Power electronic devices are critical to traction driving systems, and their reliability is essential for system safety. However, existing studies mainly focus on the wear-out period of the bathtub curve, lacking state characterization and remaining useful lifetime (RUL) prediction methods for the random failure period. Moreover, the current one-size-fits-all fixed mileage maintenance strategy compromises locomotive operation safety. To address these issues, this article proposes a novel RUL modeling approach for insulated gate bipolar transistor (IGBT) devices based on multitime-point inversion of solder layer defect morphology. Using computed tomography (CT) scans of the solder layer at multiple time points, a finite element model (FEM) of the IGBT device is established. A fatigue state inversion method is then applied to evaluate the health of each solder layer mesh element by comparing living elements with dead elements. Combined with a defect morphology iteration algorithm, the degradation process of the solder layer is simulated, and the material parameters of the weak layer are updated accordingly. An RUL prediction model incorporating microscopic morphology and material failure parameters is thus developed. The model accuracy is validated using the number of cycles to failure extracted from power cycling tests. This work enriches the lifetime prediction and reliability evaluation theory for power devices and provides a scientific basis for shifting from fixed mileage maintenance to condition-based maintenance in traction systems.

Focusing on the issue that the electrical parameters of servo PMSM are susceptible to operating temperature and short-term high overload, leading to significant dynamic high-precision trajectory tracking errors in conventional PI control methods, this paper proposes an adaptive trajectory tracking control strategy based on transfer-learning parameter identification. First, a multiphysics finite element model coupling electrical, magnetic, and thermal fields of the servo PMSM is established to obtain auxiliary training data for constructing a parameter-identification model. This model acquires resistance, dq-axis inductance, and permanent magnet flux linkage under various operating temperatures and overload conditions. A small amount of target data from the prototype is collected using experimental measurement methods. Second, a neural network is developed based on the auxiliary training data to characterize the mapping relationships between the four electrical parameters and operating temperature and current. By integrating the target data with a backpropagation (BP) neural network, feature transfer learning for electrical parameters is performed. Subsequently, an adaptive trajectory tracking control strategy is proposed, featuring online self-tuning of PI parameters for the position, speed, and current loops of the servo PMSM. Finally, simulations and experiments are conducted under combined working conditions involving energy- and time-optimal trajectories with varying operating temperatures. Compared with the conventional PI control strategy, the proposed adaptive trajectory tracking control strategy significantly reduces dynamic errors. Additionally, positioning energy consumption and time are reduced by up to 5.28% and 44.24%, respectively, which verifies the effectiveness of the proposed strategy.

This paper investigates the influence of bond wire lift-off on the heat sink temperature variation in IGBT modules. Firstly, an electrical–thermal–mechanical–fluid coupling model of an IGBT module integrated with a heat sink is established. The temperature monitoring point is determined according to the surface temperature propagation characteristics of the heat sink, and the relationship between the junction temperature variation and the heat sink monitoring point temperature variation during bond wire lift-off is analyzed. Secondly, the effects of solder layer aging degree and void location on the heat sink monitoring point temperature are investigated. The results show that solder layer aging changes the temperature baseline of the monitoring point, but has little influence on the temperature increase rate caused by bond wire lift-off. Finally, bond wire lift-off tests, junction temperature measurements, and tests under different cooling and current conditions are carried out. The relative error between the simulated and experimental temperature increase rates at the monitoring point is less than 5%, and the temperature increase rate at the monitoring point is consistent with the junction temperature increase rate under different bond wire lift-off states. A power cycling case study further shows that solder layer aging has negligible influence on the heat sink monitoring point temperature in the short term after bond wire lift-off. The results demonstrate that bond wire lift-off in IGBT modules can be evaluated by monitoring the heat sink temperature increase rate.

The global energy landscape is undergoing a low-carbon, diversified, and high-efficiency transition, creating an urgent need to develop intelligent operation and maintenance (O&M) technologies for critical nuclear power equipment to boost plant economic efficiency. As the core “heart” component of the primary loop, the reactor coolant pump (main pump) must meet extremely stringent reliability criteria to ensure safe and stable operation of nuclear facilities. Existing remaining useful life (RUL) prognostics for main pumps mostly output deterministic point estimates; they fail to quantify predictive uncertainties and cannot provide credible risk intervals to support maintenance decision-making. To fill this research gap, this study first performs coupled thermomechanical failure simulations for three vulnerable main pump components: the rotor shaft assembly, double-cone sealing structure, and motor shielding sleeve. Simulation results are validated via tests on a full-scale main pump prototype bench to extract sensitive degradation characteristic parameters. Accordingly, a hybrid Bayesian Transformer-LSTM prognostic framework is proposed for main pump RUL prediction with built-in uncertainty quantification. Data augmentation is utilized to expand multi-source degradation datasets of main pumps. The Mahalanobis distance is employed to build component-level health indicators (HIs), and a cloud barycenter weighted evaluation method fuses these sub-component HIs into a unified system-level comprehensive health index (CHI). Using the fused CHI as model input, the Bayesian Transformer-LSTM architecture incorporates probabilistic fully connected layers to simultaneously capture local time-series fluctuations and long-term global degradation trends, enabling joint RUL regression and uncertainty quantification. A full-scale main pump prototype from an in-service nuclear power plant is used to validate the multi-source data fusion strategy. Quantitative evaluation results show that the proposed method achieves a coefficient of determination R2 = 0.997, root mean square error (RMSE) = 0.018, and prediction interval coverage probability (PICP) = 0.839. Comparative ablation experiments further confirm that the proposed model delivers outstanding fitting precision and reliable uncertainty quantification, enabling long-timescale full-lifecycle health characterization of main pumps.

The accuracy of online parameter identification for permanent magnet synchronous motors (PMSMs) is constrained by discrete model errors, rank deficiency in the steady-state identification matrix, and voltage deviations resulting from inverter nonlinearities. This paper proposes a multi-parameter identification method acting as a high-precision virtual sensor, based on Zero-Order Hold (ZOH) discretization and an inverter nonlinear voltage compensation scheme utilizing a dual-sampling strategy. First, a discrete model of the PMSM, accounting for rotor position variations within the control period, is established using the ZOH discretization method. Compared with the forward Euler discretization method, this approach effectively minimizes discretization model errors, especially under high-speed operating conditions where rotor position variations are significant. Second, the rank deficiency problem of the steady-state identification matrix is overcome by combining d-axis small-signal injection with a dual-sampling strategy. Furthermore, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is introduced to achieve online multi-parameter identification. Finally, the influence mechanisms of the dead-time effect, power switch voltage drop, and turn-on delay on the output voltage are analyzed. Consequently, an inverter nonlinear voltage compensation strategy tailored for the dual-sampling mode is proposed. Experimental results demonstrate that the proposed method significantly enhances parameter identification accuracy across the entire speed range. Specifically, under high-speed conditions, the identification errors for resistance, inductance, and flux linkage are maintained within 5.47%, 4.05%, and 2.46%, respectively.

Epoxy vitrimers, featuring dynamic covalent bonds, are promising candidates for recyclable dielectrics, yet their practical applications in high-power systems are hindered by their dynamic networks at elevated temperatures, which lead to premature dielectric failure under the inevitable Joule heating. This work confirms the significant decline in insulation performance of epoxy vitrimers at elevated temperatures and proposes a multiscale filler-engineered strategy to develop thermally stable epoxy vitrimer dielectrics. Theoretical calculations of the filler-polymer interface energy levels are used to guide the optimization of the engineered microstructure and leverage the 3D printing process to control the orientation of BN micro-platelets. The structured composites exhibit a superior overall performance, including a high thermal conductivity of 1.911 W m-1 K-1, a glass transition temperature (Tg) of 122 degrees C, and a dielectric breakdown strength of 71.2 kV mm-1 at room temperature, which remains as high as 45 kV mm-1 at 180 degrees C, 67% higher than that of the pure resin. Variable-temperature infrared and UV-vis spectra confirm the structural stability of the epoxy vitrimer composites under heating. Mechanistically, the excellent room-temperature dielectric performance of the composites arises from the deep trap energy level and the wide bandgap nature of the composites, while the high-temperature stability is attributed to suppressed chain mobility and the barrier effect of multiscale fillers against hot carrier transport. These findings highlight a scalable pathway to recyclable, high-performance polymer dielectrics for demanding high-power applications.

Remaining useful life (RUL) prediction is a critical procedure to avoid catastrophic failure of shielding sleeves and prevent nuclear safety risks. Affected by the structural characteristics and service conditions of shielding sleeves, it is difficult to obtain sufficient full-life-cycle actual degradation data, which greatly restricts the training and application of data-driven prediction models. This paper proposes a remaining useful life prediction method for shielding sleeves based on data augmentation and a hybrid model. Firstly, starting from the physical failure mechanisms of two typical failure modes of shielding sleeves, namely bulging and wear. Secondly, based on the analytical models of typical failures of shielding sleeves, a degradation data augmentation method using Monte Carlo simulation is proposed to address the problem of missing full-life-cycle degradation data. Finally, a hybrid RUL prediction model for shielding sleeves based on Stacking ensemble learning is presented, which integrates the advantages of physical degradation models and deep learning methods. Experimental verification is carried out through multiple sets of degradation datasets with different failure modes. The root-mean-square error (RMSE) of the proposed prediction method can reach a minimum of 0.0058, and the mean absolute error (MAE) can reach a minimum of 0.0044. The prediction accuracy is superior to that of single models, which verifies the prediction performance and engineering applicability of the proposed method.

In a conventional vector space decomposition (VSD) coordinate system, the numerous vectors in three-level dual three-phase drives complicate vector traversal for model predictive control (MPC). Traditional MPC empirically selects candidate vectors, but it is difficult to meet all control objectives. Although weighted cost functions are widely adopted in multiobjective optimization (MOO), they introduce issues, such as objective coupling and challenging weight design, hindering global optimality. Based on the multivector and control flexibility of the system, this article proposes a weight-free MPC strategy with multiobjective decoupling optimization. First, dimensionality reduction analysis is conducted on basic vectors in double $\alpha \beta $ coordinate systems. Using the mapping relationship with conventional coordinates, a set of candidate virtual vectors (VVs) with low common-mode voltage (CMV) and zero harmonic voltage is synthesized. Moreover, dynamic vector scheduling (DVS) further minimizes switching actions, achieving decoupled control over harmonic suppression, CMV reduction, and switching frequency optimization during candidate vector construction. Second, to address the neutral point voltage (NPV) control issue caused by the lack of redundant small vectors in the candidate set, a group of redundant VVs-equal in amplitude and phase to the original VVs but without redundant small vectors-is designed. Their output ratio is adjusted in real time via a voltage predictive model, enabling active and decoupled regulation of the neutral-point voltage. The algorithm's effectiveness in MOO is validated through steady-state and dynamic experiments.

Deep neural network (DNN) model partitioning and pruning have proven to be effective methods for enhancing resource efficiency and reducing inference delay by strategically allocating DNN workloads across heterogeneous edge and cloud infrastructures. Nevertheless, the heterogeneous nature of resources complicates the deployment of DNN in mobile edge-cloud computing (MEC) networks. In this paper, we present an innovative framework for collaborative DNN inference in MEC networks by integrating fine-grained model partitioning and magnitude-based pruning. However, the joint model partitioning and pruning policy presents significant challenges due to the inherently coupled and mutually influential nature. To address it, we adopt Long Short-Term Memory (LSTM) networks as action generation controllers to generate discrete actions for model partitioning and pruning alternately. After that, we adopt the policy gradient algorithms to optimize the LSTM-generated actions with a moving average according to the Monte Carlo estimate. By directly optimizing the policy function, the proposed framework enhances the efficiency and stability of action space exploration, yielding faster convergence and improved inference performance. Experimental results on standard datasets indicate that the proposed framework outperforms state-of-the-art approaches, achieving an 8.247% increase in system reward and an average reduction of 27.313% in total delay within the considered MEC networks.

With the improvement of electronic equipment integration, the thermal management of key power devices such as IGBT in grid-connected energy storage converter has become an urgent problem to be solved in related design. Based on the transient thermal simulation technology, this paper uses COMSOL software to study the temperature distribution under overload conditions for the three-level grid-structured energy storage converter, and simulates the temperature field change process of the converter under different overload multiples and overload durations. The temperature distribution characteristics and variation laws of key areas such as power devices and heat dissipation structures are analyzed. The research results reveal the dynamic evolution mechanism of the internal temperature of the converter cabinet under overload conditions, and clarify the location and causes of temperature hot spots, which provides theoretical basis and data support for the optimization of heat dissipation design, overload capacity improvement and safe operation of the three-level grid-connected energy storage converter cabinet.

This article proposes a fast diagnosis method for single-open-switch and single-open-phase faults in T-type three-level inverter-fed dual-three-phase PMSM drives, based on magnetomotive force (MMF) balance. By extracting MMF balance degree features between the abc and def three-phase subsystems, this method enables hierarchical fault diagnosis across three stages: Identifying the fault-occurred three-phase subsystems, localizing the exact faulty bridge arm/phase within them, and pinpointing the faulty switch. First, the diagnosis is activated upon detecting abnormal MMF imbalance between subsystems, and the faulty subsystem is locked. Then, phase-sequence similarity analysis between the faulty subsystem's actual MMF and theoretical fault reference MMF narrows the fault scope to a specific bridge arm and preliminarily identifies potential faulty switch pairs. Finally, due to the difficulty in distinguishing open-switch faults between upper/lower arms and the neutral-point arm under low modulation indices, a hybrid modulation mode is introduced. This mode maintains three-level modulation for healthy bridge arms while applying two-level modulation to the faulty bridge arm, thereby enhancing fault characteristics for precise fault identification. Open-phase faults are diagnosed through synchronous alarms during consecutive positive and negative current half-cycles. The method is easy to implement, requires only a single feature and two thresholds without additional hardware or model parameters. Experiment demonstrate fault detection within 0.4 ms and accurate localization within 1/15 fundamental period under both dynamic and steady-state operating conditions.

AC excitation motors offer advantages such as constant frequency under variable speed and decoupled power in steady-state operation,making them suitable for applications like pumped storage and flywheel energy storage.However,these applications demand rapid emergency braking under high-inertia loads,which traditional mechanical braking strategies fail to meet.This study proposes a flexible braking strategy and parameter optimization method for high-inertia AC excitation motors.First,based on rotor structural characteristics,the strategy connects the rotor to a DC excitation source and the stator to multi-stage resistors,and derives the equivalent braking circuit.Second,a braking parameter optimization model is established,with multi-stage braking resistance,rotor excitation current,and resistor switching speed as variables;motor ratings and resistor power limits serve as constraints;and the shortest braking time is set as the objective.The model is solved using a genetic algorithm.Finally,multi-stage resistance braking results and influencing factors are analyzed via Matlab/Simulink simulations,and a 7 kW AC excitation motor platform is used to verify the simulations.Results show that the proposed multi-stage flexible braking strategy effectively reduces braking time,and optimized parameters achieve minimal braking duration while satisfying system power constraints,balancing braking efficiency and device economy.

The conventional current reconstruction strategies for three-level inverters require current sampling at varying instants, and employ modified PWM to compensate periodic current reconstruction dead zones (CRDZ) in high- and low-modulation regions as well as at sector boundaries. However, those approaches not only increase algorithm complexity but also introduce additional current harmonics due to asymmetric PWM. To address these issues, a non-invasive phase current reconstruction strategy with fixed sampling instants is proposed in this paper. First, this strategy employs a pair of redundant small vectors output at the start and center of the SVPWM cycle as effective current reconstruction vectors (ECRV), thereby simplifying the sampling process by fixing it at the start and center instants of the carrier. Second, a single-current-sensor topology that couples both phase and the DC-link neutral point branches is developed to eliminate CRDZ at sector boundaries. And in the low-modulation region, zero-voltage vector groups are utilized to replace redundant small-voltage vector groups for current sampling, thus eliminating CRDZ in this region. As a result, non-invasive phase current reconstruction without modifying the PWM is achieved across all areas within the hexagonal space voltage vector contour. Additionally, a mathematical method is provided to design the current reconstruction topology. Based on the switch states of selected ECRVs, a unified mapping matrix is established to map directly sampled currents and phase currents, using multiple coupled branches and their coupling times as independent variables. The coupling positions and coupling times are determined by solving the full-rank condition of the matrix. Finally, experimental results validate the effectiveness of the proposed strategy.

Deploying federated learning (FL) in mobile edge computing (MEC) networks has become a prevalent approach to distributed learning. However, the inherent heterogeneity in computing, transmission and data on edge devices (EDs) brings challenges in improving training efficiency and speed. Existing approaches primarily focus on increasing batch sizes or employing adaptive batching to expedite convergence, but often overlook the generalization ability of the model. In this paper, we propose an energy-efficient adaptive batching approach for FL in MEC networks, aiming at minimizing the energy consumption by balancing training efficiency and speed. Initially, we exploit the relationship between batch size and loss improvement while determining the optimal learning rate corresponding to the batch size and understanding the correlation among loss improvement, learning rate, and gradient noise scale (GNS). Then we dynamically adjust the batch size based on the GNS and propose a low-complexity approach for measuring GNS. Finally, we fine-tune the batch size by assessing gradient similarity on each ED to ensure an optimal level of gradient noise during training, thereby enhancing the model's generalization. Experiment results demonstrate the effectiveness of our approach with an approximate 50% and 20% reduction in energy consumption and time consumption compared with existing approaches.

ObjectivesAgainst the backdrop of energy transition and large-scale grid integration of wind power, and aiming at the increasingly complex electro-mechanical coupling characteristics of wind turbines with frequency-supported control, this study conducts theoretical and simulation analyses on the load characteristics of direct-driven permanent magnet synchronous generators (D-PMSG) under virtual synchronous generator (VSG) control.MethodsBased on multi-body dynamics and small-signal analysis theory, a linearized electro-mechanical coupling model of VSG-PMSG is constructed to characterize the dynamic relationship between loads and source-grid excitation, followed by an analysis of the response mechanisms of VSG-PMSG under impact and fatigue loads. A nonlinear electro-mechanical coupling model of VSG-PMSG with multi-time-scale interactions and multi-dynamic link effects is established. Simulation analysis of load characteristics of VSG-PMSG is conducted under typical disturbance scenarios, such as turbulent wind and grid impacts. Indicators including maximum load increase and equivalent fatigue load are selected to evaluate the effects of VSG control on turbine impact and fatigue loads.ResultsUnder turbulent wind excitation, VSG control can reduce the drivetrain fatigue load and the tower bottom side-to-side fatigue load. In addition, under grid-side frequency excitation, VSG control can induce drivetrain impact load and the tower bottom side-to-side impact load. However, these are significantly smaller than the transient impacts caused by three-phase short-circuit faults.ConclusionsVSG control alters the electrical and load characteristics of wind turbines, particularly affecting the drivetrain mode and the tower bottom side-to-side mode. The research findings provide a theoretical basis for control strategies and mechanical structure design of frequency-supported wind turbines.

With the widespread adoption of multi-chip parallel IGBT modules in high-voltage and high-power electronic systems, non-uniform junction temperature distribution caused by the asymmetric chip layout and thermal coupling has become a critical factor affecting module reliability and lifetime. However, chip-level temperature reconstruction remains challenging because internal temperatures are difficult to measure directly and thermal boundary conditions are often unknown. This paper proposes a non-invasive junction temperature inversion framework by coupling a high-fidelity three-dimensional finite element model with the conjugate gradient method. Unlike conventional forward finite element analysis, which requires predefined thermal loads and boundary conditions, the proposed method uses multi-point case temperature measurements as the only external thermal information to simultaneously identify unknown thermal parameters and reconstruct the internal non-uniform temperature distribution of multi-chip IGBT modules. The proposed framework is validated by numerical simulations and experiments on an Infineon FF450R17ME4 module under steady-state and power cycling conditions. In power cycling tests at 150 A and 270 A, the reconstructed junction temperatures agree well with infrared measurements, with an average absolute error below 1.5 °C. These results demonstrate that the proposed method establishes an effective link between accessible case-temperature sensing and internal chip-level thermal state estimation, providing a practical tool for thermal characterization, reliability assessment, lifetime prediction, and thermal management of multi-chip power modules.

PurposeThis study aims to understand the phenomenon of stress relaxation in the gate pin of press-pack IGBT devices due to the coupling effect of electrical, thermal and mechanical fields, gate pin stress relaxation life model based on multiphysics field simulation and the Arrhenius equation is established. Taking the press-pack IGBT single-chip device as the research object, the stress distribution of the gate pin of the press-pack device is analyzed through simulation. The stress relaxation experiment of the gate pin establishes the thimble stress relaxation life models under different temperatures, and the power cycling experiments of the press-pack device verify the accuracy of the life model.Design/methodology/approachFirst, the COMSOL simulation is used to establish a multiphysics field model of the press-pack IGBT device to simulate the internal stress distribution characteristics of the device. Second, the stress relaxation formula of gate pin is established through constant-temperature experiments to quantify cumulative fatigue damage, and gate pin stress relaxation life model is built based on the Arrhenius equation. Finally, power cycling experiments are conducted to obtain the variation law of the electrical and thermal characteristic parameters of the device during the stress relaxation process of gate pin and verify the accuracy of gate pin life model.FindingsThrough multiphysics field simulation and stress relaxation experiments, it is found that the load loss rate of gate pin has a linear relationship with the logarithm of time, and the stress relaxation rate of the gate thimble varies significantly under different temperatures. Gate pin shows cumulative fatigue damage; the case temperature of the thimble and the corresponding junction temperature of the chip both increase with the same trend in the power cycling experiment. By monitoring the case temperature of pin, the time when the load loss rate of the thimble decays to the threshold can be estimated in real time.Originality/valueThrough multiphysics field simulation and experimental verification, this study proposes a stress relaxation mechanism of the gate pin under the multiphysics field coupling effect in press-pack IGBT devices, as well as its impact on device reliability.

Herein, we develop a wireless temperature monitoring system to address the insufficient real-time, high-precision, three-dimensional temperature field monitoring data for large-scale wind power generators (LSWPGs) thermal design. Our system is based on SmartMesh IP wireless mesh networks. Our innovations include: 1) the development of high-temperature-resistant, centrifugal-force-resistant miniaturized wireless sensors, 2) implementation of the time-slotted channel hopping protocol to achieve sub-200 ms communication latency with a 99.999% packet delivery rate, 3) real-time temperature field reconstruction to achieve 95.9% hotspot identification precision and +/- 1 degrees C measurement accuracy. Based on experimental data, we perform a coupled temperature field-flow field analysis to optimize LSWPG cooling performance. High-fidelity wireless sensing networks are combined with multi-physics simulation, forming a closed-loop, real-time monitoring system for the thermal design and optimization of LSWPGs. The system is deployed in 6-26 MW permanent magnet synchronous generators (Dongfang Electric Machinery Co., Ltd.), including the world's largest 26 MW offshore wind turbine (as of 2024).

Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque-speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM based on the short-term dynamic feasible region constraint is proposed to effectively improve the response speed. Firstly, the dynamic trapezoidal domain operation boundary is obtained by analyzing the motor working point variation curve and considering factors such as the working temperature and trajectory control, which constitutes the torque-speed value and the dynamic constraint mechanism of trajectory planning. Secondly, based on the energy consumption model, the average thermal power is used to represent the torque overload limit condition, and a dynamic constraint method based on the short-term dynamic torque-speed operation boundary is proposed. Then, in order to reduce the computational load in the online millisecond-level response, a time-optimal trajectory optimization algorithm based on sequential least squares is proposed to calibrate the positioning time of the time-optimal trajectory under different working temperatures and angles. Finally, a simulation and experimental comparisons of the time-optimal trajectories under different angles and working temperatures are carried out to verify the effectiveness of the proposed method.

Even contact pressure distribution among submodules in a press-pack insulated gate bipolar transistor (PP-IGBT) is an important factor in reliability screening tests before engineering application. However, the current stress-sensitive method for contact pressure measurement is an invasive method where each measurement would change the contact pressure distribution of submodules. It is necessary to study a noninvasive measurement method for contact pressure distribution in PP-IGBT. In this article, a noninvasive measurement method based on the ultrasonic reflection coefficient is proposed to measure the contact pressure distribution within PP-IGBTs. First, the characteristic of ultrasonic wave propagation at the contact interface of two different materials is analyzed. The ultrasonic measuring platform for the contact pressure distribution in PP-IGBTs is designed, and the new assembly measured part has little effects on the pressure distribution within the device by different clamping force experiment. Second, an efficient contact pressure measurement system for PP-IGBTs is designed, and a calibration method of ultrasonic reflection coefficient for contact pressure measurement is proposed. Finally, the accuracy of the proposed ultrasonic measurement method for contact pressure distribution is verified by comparing the measurement results with those of the stress-sensitive film method, where the relative error of contact pressure within PP-IGBTs in three different conditions is less than 10%.