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A review on simulation models of cascading failures in power systems

iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

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computers, Materials & Continua 2024年第9期80卷 4749-4765页

作者： Dennies Tsietso Abid Yahya Ravi Samikannu Basit Qureshi Muhammad Babar Department of Electrical Computer and Telecommunications EngineeringBotswana International University of Science and TechnologyPalapyePrivate Bag 16Botswana College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11586Saudi Arabia

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems *** computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this ***,accurately segmenting the Region of Interest(ROI)fromthermograms remains *** paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree *** proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground *** features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)*** findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of *** consistency of the method across both sides of the breast suggests its viability as an auto-segmentation ***,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

关键词： Breast cancer CAD machine learning ROI segmentation thermography

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Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

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npj Computational Materials 2024年第1期10卷 3237-3247页

作者： Vishu Gupta Kamal Choudhary Brian DeCost Francesca Tavazza Carelyn Campbell Wei-keng Liao Alok Choudhary Ankit Agrawal Department of Electrical and Computer Engineering Northwestern UniversityEvanstonILUSA Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA DeepMaterials LLC Silver SpringMD 20906USA

Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials *** essential component in the process of materials discovery is to know which material(s)will possess desirable *** many materials properties,performing experiments and density functional theory computations are costly and ***,it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available *** we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials(3D/2D,inorganic/organic,computational/experimental)*** evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases,i.e.,≈90%,with additional benefits in performance for extrapolation *** believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

关键词： neural network transfer

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Analysis of Electro-Magnetic Interference on Underground Cable According to Grounding Methods in AC/DC Hybrid Distribution Networks

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Transactions of the Korean Institute of electrical Engineers 2025年第5期74卷 784-792页

作者： Kwon, Oh-Seung Song, Jin-Sol Kang, Sung-Man Kim, Wook-Won Kim, Chul-Hwan Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Electrical Environment Research Center Korea Electrotechnology Research Institute Korea Republic of Smart Power Distribution Laboratory KEPCO Research Institute Daejeon Korea Republic of

Due to the proportion of renewable energy increase, DC systems are in demand and the study on AC/DC Hybrid system is ongoing. The current AC distribution system is characterized by a passive, unidirectional flow of power, which limits power control and causes power loss in AC/DC conversion for DC demands. The AC/DC Hybrid distribution system improves system operational efficiency by integrating DC systems into existing AC system, expanding the capacity for renewable energy integration, and reducing conversion losses from AC to DC. However, depending on the line configuration, AC/DC cables may be positioned adjacently, potentially causing electro-magnetic interference (EMI) between them. This paper proposes the best grounding methods of underground cable which is appropriate to the AC/DC Hybrid distribution system. The validation of the grounding methods of underground cable was performed by Electro-Magnetic Transients Program (EMTP) ATP-Draw. Copyright © The Korean Institute of electrical Engineers.

关键词： DC distribution systems

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Real-Time Economic Dispatch Approach for Wholesale Energy Market with Multi-Transmission-Node der Aggregation

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IEEE Transactions on Power Systems 2024年第4期40卷 3185-3197页

作者： Shao, Zhentong Wang, Weilun Eldridge, Brent Somani, Abhishek Wu, Lei Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Pacific Northwest National Laboratory RichlandWA99354 United States

Enabling the integration of distributed energy resources (DERs) into the wholesale market, as prompted by the FERC Order 2222, introduces substantial operational complexities. To align with the current wholesale energy market practice, regional transmission organizations (RTOs) are pursuing the aggregation of transmission-node-level DERs (T-DERs), in which each T-DER is a projected representation of all DERs in the distribution network underneath the transmission node, such as a virtual power plant (VPP). This paper advances a realtime economic dispatch (RTED) framework to facilitate the aggregation of multiple T-DERs, optimizing their participation in energy market operations. We enhance the distribution factors (DFs) based modeling to capture the impact of individual TDERs across the transmission network. Specifically, an innovative DF updating strategy is introduced in our methodology, incorporating a tailored K-Nearest Neighbors (KNN) predictor and a discrete PI corrector, along with transmission signals that sufficiently guarantee transmission constraints. These enhancements optimize the economic efficiency of the market clearing process, allowing numerous T-DERs to be involved in current energy market practices. The proposed method is tested on modified 24-bus and 118-bus systems via a rolling RTED process with real load data. The test results show that the proposed framework demonstrates marked improvements in minimizing system operation costs and satisfying the transmission network constraints. © 1969-2012 IEEE.

关键词： Distributed energy

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Broadband Impedance Matching for Wearable Magnetoinductive Waveguides

Broadband Impedance Matching for Wearable Magnetoinductive W...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Jenkins, Connor Kiourti, Asimina ElectroScience Laboratory Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43212 United States

ISBN: (纸本)9781946815200

Magnetoinductive Waveguides (MIWs) consist of coupled electrically small resonant elements that support the propagation of magnetoinductive waves. By exciting current on an element, a magnetic field is formed, leading to the generation of current on neighboring elements via Faraday's Law. This pattern continues to form a traveling wave. MIWs have been used in wireless power transfer applications, sub-wavelength imaging setups, and as data communication media for applications such as Magnetic Resonance Imaging (MRI) or in challenging Radio-Frequency (RF) environments such as underground, underwater or on the body. In particular, our previous work has focused on their application to wireless body area networks (WBANs) that seamlessly interconnect wearable devices and sensors. © 2025 USNC-URSI.

关键词： Inductive power transmission

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & Sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

AIMS Electronics and Electrical Engineering

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AIMS Electronics and electrical engineering 2023年第4期7卷 380-405页

作者： Dkhichi, Fayrouz Laboratory of Networks Computer Science Telecommunications & Multimedia Electrical Engineering Department Superior School of Technology Hassan II University of Casablanca Morocco

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m , 300 W/m , 700 W/m , 1000 W/m , 800 W/m and 400 W/m ) and by varying the temperature values (15°C, 35°C, 45°C and 5°C). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%;the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. © 2023 The Author(s).

关键词： Maximum power point trackers

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