evolution of future grid codes. Full article

To address the low-voltage fault issue in doubly fed induction generator-motor (DFIGM) systems, this paper proposes a practically implementable cooperative control strategy that integrates an improved current reversely tracking control (CRTC) scheme with an enhanced IGBT-based active crowbar topology. The proposed method optimizes the current-tracking coefficients under rotor voltage and current constraints during LVRT operation. Meanwhile, the enhanced active crowbar provides reactive power support, thereby suppressing negative-sequence current components, mitigating harmonic distortion, and improving the power quality at the point of common coupling (PCC). A 10-MW DFIGM model is developed, and comparative studies are conducted with the conventional inductance emulating control (IEC) and the crowbar structure. The experimental results show the feasibility and effectiveness of the proposed method. Full article