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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yingxiu Xu, Qimin Zhang, Jinglong Xu, Lei Li, Lingzhi Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments. © 2022 IEEE.

关键词： Ethernet

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Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第4期1卷 322-335页

作者： Wu, Sirui Lin, Jin Li, Jiarong Liu, Feng Song, Yonghua Xu, Yanhui Cheng, Xiang Yu, Zhipeng Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

Renewablepower to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility. © 2023 IEEE.

关键词： Ammonia

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Consensus-based Distributed Augmented Lagrangian Method for Nonconvex Optimization with Coupled Constraints

Consensus-based Distributed Augmented Lagrangian Method for ...

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2022 Chinese Automation Congress, CAC 2022

作者： Su, Yanxu Anhui University School of Artificial Intelligence Hefei230601 China Ministry of Education Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Nanjing210096 China

ISBN: (纸本)9781665465335

In this paper, we study the nonconvex optimization problems with consideration of globally coupled constraints. The constrained optimization problem is converted into an augmented Lagrangian function, and a distributed primal-dual algorithm is exploited in the context of finite-time consensus for solving such a problem. Particularly, we do not require the globally coupled constraints to be separable. Instead, we introduce some local variables for estimating the constraint violations. Under some mild assumptions in the research of nonconvex optimization, we prove the convergence of the presented algorithm to a KKT point of the problem. The numerical experiment verifies the correctness and the effectiveness of the theoretical results. © 2022 IEEE.

关键词： Constrained optimization

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A Comparing Study on Assessment Mechanism for Renewable Energy Bidding Deviation in Spot Trading 5

A Comparing Study on Assessment Mechanism for Renewable Ener...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Wang, Hao Zhang, Li Guo, Yixin Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China North China Electric Power University School of Electrical and Electronic Engineering Baoding China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Participating in market trading is an essential step to promote the sustainable development of renewable energy. To properly deal with the uncertainty of renewable energy, it is important to establish a reasonable bidding deviation assessment mechanism in the electricity market. Based on the analysis of existing research and practice, the bidding deviation assessment mechanism are summarized into two categories, that is, the outcome assessment mechanism (OAM) and the performance assessment mechanism (PAM). Models of the mechanism are built to analyze the principles of the two types of mechanism, and the impact of aggregation effect of renewable energy on the assessment is discussed. Simulation results show that the outcome assessment mechanism will lead to over-assessment, but is conducive to motivating renewable energy stations (RSs) to actively reduce bidding deviation;while the performance assessment mechanism can ensure the cost-recovering of balancing dispatch, but make RSs unable to improve market behavior except to passively accept penalties at settlement. To promote the development of renewable energy, both assessment mechanisms have room for further improvement. © 2022 IEEE.

关键词： Power markets

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Operation Plan Optimization of High-Speed Railway Based on Flexible Grouping 7

Operation Plan Optimization of High-Speed Railway Based on F...

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7th International Conference on Electromechanical control Technology and Transportation, ICECTT 2022

作者： Li, Cheng Li, Feng Wang, Hui State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9781510656659

Whether the high-speed rail train operation plan is reasonable has an important impact on the transportation efficiency and benefits. This paper focuses on the optimization of high-speed train operation plan under the condition of unbalanced passenger flow. We formulate an integrated mixed integer linear programming model by adopting the form of flexible grouping. The model comprehensively considers constraints such as train unit allocation and passenger flow loading. It aims to minimize the number of empty seats in all trains. The inputs to the model are train units and passenger demand. The allocation of train units is completely driven by passenger demand. Taking the Beijing-Shanghai high-speed railway as an example, the results show that the load rate of train services is 96.42%, which is much higher than the fixed grouping. Therefore, a flexible grouping operation plan proposed in this paper can effectively improve train units' utilization and reduce enterprises' operating costs. © 2022 SPIE

关键词： Integer programming

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Batch-subsample Prototype Network Classification Model for Imbalanced and Overlapping Data

Batch-subsample Prototype Network Classification Model for I...

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Chinese Automation Congress (CAC)

作者： Wang Shizhao Shu Shaoguang Liu Xueshi System Software Engineering Laboratory Beijing Institute of Control Engineering Beijing China

The classification of imbalanced and overlapping data is more difficult. When the number of categories is uneven and category areas are overlap, general neural network classifiers can only extract limited features from the data, which makes the model relies too much on existing samples, resulting in a low classification accuracy. Aiming at the above classification problem, this paper proposes a batch-subsample prototype network model. Based on the prototype network, the model achieves the separation of different categories in classification. In the training process, selecting each batch of training data by subsample method, so that the data for training is balanced each time, which effectively reduce the impact of category imbalance and overlapping on model training. This model is tested in the dataset NSL-KDD with the characteristics of imbalance and overlapping. The experimental results show that the classification accuracy of the batch-subsample prototype network (BSPN) model has been effectively improved, and the classification ability of minority categories also be better. To a certain extent, the model solves the problem that the neural network model has poor classification effect on imbalanced and overlapping data. Above all, the batch-subsample prototype network model can improve the classification accuracy in imbalanced and overlapping data, optimize the recognition rate of minority categories, and increase the classification robustness of the model, which is of great significance for imbalanced and overlapping data classification.

关键词： Training Automation Neural networks Prototypes Training data Feature extraction Data models

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Short-Term Photovoltaic Prediction Based on Transfer Learning and LSTM

Short-Term Photovoltaic Prediction Based on Transfer Learnin...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Tianyu Zhang Kedi Zheng Qixin Chen Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

With the proposal of carbon peaking and carbon neutrality goals, the proportion of renewable energy generation continues to increase and the number of new photovoltaic power stations is growing rapidly. Accurate photovoltaic power prediction is of great significance for the safe, stable and operation of power system. In order to solve the problem of insufficient historical data of newly constructed PV power plants, this paper proposes a short-term PV power prediction model based on Long Short Term Memory and transfer learning. The model uses meteorological data, numerical weather forecast data and PV power as features. According to the transfer learning theory, the Long Short Term Memory model is pre-trained using the historical data of other previously-built PV power plants, and then a small amount of local operation data is used to fine-adjust the model. Case study based on Hebei open source photovoltaic and meteorological data set proves that the proposed transfer learning model can effectively improve the short-term PV power prediction accuracy, and the MAPE can be improved by up to 15%.

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Applying system Thinking to Learn from Accident of Modern Automatic control systems

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Chinese Journal of Electronics 2025年第2期23卷 409-414页

作者： Ru Niu Yuan Cao Xiaocheng Ge Tao Tang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Department of Computer Science University of York York UK

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events, rather than a simple cause-effect chain. We take railway Train control systems (TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP - one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Sociotechnical systems Analytical models systematics Railway accidents Transportation Medical services Rail transportation systems thinking Research and development Accidents

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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