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关键词： Wind energy   Machine learning   Deep learning   Reinforcement learning   Multi-objective optimization   Smart control   Fault detection   2-DOF   Two-degree-of-freedom   5G-NG-RAN   5G-Next Generation-Radio Access Network   AI   Artificial Intelligence   AD-PSO-WOA   Adaptive Differential PSO-Guided Whale Optimization Algorithm   AEP   Annual Energy Production   AGA   Adaptive Genetic Algorithm   AGA-DDPG   Adaptive Genetic Algorithm + Deep Deterministic Policy Gradient   ANFIS   Adaptive Neuro-Fuzzy Inference System   AUC   Area Under the Curve   BPNN   Back-Propagation Neural Network   CFD   Computational Fluid Dynamics   CI/CD   Continuous Integration and Continuous Deployment   CNN   Convolutional Neural Network   CVSS   Common Vulnerability Scoring System   DBN   Deep Belief Network   DCNN   Deep CNN   DFIG   Doubly Fed Induction Generators   D-GABC   Density-based Grey Artificial Bee Colony   DQN   Deep Q-network   DDQN   Double DQN   DL   Deep Learning   DNN   Deep Neural Network   DRL   Deep Reinforcement Learning   DT   Decision Tree   EEMD   Ensemble Empirical Mode Decomposition   EMD   Empirical Mode Decomposition   ELM   Extreme Learning Machine   EMS   Energy Management System   ESS   Energy Storage System   EWT   Empirical Wavelet Transform   FFT   Fast Fourier Transform   GAN   Generative Adversarial Networks   GGWO   Genetic Grey Wolf Optimizer   GP   Gaussian Processes   GMDH   Group Method of Data Handling   GRNN   Generalized Regression Neural Network   GRU   Gated Recurrent Unit   GWO   Grey Wolf Optimization   HARO   Huber Adam Regression Optimizer   HHO   Harris Hawks Optimization   IGBT   Insulated Gate Bipolar Transistor   IoT   Internet of Things   JVAE   Joint Variational Autoencoder   KNN   K-Nearest Neighbor   LASSO   Least Absolute Shrinkage and Selection Operator   LC   Lagrangian Control   LiDAR   Light Detection and Ranging   LDA   Linear Discriminant Analysis   LR   Linear Regression   LIME   Local Interpretable Model-agnostic Explanations   LSTM   Long Short-Term Memory   LUBE   Lower Upper Bound Estimation   MAE   Mean Absolute Error   MAPE   Mean Absolute Percentage Error   MCP   Measure-Correlate-Predict   MEC   Multi-access Edge Computing   Meta-MARL   Meta Multi-Agent Reinforcement Learning   ML   Machine Learnin  

摘要： Wind energy plays a pivotal role in the global transition toward sustainable energy. However, its intermittent and stochastic nature presents challenges in achieving optimal performance, reliability, and seamless grid integration. Recent advances in machine intelligence—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—offer powerful tools to address these challenges across forecasting, control, maintenance, and diagnostics. This systematic review provides a comprehensive evaluation of how machine intelligence has contributed to the optimization of wind energy systems. These techniques have been applied to enhance turbine-level performance, reduce power losses, predict faults, and maximize energy yield under uncertain and dynamic conditions. Particular emphasis is placed on hybrid models that combine data-driven algorithms with physical dynamics and domain heuristics, enabling real-time, predictive, and autonomous wind farm operations. Furthermore, the study critically examines integration barriers such as noisy SCADA data, regulatory compliance, computational costs, and sustainability trade-offs. The findings highlight that multi-objective optimization—balancing energy production, system resilience, and cost efficiency—is central to the most successful implementations. Hybrid frameworks, explainable artificial intelligence (AI), edge computing, and transfer learning are identified as key enablers for scalable deployment. This review offers a comprehensive roadmap for the application of machine intelligence in advancing wind energy optimization and provides actionable insights for researchers, engineers, and policymakers committed to developing intelligent, adaptive, and sustainable wind power infrastructures.

关键词： Condition monitoring   V-I curve   Rogowski coil   PCB sensor   IGBT   Power semiconductor devices   Inverter  

摘要： As reliability is growing critical in power electronics systems, preventive maintenance for power semiconductor devices is crucial for extending system lifespan and improving robustness. However, conventional time-based maintenance often increases operational costs and unexpected downtime due to significant variation and uncertainty in the power semiconductor device's lifetime. In contrast, condition-based maintenance offers an optimized supervision scheme based on real-time health monitoring, reducing unnecessary costs and increasing overall system reliability. This paper presents a practical, non-intrusive, online condition monitoring system for detecting degradations in power semiconductor devices. The developed V-I curve monitoring system accurately measures V CE(SAT) - I C curves for IGBT and V F - I F curves for power diode during the normal operation of a full-bridge inverter. A novel conduction current ( I C ) sensing method based on a low-cost, tiny PCB sensor and a two-stage integrator was proposed. On-state voltage ( V ON ) is measured by a diode-based circuit, and case temperature ( T C ) is sensed by a thermocouple. Real-time V ON - I C - T C samples are captured simultaneously with 16-bit ADCs of the microcontroller and stored in the SD card. Acquired V-I curve data is analyzed using two statistical approaches to detect bond wire and solder layer degradations. Both the residual-based fit model and the multivariate T 2 outlier methods successfully detected abnormal conditions, and comparisons are made for these two approaches.

关键词： Loop thermosyphon   Vertically placed flat evaporator   Height differences   Start-up characteristics   IGBT cooling  

摘要： This study presents a comprehensive experimental investigation into the thermal-hydraulic performance and flow dynamics of a loop thermosyphon system incorporating a vertically oriented flat evaporator designed for insulated gate bipolar transistor (IGBT) module cooling. The effects of critical operational parameters, namely liquid filling ratio (31 %-92 %), heat load (100-900 W), and height difference between evaporator and condenser (0 cm, 20 cm, 40 cm, and 60 cm), on heat transfer characteristics, temperature uniformity, and startup behavior were systematically examined. Results reveal that increasing the height difference enhances the gravitational driving force, thereby expanding the operational heat load range and reducing the average evaporator temperature;however, it concurrently exacerbates temperature non-uniformity due to complex two-phase flow instabilities. Notably, an optimal filling ratio near 73 % was identified at zero height difference, where the system exhibits heightened sensitivity to filling variations and limited natural circulation capability. Detailed pressure measurements elucidate the interplay between liquid column height and flow resistance, highlighting a saturation phenomenon in driving pressure at elevated heat loads. The study further uncovers transient gas blockage during start-up, manifesting as short-lived dry-up and temperature oscillations, which detrimentally affect system stability. These phenomena are attributed to the coupled effects of liquid film dynamics, bubble behavior, and gravitational forces within the confined geometry of the flat evaporator. The findings provide critical insights into the underlying physical mechanisms governing loop thermosyphon operation with flat evaporators and offer practical guidelines for optimizing design parameters to achieve enhanced thermal management of high-power electronic devices.

关键词： Condition monitoring  

摘要： A new method for multi-parameter detection of bond wire lift-off, emitter current, and junction temperature using gate voltage waveforms and a convolutional neural network is proposed for the condition monitoring of power modules. This method was demonstrated to classify 80 levels for the full combination of various parameters. In addition, digital gate control (DGC) was utilized to improve not only the switching characteristics but also the detection accuracy. The experimental results show that the sensitivity of the gate voltage waveforms changed significantly due to the influence of the combined parameters. The detection accuracy depends on the control conditions of DGC, and optimized conditions achieved a high accuracy of over 96%, even for multi-parameter detection. © 2025 The Author(s)

摘要： A Snapback-Free and fast reverse recovery capability RC IGBT featuring the Passive Trench MOS and trench shield SBD is proposed and investigated by simulation. The PT-MOS is introduced at the Collector. The gate and the source of PT-MOS was shorted by the N-buffer layer, which realizes the reverse conduction ability without additional input signal. Additionally, the Schottky Barrier Diode (SBD) is integrated at Emitter. The SBD is shield by dual trench gates. Thus, the breakdown voltage will not be affected by the SBD. During forward-conduction stage, the PT-MOS is off-state. The snapback is eliminated by the P-well electron barrier. During backward-conduction stage, the PT-MOS and SBD are turn-on automatically to achieve reverse conducting ability. During reverse recovery stage, the SBD can effectively suppress hole injection from the body diode, thereby significantly reducing the reverse recovery charge. The simulation results indicate that, compared with conventional RC IGBT and FPL IGBT, the Proposed IGBT achieves a 67 % and 76.5 % reduction in reverse recovery charge, respectively. At the same turn-off loss E OFF of 29 mJ/cm 2 , the on-state voltage V ON of the Pro IGBT is reduced by 40 % and 11 % compared to the Con IGBT and FPL IGBT, respectively.

关键词： 电气节能   环保机械   变频器   伺服驱动   智能控制  

摘要： 在环保行业中,机械装备的电气能耗占据相当大的比重,电气节能技术的研究与应用显得尤为重要。本文分析环保机械装备的分类与特点,总结当前电气节能技术的应用现状和面临的挑战。从电气节能的基本原理出发,探讨主要的电气节能技术及其在环保机械中的应用。详细介绍变频器技术、伺服驱动系统和智能控制系统的节能效益,并提出电气节能技术的优化设计思路,以期为环保行业的电气节能技术提供参考。

关键词： Insulated gate bipolar transistors (IGBT)  

摘要： The health condition of insulate gate bipolar transistor (IGBT) modules directly determines whether the entire power electronics system can operate stably over the long term. Currently, IGBT module health monitoring methods mainly focus on single failure modes. However, in engineering applications, IGBT modules often undergo multiple types of aging due to the combined effects of electrical, thermal, and mechanical stresses. Moreover, the high-speed switching of IGBT modules generates significant self-heating, accurate junction temperature prediction is also critically important. To address the above issues, this paper proposes a multi-mode aging decoupling monitoring method for IGBT modules based on temperature sensitive electrical parameters (TSEPs) separation strategy. By analyzing the effects of junction temperature variation, gate oxide degradation, and bond wires lift-off on pre-threshold voltage change rate dVth-pre/dt, threshold voltage Vth, and auxiliary emitter voltage VeE, the specificity of these parameters to junction temperature and different aging modes are obtained, achieving multi-mode aging decoupling. The experimental results show that the proposed method can effectively identify IGBT modules gate oxide degradation or bond wires lift-off while predicting the junction temperature. It is also capable of decoupling and quantifying each aging mode degrees, and exhibits a certain level of immunity to load current variations. © 2025

关键词： Risk assessment  

摘要： In predicting the remaining useful life (RUL) of IGBT, the time series data (e.g., emitter-collector on-state voltage drop) characterizing the aging state of IGBT have the nonlinear characteristics of "slow change in the early stage and rapid degradation in the later stage", and are accompanied by long-term trends and short-term disturbances, which bring challenges to the stability and prediction accuracy of traditional modeling methods. In addition, the existing RUL prediction models mostly focus on point prediction or parameter interval prediction based on distribution assumptions, which is difficult to meet the demand for quantification of prediction uncertainty and risk assessment in engineering applications. To this end, this paper proposes an improved gated recurrent network model (QT-GAGRU) based on trend-residual decomposition and quantile regression for realizing point-interval prediction of IGBT RUL. First, the trend and residual decomposition of the time series are performed and modeled separately using the sliding average method to alleviate the impact of data non-stationarity on the prediction performance;second, the input difference-based gate-aware mechanism (GAGRU) is introduced into the gated recurrent unit to enhance the model's ability of modeling the mutation features in the nonlinear degradation process;and then, for the first time, this paper incorporates the nonparametric interval prediction method -quartile regression (QR) is introduced into the IGBT RUL prediction model to achieve the quantification of model uncertainty without relying on distributional assumptions. Finally, the experimental results on self IGBT aging dataset and NASA public dataset show that the accuracy of the model is better than the existing IGBT RUL prediction models in terms of point prediction, and the interval prediction coverage is high and the interval width is small, which verifies the validity and engineering utility value of the QT-GAGRU model. © 2025

关键词： Insulated gate bipolar transistors (IGBT)  

摘要： A field-stop IGBT including n-type and p-type channel metal-oxide-semiconductor (MOS) systems and Si/4H–SiC hetero-junction emitter (HEJE) is proposed. The n-MOS aside the primary gate is adopted to conduct electrons for realizing the enhanced device, the p-MOS aside the auxiliary gate is employed to extract holes during turn-off for decreasing energy loss (Eoff). The HEJE acts as potential barrier to hinder holes outflow for enhancing conductance modulation while reducing on-state voltage (Von) without falling breakdown voltage (VB). The device structure and performance are simulated and optimized by the Sentaurus TCAD, the operation mechanism is elucidated, the influence from the interfacial defects in Si/4H-SiC HEJE, p-MOS and n-MOS on device performance is analyzed respectively. The devised IGBT shows obvious decrease in Von and Eoff comparing to the counterparts without HEJE and p-MOS. This paper provides new scheme for designing superior IGBT. © 2025 Elsevier Ltd

关键词： 空间矢量脉宽调制   矢量控制   死区时间补偿  

摘要： 针对永磁同步电机(Permanent Magnet Synchronous Motors,PMSM)矢量控制(Field-Oriented Control,FOC)中采用软件实现空间矢量脉宽调制(Space Vector Pulse Width Modulation,SVPWM)所存在的成本高、速度响应慢、难以满足新兴控制需求等问题,设计了一种高速七步式SVPWM专用集成电路。该设计在优化SVPWM实现流程的同时,集成了死区补偿功能,有效提升了输出电压的精度。电路功能采用Verilog硬件描述语言实现,并通过逻辑综合生成门级电路。在100 MHz时钟频率下,所设计的SVPWM电路具有0.3 ns的时间余量,面积为124501μm^(2)。其输出的三路PWM信号频率约为1.53 kHz,相对误差仅为0.0015%,可满足转速在10000 r/min以下PMSM的正常运行需求。