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6th International Conference on HVdC, HVdC 2024

作者： Yang, renxin Cai, Haiqing Su, Mingzhang Gu, Haohan Chen, Zhihao Cai, Xu Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai China Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc Guangzhou China National Energy Power Grid Technology R&d Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350387759

Using modular multilevel converter-based multi-terminal high voltage direct current (MMC-MTdC) transmission to integrate large-scale renewable power into the main power grid is an important development trend in China. To cope with the stability issues under weak grid conditions, different grid-forming control strategies have been proposed for the MMC in MTdC transmission systems. These control strategies have different structures due to different control targets and are difficult to unify. To solve this problem, a novel grid-forming control structure is proposed in this paper. The MMC emulates the synchronous generator (SG), and the submodule capacitor voltage emulates the SG rotor speed. On this basis, by utilizing the modulation control degree freedom, a unified dC-side controller is proposed to realize different control targets. Finally, the simulation model of a 4-terminal MMC-MTdC transmission system with renewable power integration is established in the PSCAd/EMTdC. The effectiveness of the proposed strategy is verified by the simulation results. © 2024 IEEE.

关键词： grid-forming MMC-MTdC multi-targets control unified control structure

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Synthetic Voltage and Capacity Profile regeneration Through Constant Voltage Sampling Process from Second Life Batteries Perspective

Synthetic Voltage and Capacity Profile Regeneration Through ...

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International Conference on Electrical Energy systems (ICEES)

作者： Vignesh S Jeyraj A-L Selvaraj Hang Seng Che Tey Kok Soon HiCoE UMPEDAC Level 4 WISMA R&D Institute for Advanced Studies Universiti Malaya Kuala Lumpur Malaysia HiCoE UMPEDAC Level 4 WISMA R&D Universiti Malaya Kuala Lumpur Malaysia Department of Computer System & Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Predicting State of Health (SoH) of Second Life Batteries (SLB) is a challenging aspect when the repurposer is often surrounded by uncertainty in historical data from Electric Vehicle (EV). The EV Original Equipment Manufacturer (OEM) hardly reveal any battery related data to public domain, it also reciprocates to the repurposer. However, SoH estimation demands minimal historic data to compare past and present state of the batteries. Thus, when repurposer examines SoH of SLB, it is mandated to adhere on either of reference Performance Test (rPT) or the one which is recommended in the cell datasheet by the cell manufacturer to compare the SLB performance with the past data. While executing this process, the repurposer often conducts multiple charge and discharge cycles to comment on SoH of the SLB cells. Hence, in order to reduce the processing time in conducting cycling experiments, and to predict the SoH of SLB, the authors proposed a novel approach on regenerating voltage and capacity profile through Constant Voltage Sampling (CVS) process considering SLB perspective. The regenerated curve is deemed as synthetic curve wherein the forward and backward prediction of SoH is carried out. raw test data is accessed from University of Michigan repository for serving as reference to the authors. The results of voltage and capacity profile are validated successfully for SoH ranges from 100% to 80%. With the help of CVS technique, discharge voltage and capacity profiles for SoH ranges from 80% to 50% were reconstructed successfully to define the health indicators governing SoH and remaining useful Life (ruL).

关键词： degradation Uncertainty Estimation Voltage Second Life discharges (electric) Batteries History Testing Synthetic data

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Improvements Through Forward Power Transfer in an Isolated Flyback Boost Converter for Solar Applications

Improvements Through Forward Power Transfer in an Isolated F...

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Ph.d. research in Microelectronics and Electronics (PrIME)

作者： Juan Cruz-Cozar Alfredo Medina-Garcia Jorge Perez-Martinez Cristina Martos-Contreras Noel rodriguez diego P. Morales R&D Funding Infineon Technologies AG Munich Germany Power System Engineering Infineon Technologies AG Munich Germany Electronics and Computer Technology University of Granada Granada Spain

ISBN: (数字)9798350386301

ISBN: (纸本)9798350386318

The concept of distributed renewable energy systems brings with it the need for further research in power electronics. The use of local solar energy, with technologies such as microinverters and micro-optimizers, implies the need to raise voltages to adapt them to a distribution bus from which the rest of the elements operate. In this work, the resonant behavior of a forward-flyback boost is studied, which allows to obtain a higher energy density than in a conventional flyback. For this purpose, an exhaustive comparison is made in terms of energy between both proposals, showing how the addition of a forward behavior improves the performance. In addition to the theoretical comparison, a converter is made to operate under the same conditions transferring only energy in flyback mode as well as in forward-flyback mode. This demonstrates how higher power density can be obtained thanks to forward transfer through capacitors, devices with better energy density.

关键词： renewable energy sources Inductance Power system measurements Capacitors Prototypes Solar energy Transformers

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Estimating Smart Grid Stability with Hybrid rNN+LSTM deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing recurrent Neural Networks (rNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of rNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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development and tests of sliding contact line-powered track transporter

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International Journal of Agricultural and Biological Engineering 2023年第4期16卷 68-75页

作者： Yihua Jiang Fang Yang Zihao Zhang Shanjun Li College of Engineering Huazhong Agricultural UniversityWuhan 430070China China Agriculture(Citrus)Research System Wuhan 430070China National R&D Center for Citrus Preservation Wuhan 430070China Citrus Mechanization Research Base Ministry of Agriculture and Rural AffairsWuhan 430070China Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River Ministry of Agriculture and Rural AffairsWuhan 430070China

In order to solve the problems of complexity of control systems and the limited power supply of traditional fuelpowered and battery-driven transporters operating in mountainous orchards,a sliding contact line-powered track transporter was designed and manufactured based on theoretical *** components of the transporter were developed such as a PLC-based(programmable logic controller)control system,a sliding contact power supply,and transmission system,and a position limit *** functions and performance of designed transporter were *** test results showed that the transporter exhibited a high stability of operation with an average operation velocity of 0.70 m/s,the maximum working slope of 48°,the maximum load of 400 kg,and the maximum remote control distance reaching 1482 *** the power supply circuit of sliding contact line was 108.8 m in length,the maximum voltage drop was 2.4 V,and the maximum power loss was 174.72 W,which were close to the theoretical calculation *** a single power supply cabinet,the transporter can operate normally for a maximum track distance of 175.69 *** the technical indicators of the transporter met the design requirements,and the above-mentioned problems such as complexity of the control system and limited energy supply of the traditional mountain orchard transporter were well *** study can provide reference for the design and optimization of mountain orchard transporter.

关键词： mountain orchard transporter remote control sliding contact line voltage drop

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Fourier Transform and Machine Learning for real-Time Monitoring of Froth Flotation in Mining Industry 7

Fourier Transform and Machine Learning for Real-Time Monitor...

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7th IEEE International Congress on Information Science and Technology, CiSt 2023

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Hasidi, Oussama Amar, Oumkeltoum Chafiqui, Youssef Marrakech Morocco Marrakech Morocco Marrakech Morocco R&d and Engineering Center Reminex Managem Group Marrakech Morocco Faculty of Sciences Semlalia Marrakech Morocco Seia Departement Rabat Morocco Computer Systemes Engineering Laboratory Faculty of Sciences Semlalia Marrakech Morocco

ISBN: (纸本)9781665461337

The process of froth flotation is widely used in the mining industry to separate minerals from ores. Maintaining the froth concentration grades at a stable level is crucial for efficient separation of valuable minerals. The classical monitoring methods of the mineral concentration grades currently used in the mining industry are expensive and time-consuming. These methods typically require manual sampling and laboratory analysis, which can delay decision-making and increase costs. To address this issue, we propose a novel approach that employs Fast Fourier Transform (FFT) and Machine Learning (ML) techniques to monitor the froth concentration grades in real-time. A real-world industrial image dataset is transformed from pixel intensities to froth descriptors using FFT, enabling the identification of complex patterns of the flotation froth. The extracted features are used as input to a Machine Learning and Neural Network models to predict the concentrations of Pb, Fe, Cu, and Zn in the cleaner banks of the Lead flotation circuit. We train and test our approach using a labeled dataset from the industrial flotation circuit and evaluate its performance using a new industrial dataset. The results show that our approach achieves a precision of up to 4.16 MAE for the concentrate grades of Zn, Pb, Fe, and Cu, indicating significant potential for real-time monitoring of the froth concentration grades using the FFT-ML approach. The proposed approach has the potential to improve the operational performance and efficiency of complex polymetallic flotation circuits in mineral processing. © 2023 IEEE.

关键词： Image processing

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domain Connection based Unsupervised domain Adaptation for Semantic Segmentation

Domain Connection based Unsupervised Domain Adaptation for S...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chunze Yang Xiaodong Zhang Peiyuan Tang Haoran Yuan Haojie Xin Zijiang James Yang School of Computer Science and Technology Xi ’an Jiaotong University Xi’an China School of Computer Science and Technology Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China Technology R&D Department Synkrotron Inc. Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Collecting and annotating data for semantic segmentation can end up costing a lot of time and energy. Unsupervised domain Adaptation (UdA) for semantic segmentation allows models trained on certain source domain data (such as the GTA synthetic dataset) to be applied to certain target data (like the Cityscapes real dataset), significantly decreasing the requirement for target-domain manual pixel-level *** this paper, we suggest a cross-domain unified semantic segmentation network training framework, the Attention distribution Adaptation Network (AdAN). It (1) proposes the Content Consistency Transformation (CCT), which maps source domain data to an intermediate domain that has a data distribution that is comparable to that of the target domain, and (2) introduces the Attention Adaptation Module (AAM), which bolsters the synchronization of attention between the intermediate domain and the target domain. AdAN achieved unprecedented mIoUs of 77.2 and 68.8 on GTA→Cityscapes and Synthia→Cityscapes, respectively, corresponding to improvements of +1.3 and +0.6 over the state of the art.

关键词： Training Adaptation models Semantic segmentation Manuals Signal processing data models Active appearance model Synchronization Speech processing Synthetic data

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radar Based Gesture recognition in Embedded systems

Radar Based Gesture Recognition in Embedded Systems

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International Conference on Electronics Technology (ICET)

作者： Tingxuan Liang Yue Lin Hongtao Xu Department of Microelectronics State Key Laboratory of Integrated Chips & System Fudan University Shanghai China R&D ICLegend Micro Nanjing China

Gesture recognition has played a significant role in human-computer interaction. Millimeter-wave radar sensor is a class of necessary sensor for human gesture recognition in an indoor condition due to its advantages of private protection, low hardware cost. However, for gesture recognition application, it is difficult for us to meet the requirements of high accuracy and real-time simultaneously in embedded system. Unlike other conventional method which increase the cost of hardware of embedded system to improve detection accuracy, in this paper, we proposed a radar-based gesture recognition in low power consumption and low-cost embedded system by using 24 GHz millimeter-wave radar with 1 transmitting antenna and 2 receiving antenna channels. Besides, 2d trajectory information on XY coordination and lightweight deep neural network have been used to distinct ten air-writing numbers. As a result, this system could reconstruct the air-writing symbols and recognize numeric character 0-9 with 95.2% accuracy in real time.

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Transformer-Based Model Applied to Customer demand Forecasting: A Comparative Analysis

Transformer-Based Model Applied to Customer Demand Forecasti...

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International Conference on Intelligent Computing in data Sciences (ICdS)

作者： Ahmed Ouaddi Jamal Benhra R&D Engineer intern ECC Computer science engineer ESITH Casablanca Morocco LARILE Laboratory ENSEM Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

This paper presents a novel approach for forecasting customer demand using a Transformer-based model, benchmarked against Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Beginning with an in-depth analysis of the fundamental principles of each model, the study conducts extensive hyperparameter optimization to determine the optimal settings for each technique. Performance evaluation is conducted based on the models' accuracy, efficiency, and adaptability to various market conditions, with a particular focus on the advanced attention mechanisms of the Transformer model for capturing long-range dependencies in data. The comparative analysis highlights the strengths and weaknesses of each approach, providing valuable insights into their practical applications in demand forecasting. Findings demonstrate the potential of the Transformer model to outperform traditional methods like CNN and LSTM in certain scenarios, offering a robust tool for businesses seeking to enhance their forecasting capabilities. The insights from this study provide a roadmap for selecting the most suitable forecasting technique, enabling businesses to better manage inventory, optimize supply chains, and meet customer demands more effectively.

关键词： Adaptation models Analytical models Accuracy Supply chains demand forecasting Predictive models Transformers data models Convolutional neural networks Long short term memory

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Conflict Mechanism Analysis Method for Electric Vehicles Participating in Multiple Types of Power Grid Ancillary Services

Conflict Mechanism Analysis Method for Electric Vehicles Par...

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2023 International Conference on Power system Technology, PowerCon 2023

作者： Xue, Yinjuan Chen, Qifang Cai, Haiqing Xia, Mingchao Gu, Haohan School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350300222

As the high-quality flexible resource of the grid, electric vehicles (EVs) can provide multiple auxiliary services for the grid at the same time. However, due to the one-way charging and discharging of EVs, as well as the limitation of power and capacity, which may cause availability conflicts when EVs participate in different auxiliary services simultaneously. This paper focuses on the analysis of the conflict mechanism of EVs participating in the secondary frequency regulation and peak shaving ancillary services at the same time. The impact mechanism between different auxiliary services in this conflict mode is revealed from the two aspects of limit conflict and charging and discharging instruction conflict, and the results of availability conflict of the two types of auxiliary services are verified and analyzed through simulation examples. © 2023 IEEE.

关键词： Frequency modulation

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