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A Gaussian Process-Regularized Graphical Learning Method for Distribution System State Estimation Using Extremely Scarce State Variable Labels

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IEEE Transactions on Smart Grid 2025年

作者： Hu, Jiaxiang Xu, Qianwen KTH Royal Institute of Technology Electric Power and Energy systems School of Electrical Engineering and Computer Science Stockholm11428 Sweden

Learning-based distribution system state estimation (DSSE) methods typically depend on sufficient fully labeled data to construct mapping functions. However, collecting historical labels (state variables) can be challenging and costly in practice, resulting in performance degradation for these methods. To fully leverage low-cost unlabeled historical measurement data, this article proposes a Gaussian process (GP)-regularized semi-supervised learning method for DSSE models, aiming at achieving feasible estimation precision using minimal state variable labels while also providing valuable interval estimation of state variables. Firstly, a structure-informed graphical encoder is established to generate appropriate node embeddings. A tailored GP-regularized learning method is then developed to model the intermediate latent space using these embeddings. It constructs the unlabeled embeddings by a weighted combination of labeled space vectors, with the weights determined by a kernel function, thereby forming additional supervision and regularizing the learning process of the network in the latent space. The regularized embeddings are then fed into a decoder to yield estimation outcomes. This procedure enables the proposed method to model intrinsic correlations across measurement data, as well as capture essential patterns related to DSSE even using extremely limited state labels. The trained DSSE models can thus adapt to the domain of new measurements. Lastly, the decoder outcomes and related latent embeddings are processed through a composite GP kernel to further derive the interval estimation of state variables, enabling uncertainty quantification. Experimental results demonstrate the effectiveness of the proposed method in handling extremely limited historical state labels and accurately quantifying the uncertainty of state variables. © 2010-2012 IEEE.

关键词： Self-supervised learning

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A Novel Rectification Efficiency Optimization Method of Receiver for Wireless power Transfer

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IEEE Transactions on Industry Applications 2025年第3期61卷 3969-3981页

作者： Ding, Jianying Jin, Ke Xiao, Yiwen Zhai, Zehao Zhu, Xirui Zhou, Weiyang Li, Xing Chen, Zhongwei College of Automation Engineering Jiangsu Key-Laboratory of New Energy Generation and Power Conversion Nanjing University of Aeronautics and Astronautics Nanjing210016 China Chervon Technology Research Institute Nanjing211100 China AVIC Leihua Electronic Technology Research Institute Department of Electrical Engineering Wuxi214063 China Aerospace Systems Engineering Shanghai Shanghai200240 China Co. Ltd. Suzhou215028 China

Microwave wireless power transmission (MWPT) has become a current research hotspot due to its potential to address problems such as power supply in remote areas, short drone flight time, and the lack of non-renewable resources. The microwave receiver plays a crucial role in the MWPT system by receiving RF power and converting it into DC power for use by the load. However, uneven power distribution at the receiver and changes of the output load will lead to mismatch loss and rectification efficiency reduction of the receiver, which greatly limits the application of MWPT. To address the problem, this article proposes a novel method for optimizing receiver of the MWPT system. Firstly, a self-switching rectifier is proposed, which can achieve adaptive rectification without increasing power loss, and has a simple structure and lower cost. Secondly, the power distribution law of the receiver is revealed, and on this basis, an effective power combining strategy is given to improve the power capacity of the receiver and balance the output of each branch. Then, an automatic impedance matcher without adding a microcontroller is presented to achieve automatic impedance matching under a wide output load range. Finally, the correctness and feasibility of this method are verified through experiments. © 1972-2012 IEEE.

关键词： Inductive power transmission

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Study on the Effect of Ultrasonic Impact Treatment on the Impact Toughness of S355J2 Steel Welded Joints

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Journal of Materials engineering and Performance 2025年 1-13页

作者： Gu, Bangping Yang, Yuchen Zhuo, Jingshu Lai, Jintao Xu, Guanhua Gao, Liqiang Yue, Cong Logistics Engineering College Shanghai Maritime University Shanghai 201306 China Department of Mechanical and Electrical Engineering Shaoxing University Shaoxing 312000 China State Key Laboratory of Fluid Power & Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou 310027 China

This study thoroughly investigates the effect of Ultrasonic Impact Treatment (UIT) on the impact toughness of S355J2 steel welded joints prepared using gas tungsten arc welding (GTAW). The study systematically analyzes the mechanisms by which UIT affects impact toughness through Charpy impact tests, optical microscopy, scanning electron microscopy (SEM) analysis, and microhardness measurements. The experimental results indicate that the average impact toughness of the welded joints significantly increased to 66.02 J/cm² after UIT, representing a 45.39% improvement. Regular UIT significantly improved the surface quality of the samples, reducing surface roughness, enhancing surface uniformity, and effectively minimizing stress concentration, thereby inhibiting crack propagation. Additionally, by refining the surface grain size, UIT enhanced the surface hardness of the material. Observations from SEM revealed the mechanisms by which UIT strengthens and toughens the impact toughness of the welded joints, showing significant plastic deformation in the impact fracture zone, enlarged void areas, and a transformation in the fracture characteristics of the radiative zone. These changes suggest that UIT enhances the material’s resistance to crack propagation, increases energy consumption during crack growth, raises fracture stress, effectively slows down material degradation, and even achieves crack suppression. © ASM International 2025.

关键词： fracture morphology impact toughness microhardness ultrasonic impact treatment (UIT) welding

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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An Efficient Test Scheduling Method Based on Dynamic Pairing

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Kim, Heetae Yun, Hyojoon Yoon, Doohyun Kang, Sungho Yonsei University Computer Systems and Reliable SoC Laboratory Department of Electrical and Electronic Engineering Seoul03722 Korea Republic of

Test scheduling is a process that manages tests within a System-on-Chip (SoC) to minimize test time by allocating test resources and adjusting priorities. Efficient test scheduling offers cost saving opportunities by reducing test time without compromising test coverage. Since test scheduling is a NP-hard problem, conventional methods adopt optimization or heuristic algorithms that leverage metrics of each test. However, due to the interdependence of tests based on how limited resources are allocated, finding the optimal solution to minimize test time is challenging. In this paper, a dynamic pairing algorithm is proposed to consider the mutual influence of tests on each other in the test scheduling process. The proposed method identifies the available test resources for a specific pair and minimizes the test time of the paired modules under identified constraints. Additionally, the proposed algorithm employs a heuristic-based approach to test scheduling to reduce the CPU time needed for scheduling. The proposed heuristic algorithm sequentially schedules test target modules and determines the optimal test schedule through dynamic pairing. Experiments have been carried out on diverse benchmarks and under various constraint conditions. The results indicate that the proposed method achieves shorter test times on average in comparison to conventional methods. © 2025 IEEE. All rights reserved.

关键词： System-on-chip

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Machine Learning for Scalable and Optimal Load Shedding Under power System Contingency

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IEEE Transactions on power systems 2025年

作者： Zhou, Yuqi Zhu, Hao National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO80401 United States The University of Texas Chandra Family Department of Electrical & Computer Engineering AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. © 1969-2012 IEEE.

关键词： Decentralized control

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Temperature Field Analysis of High Speed Permanent Magnet Motor Considering Wind Friction Loss 11th

Temperature Field Analysis of High Speed Permanent Magnet Mo...

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11th Frontier Academic Forum of electrical engineering, FAFEE 2024

作者： Zhou, Ze-Yu Xu, Bo-Wen Ma, Ji-En Zhang, Zhi-Ping Qiu, Lin Fang, You-Tong College of Electrical Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Gree Electric Appliance Co. Ltd Zhuhai519070 China

ISBN: (纸本)9789819787791

Permanent magnet motor is attracted widespread attention due to the high efficiency and power density, but demagnetization caused by overheating can affect their lifespan and operational reliability. This paper focuses on the research of high-speed permanent magnet synchronous motors with forced air cooling, analyzes the air friction losses and the electromagnetic losses of the motor, improves the calculation accuracy by considering the influence of rotor surface roughness and axial cooling wind speed in the air gap on the air friction losses of the rotor;On this basis, finite element and thermal network methods are used to construct coupling analysis models for the electromagnetic field and temperature field of the motor. The experimental verification results show that the thermal network model significantly improves the accuracy of motor temperature field analysis and have the advantage of high computational efficiency;Finally, the validated model of the motor ventilation structure is used for optimization analysis, the results show that the ventilation structure with four hole channels has advantages in motor heat dissipation. The above research provides theoretical support for breakthroughs in motor thermal management technology and references for motor design and optimization. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Small-Signal Analysis and External Ramp Design for Multiphase Current-Mode Constant On-Time Control with Phase Overlapping

Small-Signal Analysis and External Ramp Design for Multiphas...

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Annual IEEE Conference on Applied power Electronics Conference and Exposition (APEC)

作者： Sundaramoorthy Sridhar Qiang Li The Bradley Department of Electrical and Computer Engineering Center for Power Electronic Systems Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

High-current processor core voltage regulators (VRs) use current-mode constant on-time (COT) control to simplify phase interleaving and achieve high control bandwidth. Reducing VR bus voltage and increasing phase count results in steady-state phase overlapping within their operating duty range. With phase overlap, a smaller external ramp reduces the inner current loop stability margin and introduces large magnitude peaks in control-to-output response. These peaks could lead to double crossover, reduce gain margin, and make high-bandwidth outer voltage loop unstable in phase overlapping regions. Increasing the ramp damps these peaks and improves the gain margin but reduces the phase margin at the target bandwidth. Hence, this paper simplifies infinite-order describing function model and provides external ramp design guidelines to attain desired stability margin at the target bandwidth. The simplified model and small-signal analysis are validated using SIMPLIS simulation and experiments from a six-phase current-mode COT buck converter.

关键词： Regulators Design methodology Transfer functions Process control Bandwidth Stability analysis Resonance power electronics Steady-state Voltage control

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Effect of Magnetic Couplings on Conducted EMI of GaN-Based PFC Converter

Effect of Magnetic Couplings on Conducted EMI of GaN-Based P...

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Annual IEEE Conference on Applied power Electronics Conference and Exposition (APEC)

作者： Tyler McGrew Qiang Li The Bradley Department of Electrical and Computer Engineering Center for Power Electronic Systems Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Wide-bandgap power devices have enabled power supplies to increase in density while maintaining high efficiency. However, EMI issues remain one of the most challenging aspects when designing a power converter. These issues are often caused by parasitic near-field couplings which can degrade or bypass the attenuation of the EMI filter. This paper investigates how parasitic magnetic couplings impact the conducted noise of a high-frequency PFC converter using PCB-winding inductor. Analytical models are presented for both differential mode (DM) and common mode (CM) noise, including the effects of magnetic couplings between the PFC inductor and EMI filter. Then, three ferrite inductor core designs are evaluated to demonstrate how the core’s geometry and permeability affect its leakage magnetic field. Choosing the optimal core design is shown to significantly reduce the magnetic field outside the PFC inductor, which reduces DM noise by more than 20 dB and reduces CM noise by 7 dB.

关键词： Couplings Ferrites Magnetic cores Magnetic separation Atmospheric modeling Electromagnetic interference Noise Air gaps Permeability Inductors

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Innovative dual-mode device integrating capacitive desalination and solar vapor generation for high-efficiency seawater desalination

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Journal of Energy Chemistry 2025年第1期100卷 171-179页

作者： Jiacheng Wang Zhaoyu Chen Ruduan Yuan Jiaxin Luo Ben Zhang Keju Ji Meng Li Juanxiu Xiao Kuan Sun National Innovation Center for Industry-Education Integration of Energy Storage Technology MOE Key Laboratory of Low-grade Energy Utilization Technologies and SystemsCollege of Energy&Power EngineeringChongqing UniversityChongqing 400044China Xuefeng Mountain Energy Equipment Safety National Observation and Research Station CQU-NUS Renewable Energy Materials&Devices Joint LaboratoryChongqing UniversityChongqing 400044China Southwest Technology and Engineering Research Institute Chongqing 400039China Jiangsu Provincial Key Laboratory of Bionic Functional Materials College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016JiangsuChina State Key Laboratory of Marine Resources Utilization in South China Sea Collaborative Innovation Center of Marine Science and TechnologySchool of Marine Science and EngineeringHainan UniversityHaikou 570228HainanChina

Solar-driven interface evaporation with high solar-to-steam conversion efficiency has shown great potential in seawater ***,due to the influence of latent heat and condensation efficiency,the water yield from solar-driven interface evaporation remains insufficient,posing a significant challenge that requires *** this work,we designed a dual-mode high-flux seawater desalination device that combines solar-driven interface evaporation and capacitive *** utilizing coupled desalination materials exhibiting both photothermal conversion and capacitance activity,the device demonstrated photothermal evaporation rates of 1.41 and 0.97 kg m^(-2)h^(-1)for condensate water yield under one-sun ***,the device exhibited a salt adsorption capacity of up to48 mg g^(-1)and a salt adsorption rate of 2.1 mg g^(-1)*** addition,the salt adsorption capacity increased by approximately 32%under one-sun ***,photo-enhanced capacitive desalination performance was explored through numerical simulations and theoretical *** calculations and characterizations confirmed that the defect energy levels formed by the introduction of sulfur vacancies can effectively widen the light absorption range,improve photothermal conversion performance,and stimulate more photoelectrons to participate in capacitive ***,the electron distribution state of molybdenum disulfide with sulfur vacancies and surface defect sites contributes to ion/electron transport at the solid-liquid *** work provides a novel pathway for integrating solar vapor generation with other low-energy desalination technologies.

关键词： Capacitive deionization Solar vapor generation Molybdenum disulfide Defect engineering

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