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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Dohyun Jang In-Seok Song Hye-Won Yang Dong-Yoon Shin Jin-Hwan Lee Sang-Yong Jung Department of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Control System Research Team Research Engineer KCNC Suwon Korea Department of Electrical Engineering Chonnam National University Gwangju Korea

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

This paper presents the design of a 36kW V-type rib-less Interior Permanent Magnet Synchronous Machine (IPMSM) for EV propulsion, with a focus on optimizing motor efficiency through a PWM harmonic-based approach. The core of this design is an innovative analytical current harmonic model (ACHM) that specifically addresses iron losses in 3-phase 2-level voltage-source inverters. This model effectively captures the effects of PWM harmonics, significantly enhancing motor efficiency across various operating points. The efficiency improvements are vividly demonstrated in maps that highlight the impact of PWM harmonics on motor performance. The practical application of this PWM harmonic-focused design is validated through rigorous electromagnetic FEA and prototype testing, confirming its suitability for high-performance EV propulsion systems.

关键词： Permanent magnet machines Analytical models Computational modeling Transportation Pulse width modulation Propulsion Motors

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Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs 43

Quality Prediction of Injection Molding Process Using CNN an...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Yibo Li, Dewei Zhou, Yuanqiang Gao, Furong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai201100 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Guangzhou Hkust Fok Ying Tung Research Institute Department of Chemical and Biological Engineering Guangzhou511458 China

ISBN: (纸本)9789887581581

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks (CNN) and Parallel-Cascade Long Short-Term Memory (PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding, overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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WTConv-Adapter: Wavelet Transform Convolution Adapter for Parameter Efficient Transfer Learning of Object Detection ConvNets 8

WTConv-Adapter: Wavelet Transform Convolution Adapter for Pa...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Jia, Guanwei Xu, Lizhi Zhu, Liqiang Science and Technology Department Guoneng Shuohuang Railway Development Co. Ltd. Cangzhou062350 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

In the field of natural language processing, utilizing large-scale pre-trained foundational models for parameter-efficient transfer has become a standard paradigm to enhance training efficiency and model performance. However, its application in computer vision, particularly in object detection tasks, remains to be further explored. This study introduces WTConv-Adapter, a parameter-efficient transfer module for convolutional networks in object detection. The module integrates traditional convolution with wavelet transform convolution, achieving a lightweight design while maintaining a large receptive field and multi-frequency response capabilities. Experiments on object detection under complex lighting conditions demonstrate the significant advantages of introducing wavelet convolution in parallel, leading to improved detection performance. Additionally, its transferability was tested and compared with backbone freezing, full fine-tuning, and Conv-Adapter, showing favorable results in both parameter efficiency and performance (achieving a 1.8% performance improvement over full fine-tuning with fewer parameters and training iterations). Moreover, its performance in the presence of pre-processing networks was evaluated, revealing strong adaptability to changes in input feature distributions. © 2025 SPIE.

关键词： Wavelet transforms

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Cascading Failure Screening Based on Gradient Boosting Decision Tree for HVDC Sending-End systems with High Wind Power Penetration

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IEEE Transactions on Power systems 2025年

作者： Liu, Tianhao Yan, Jiongcheng Liu, Yutian Chung, Chi Yung Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong

In LCC-HVDC sending-end AC systems, cascading failures combined with the dynamic response of wind turbines (WTs) can lead to HVDC commutation failures. The resulting transient voltage disturbances cause WT tripping in sending-end systems. Cascading failures that involve the interaction between WTs and HVDC significantly limit the wind power transmitted by HVDC systems. This paper proposes an online cascading failure screening method based on gradient boosting decision tree (GBDT) for HVDC sending-end systems with large-scale WTs. First, a confidence level-based WT tripping model is proposed for cascading failure risk assessment considering a typical cascading failure propagation pattern. Then, Monte Carlo tree search is improved using a contrastive pruning technique to generate evenly distributed samples of cascading failures offline. Dynamic insecure scenarios are quickly identified using an improved support vector machine. Finally, GBDT is utilized to screen for cascading failures online by predicting subsequent high-risk failures using operating features. A dynamic weighting technique is proposed for GBDT to improve the fault prediction accuracy. Simulation results of a modified New England test system and the Ningxia provincial power grid in western China demonstrate that the proposed method can quickly screen for cascading failures considering the dynamic interaction between WTs and HVDC. © 1969-2012 IEEE.

关键词： Risk assessment

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Deep learning-based distributionally robust joint chance constrained distribution networks PV hosting capacity assessment

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Applied Energy 2025年 394卷

作者： Wang, Zihui Jia, Yanbing Han, Xiaoqing Wang, Peng Liu, Jiajie Key Laboratory of Cleaner Intelligent Control on Coal & Electricity Ministry of EducationShanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan China Department of Electrical and Electronic Engineering Nanyang Technology University Singapore

As distributed photovoltaic (PV) penetration in distribution networks (DNs) is increasing, it is essential to assess the PV hosting capacity (PVHC) to ensure the safe operation of DNs. This paper proposes a data-driven distributionally robust joint chance constrained (DRJCC) distribution networks PVHC assessment framework. Firstly, the spatiotemporal attention, projection, supervision, and Transformer architecture-based generative adversarial blocks are introduced to develop an augmented time series generative adversarial network (ATS-GAN), which, by integrating both supervised and unsupervised learning during the joint training process, better captures the spatiotemporal characteristics of PV and load power. Subsequently, leveraging the ATS-GAN, a Wasserstein metrics-based ambiguity set of PV and load power probability distributions is constructed, centered on the distributions induced by the generator neural network. Secondly, the DRJCC PVHC assessment model is proposed. A combination of the Bonferroni inequality and conditional value-at-risk approximation is adopted to transform the multivariate DRJCC model into a tractable conic formulation for efficient computation. Numerical results demonstrate that the proposed method effectively captures the spatiotemporal characteristics and uncertainties of multivariate distributions under multiple constraints, significantly reducing the conservatism typically associated with distributionally robust individual chance constraints. © 2025

关键词： Normal distribution

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Phase tracking approach for TDOA measurements in critical lunar spaceflights

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Science China(Information Sciences) 2020年第2期63卷 239-241页

作者： Wanhong HAO Cheng CHENG Daihui MO Zhenghao ZHANG Haitao LI Beijing Institute of Tracking and Telecommunications Technology Department of Electronic Engineering Tsinghua University Institute of China Electronic and Equipment System Corporation

Dear editor,In the coming years, China will make a series of profound breakthroughs in the field of lunar and deep space explorations, such as the Chang’E-4landing on the far-side of the moon, the Chang’E-5 sample return, and the ambitious Mars mission. These missions include a spacecraft powered descending flight, which is the most critical and

关键词： Doppler Phase tracking approach for TDOA measurements in critical lunar spaceflights

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Liquid Filling Quality control system for Beverage Industry Using Image Processing (CNN)

Liquid Filling Quality Control System for Beverage Industry ...

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International Conference on control, Automation and systems ( ICCAS)

作者： B. Chatchapol W. Autanan W. Anan Department of Electrical Engineering Faculty of Electric Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand Robotics Engineering and AI Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Electronic Engineering and Automation Control System Faculty of Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. Image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Liquids Image processing Velocity control Production Quality control Inspection Belts Filling Beverage industry Monitoring

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Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant systems

arXiv

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arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

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Comparative Analysis of Different Sliding Mode control Approaches for Load Frequency control in Edge of the Grid system

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Aich, Pritom Ahmed, Anis Shah, Rakibuzzaman Islam, Md Rabiul Roy, Naruttam Kumar Nasir, Ali Department of Electrical and Electronic Engineering Daffodil International University Dhaka Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Federation University Australia Mt HelenVIC Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored. The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Robust control

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