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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Boshkovski, Filip Nicholas, Carl Zhang, Zichen Paret, Paul Ngo, Khai. D. T. Lu, Guo-Quan Virginia Tech Department of Materials Science and Engineering Blacksburg United States Virginia Tech Cpes Bradley Department of Electrical and Computer Engineering Blacksburg United States National Renewable Energy Laboratory GoldenCO United States

ISBN: (纸本)9798350316445

Double-side cooled power modules are being developed for next-generation traction inverters due to their better heat extraction, lower profile, and lower parasitic inductances. However, due to their rigid structure, they cause reliability concerns arising from high thermo-mechanical stresses at the interconnection joints in the module. In this work, a materials-based approach using rigid encapsulants is presented for reducing thermo-mechanical fatigue. Finite-element thermo-mechanical simulations were performed to examine the effects of the elastic modulus and coefficient of thermal expansion of epoxy-based encapsulants on the bond deformation inside a double-side cooled power module. It was found that a rigid encapsulant with a high modulus of 6.0 GPa or above and a coefficient of thermal expansion around 20 ppm/°C would improve the thermo-mechanical reliability of double-side cooled power modules by decreasing the permanent bond deformation inside the modules by 50-60%. © 2023 IEEE.

关键词： double-side cooled modules rigid encapsulants thermo-mechanical reliability

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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Customer-Level Photovoltaic Disaggregation With Customer-Specific Exemplars

Customer-Level Photovoltaic Disaggregation With Customer-Spe...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Anne Stratman Wei Qiao Liyan Qu Department of Electrical and Computer Engineering Power and Energy Systems Laboratory University of Nebraska-Lincoln Lincoln NE USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Residential photovoltaic (PV) systems, which have become widely integrated into distribution systems, are typically installed behind-the-meter (BTM). However, accurate estimates of the BTM PV generation are necessary for load forecasting, network control, and demand response, so the measured net load data must be disaggregated into separate measurements of PV generation and pure load. This paper proposes a dictionary learning-based, customer-level PV disaggregation framework based on constructing customer-specific dictionary atoms. First, the observable data of a reference home is used to disaggregate the data of another fully observable home. The disaggregation is formulated as dictionary learning problem and solved via lasso regression. Then, synthetic dictionary atoms are constructed as a weighted linear combination of the PV generation and pure load profiles of the reference home, and the estimated profiles of the second observable home. The weights are optimized to minimize the disaggregation error of the known profiles of the second observable home. Finally, the data of the target home is disaggregated initially. Then, the optimized weights are used to construct synthetic exemplars using the reference home's data and the initial disaggregation estimates of the target home. The proposed method is compared to the case where synthetic exemplars are not used and is tested under a variety of weather conditions. Case study results show that the proposed framework reduces the disaggregation error by $3.27-81.01 \%$ , with a mean reduction of $\mathbf{2 0. 2 4 \%}$ across all test days.

关键词： Photovoltaic systems Dictionaries Accuracy Load forecasting Machine learning Solar energy Atoms Demand response Weighted sum model Testing

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network 2

Research on Deep Learning Model of Feature Extraction Based ...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States Graduate School of Art & Science New York University New York United States Viterbi School of Engineering University of Southern California Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States School of Computer Science Carnegie Mellon University Pittsburgh United States University of Houston Cullen College of Engineering Houston United States

ISBN: (纸本)9798350360240

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. © 2024 IEEE.

关键词： Convolutional neural networks

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Electric Vehicle Performance Enhancement Utilizing Hybrid Energy Storage systems

Electric Vehicle Performance Enhancement Utilizing Hybrid En...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Hossam M. Hussein SM Sajjad Hossain Rafin Mahmoud S. Abdelrahman Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Fl USA

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

Energy storage systems (ESSs) are vital in various applications, including electric vehicles (EVs) and microgrids. In comparison, lithium-ion batteries (LIBs) are widely used in ESSs, and challenges such as power density, thermal management, and cell voltage balance prompt the exploration of alternative solutions. Integrating supercapacitors (SCs) alongside batteries in hybrid energy storage systems (HESS) presents a promising solution. SCs can provide high bursts of power, extending battery lifespan and improving efficiency, especially during high-demand scenarios like acceleration and braking. Optimizing energy management strategies (EMS) is crucial to minimize operational costs and ensure practical battery usage within HESS. Different driving cycles, such as the New European Driving Cycle (NEDC) and World Harmonized Light Vehicles Test Procedure (WLTP), provide insights into real-world driving conditions. HESS configurations and operational modes, including traction and regenerative braking, are essential for efficient EV operation. This study uses simulation models to investigate EVs' performance with HESS under various driving conditions, highlighting the system's stability and adaptability.

关键词： Lithium-ion batteries Power system measurements Supercapacitors Propulsion Power system stability Thermal management Stability analysis Energy management Thermal stability Strain

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Data Fusion-based Hybrid Framework for Cyber-physical Security of the Smart Grid

Data Fusion-based Hybrid Framework for Cyber-physical Securi...

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Resilience Week (RWS)

作者： Sharon A. Boamah Reynold Mathieu Victor Faillace Dennis Agnew Janise McNair Arturo Bretas Department of Electrical and Computer Engineering University of Florida Gainesville FL Distributed Systems Group Sandia National Laboratory

ISBN: (数字)9798350388985

ISBN: (纸本)9798350388992

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid.

关键词： Simulation Data integration Data models Stability analysis Smart grids Communication networks Security Reliability State estimation Resilience

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Privacy-Preserving Load Forecasting via Personalized Model Obfuscation

Privacy-Preserving Load Forecasting via Personalized Model O...

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IEEE General Meeting Power& Energy Society

作者： Shourya Bose Yu Zhang Kibaek Kim Department of Electrical & Computer Engineering University of California Santa Cruz CA Mathematics and Computer Science Division Argonne National Laboratory IL

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.

关键词： Training Privacy Load forecasting Federated learning Simulation Predictive models Data models Smart meters Voltage control Load modeling

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