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2023 China automation Congress, CAC 2023

作者： Li, Ting Xue, Wei Yang, Xinya Wu, Dongchang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models. © 2023 IEEE.

关键词： Efficiency

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Improved YOLOv8 for High-Precision Detection of Rail Surface Defects on Heavy-Haul Railways

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Chinese Journal of Electronics 2025年第3期34卷 802-815页

作者： Cao Yuan Ma Long Sun Yongkui Wang Feng Su Shuai National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing 100044 China School of Automation and Intelligence Beijing Jiaotong University Beijing 100044 China

The complex infrastructure and harsh conditions of heavy-haul railways result in frequently and rapidly deteriorating rail surface defects. Accurate detection of these defects is essential. To solve the problem of low detection precision caused by complex background interference, significant variation in defect scales, and similar features between different types of defects, a high-precision rail surface defect detection method for heavy-haul railways based on an improved YOLOv8 is proposed. First, the original grayscale images are preprocessed to reduce background noise interference. Then, the designed scale variation adaptation module is introduced to mitigate the impact of significant scale variations in the target defects. Additionally, a bidirectional feature pyramid network is incorporated to enhance feature fusion effectiveness. Furthermore, a small target detection head is introduced to improve the detection performance of small-scale defects. Lastly, network performance is optimized by replacing the original loss function with wise-intersection over union. Experimental results demonstrate that the improved model achieves a mean average precision at 50% intersection over union (mAP50) value of 0.975, representing a 4.13% improvement in precision and a 7.75% increase in recall compared to the baseline model. The improved model effectively detects typical defects such as spalling, shelling, and corrugation, providing valuable technical support for field maintenance personnel.

关键词： Heavy-haul Rail Surface defect detection Improved YOLOv8

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Akbarian, Amirhossein Bahrami, Mahdi Vakilian, Mehdi Lehtonen, Matti Sharif University of Technology Center of Excellence in Power System Management and Control Electrical Engineering Department Tehran Iran Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350332308

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration;in well developed countries;due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers' perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users' response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account. © 2023 IEEE.

关键词： Crime

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Learning from ChatGPT: A Transformer-Based Model for Wind Power Forecasting

Learning from ChatGPT: A Transformer-Based Model for Wind Po...

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2023 IEEE International Conference on Environment and Electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Dai, Xiaoran Liu, Guo-Ping Hu, Wenshan Lei, Zhongcheng Zhou, Hong School of Electrical Engineering and Automation Wuhan University Wuhan China Center for Control Science and Technology Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798350347432

Wind power forecasting is a crucial aspect of re-newable energy production, as it helps to optimize energy output and ensure grid stability. In recent years, Transformer-based language models such as ChatGPT have been successfully used in natural language processing tasks, but their application in wind power forecasting remains largely unexplored. In this article, we propose using a Transformer model, the core of ChatGPT, to improve the accuracy of wind power forecasting. Using the self-attention mechanism, the developed model can capture the complex temporal relationships in large-scale time series data. Furthermore, the proposed method is evaluated on a test set using various performance metrics. Results show that our model outperforms traditional forecasting models, achieving higher accuracy. Our findings suggest that Transformer-based models have significant potential for improving wind power forecasting accuracy and ultimately contributing to a more sustainable energy future. © 2023 IEEE.

关键词： Time series

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Short-Term Load Forecasting Method Based on Multi-Branch Convolutional Network and Bayesian Optimization 43

Short-Term Load Forecasting Method Based on Multi-Branch Con...

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43rd Chinese control Conference, CCC 2024

作者： Lv, Ji Ding, Min Li, Quanhui Zhou, Sibei Fang, Zhijian China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Accurate short-term power load forecasting is important to ensure the reliability and economy of the power grid. Convolutional neural networks are widely used, but they are difficult to capture the multi-scale temporal characteristics of load data. To address this problem, this study proposes a short-term load forecasting method based on a multi-branch CNN architecture and Bayesian optimisation. By using different sizes of convolutional kernels in different branches, different time-scale features of power load data are captured to improve the identification of rapid changes and potential long-term trends. In addition, the hyperparameters of the forecasting model are automatically adjusted by a Bayesian optimisation algorithm to achieve the best forecasting performance. In forecasting experiments on real load data, the method in this paper significantly improves the forecasting accuracy compared to baseline models. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Convolutional neural networks

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LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission 50

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wu, Yu Yang, Bo Zhu, Dafeng Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665464543

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy. © 2024 IEEE.

关键词： Prediction models

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Simplified control Strategies for Power Converters in a Grid-Connected Hybrid DC-AC Microgrid

Simplified Control Strategies for Power Converters in a Grid...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Busarello, Tiago Davi Curi Simões, Marcelo Godoy Laaksonen, Hannu Kauhaniemi, Kimmo Federal University of Santa Catarina Department of Control Automation and Computing Engineering Blumenau Brazil University of Vaasa School of Technology and Innovations Vaasa Finland

ISBN: (纸本)9798350332308

Many typical converter control strategies used in microgrids are complex or require modifications in the power structure. The complexities are due coupling effects among controllers and time-delay caused in exchanging information in the system. This paper proposes simplified control strategies for power converters in a grid-connected hybrid DC-AC microgrid, aiming to ensure precise voltage and current regulation. The used controllers are proportional-resonant, integral-double-lead, proportional-integral and single integral controller. In this paper, the methodology for tuning each controller is carefully described in detail and recommendation for choosing their bandwidth is given. Then, the designed controllers are discretized through the Backward-Euler approximation. The power converters of the microgrid are an inverter, a Buck, a Boost and a bidirectional DC-DC. A case study is conducted through simulations with Matlab/Simulink. Results show that the controlled variables follow their reference signals with negligible steady-state error, proving the efficacy of the simplified control strategies and allowing an stable operation of the microgrid. © 2023 IEEE.

关键词： controllers

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ADRC-Based controller with Linkage Compensation for Interleaved Parallel Converters 6

ADRC-Based Controller with Linkage Compensation for Interlea...

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6th International Conference on Energy, Electrical and Power engineering, CEEPE 2023

作者： Wang, Bo Zhou, Xuesong Zhao, Chenyang Tao, Long Ma, Youjie Gao, Zhiqiang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Control Science and Engineering Tiangong University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9798350348279

The microgrid is the key development direction of distributed energy. However, the performance indicators of its low-voltage side interface are often affected under complex operating conditions. To address this problem in a targeted manner, a linkage compensatory active disturbance rejection controller (LC-ADRC) that considers both the higher-order perturbation model and object model information is proposed. In this work, the extended state observer (ESO) and the feedback control law of the LC-ADRC are reconstructed according to the interleaved parallel bi-directional converter (IBC) characteristics, and the theoretical analysis shows that the dynamic response and immunity to disturbances of the IBC under typical conditions can be improved simultaneously. Finally, the experimental curves and test indexes obtained from the 300-watt test platform designed for these operating conditions verified the accuracy of the theoretical analysis. © 2023 IEEE.

关键词： controllers

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Optimizing Hydrogen Highway Planning Model Considering Hydrogen Cost and Vehicle Flow Effects

Optimizing Hydrogen Highway Planning Model Considering Hydro...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Zhang, Yan Lv, Yong Wan, Yanming Sun, Yuxin Zhang, Xian Wang, Guibin Liu, Chang China Hydrogen Alliance Research Institute Beijing China Shenzhen University School of Mechanical Electrical and Control Engineering Shenzhen China University of Liverpool Department of Electrical and Electronic Engineering Liverpool United Kingdom School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350352290

In recent years, global governments have been actively advocating for the adoption of hydrogen fuel cell vehicles (HFCVs) as a means to foster a more sustainable future in transportation. However, the widespread integration of these vehicles necessitates robust hydrogenation infrastructure support. This paper presents a novel hydrogen highway optimization planning model tailored for the strategic placement of hydrogen refueling stations (HRSs) along highways. Based on the traditional HRS site selection models, this approach innovatively integrates factors encompassing hydrogen procurement, transportation, and utilization. The model optimizes the location, scale of the HRSs and quantity of hydrogen dispensers within each HRS, utilizing the minimum unit cost of hydrogen as the objective function. To verify the effectiveness, the proposed model is applied in the Beijing-Tianjin-Hebei (BTH) and Chongqing regions, conducting HRS location planning across both demonstration and promotion stages and the specific planning results are obtained. This study offers valuable insights into hydrogen infrastructure planning along highways, serving as a reference for future endeavors in this domain. © 2024 IEEE.

关键词： Hydrogen fuels

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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