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China International Electrical and Energy Conference (CIEEC)

作者： Cen Chen He Du Xi Cai Haoran Wang Guofu Zhai Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China China Three Gorges Construction Engineering Corporation Chengdu China Three Gorges Intelligent Industrial Control Technology Company Wuhan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

With the widespread use of power electronic equipment, accurately identifying soft faults in power electronic converters is crucial for the stable and reliable operation of power electronic equipment. This paper proposed a soft fault diagnosis method for three-phase DC/AC converter using a data-based approach. Firstly, simulation analysis is conducted on the soft faults of three-phase DCI AC converter, and appropriate test signals are selected as data sources. Then, the LSTM neural network and its variants are used to obtain fault diagnosis models. Finally, the method is validated through experiments, and the results show that it has good performance in soft fault diagnosis of the three-phase DCI AC converter.

关键词： Fault diagnosis Training Accuracy Soft sensors Neural networks White noise Power electronics

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Generalized Predictive Analysis of Reactions in Paper Devices Via Graph Neural Networks

SSRN

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SSRN 2024年

作者： Sun, Hao Pan, Yihan Dong, Hui Liu, Canfeng Yang, Jintian Tao, Yihui Jia, Yuan School of Mechanical Engineering and Automation Fuzhou University Fuzhou China School of Mechatronics Engineering Harbin Institute of Technology Harbin China Fuzhou University China Department of Automation Control and System Engineering University of Sheffield Sheffield United Kingdom Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5%, 94.3% and 99.0% by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Optimization of Stacking Location for Mixed Loading of Steel Products

Optimization of Stacking Location for Mixed Loading of Steel...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Rong Zheng, Song Wang, Jinlong Xu, Zhezhuang Wen, Weixiang Chen, Boyu Zhang, Qingdong Fuzhou University College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming Fujian353000 China

ISBN: (纸本)9781728119489

In the steel logistics park, the operational efficiency is repeatedly compromised due to excessive truck turnaround. For solving this problem fundamentally, the mixed loading relationship of multi-type steel products is excavated and the optimum solution for storage yard is proposed in the paper. Due to the limited stacking space, some products with small outbound demand have to be stacked together. A two-stage model is established to meet the stacking requirements for different outbound quantity of steel products. In the first stage, products with a small amount of stock will be mixed and stacked together according to certain rules. Then all products are allocated to specific yards in the second stage with the goal of maximizing the mixed correlation of steel products in the same yard to reduce the number of transitions for pick-up trucks. At the end, products in large quantities are placed in the stacking position close to the pick-up position. Experiments demonstrate that the proposed scheme is of great benefit to improve the operation efficiency of the logistics park. © 2022 IEEE.

关键词： Trucks

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On Uniform Observability of Linear Time-varying Systems with respect to Subspaces and Its Applications

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IEEE Transactions on Automatic control 2025年

作者： Liu, Tao Huang, Jie Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong

Recently, the notion of uniform observability with respect to a subspace was proposed in [21] and was utilized to solve the synchronization problem for single-integrator multi-agent systems with time-varying matrix-weighted couplings. This paper further explores the concept of uniform observability of a linear time-varying system with respect to a subspace. We will first establish the invariance property of uniform observability with respect to some invariant subspace under time-varying output feedback control. Then, based on this invariance property, we will establish an exponential convergence result with respect to an invariant subspace for a class of linear switched systems. Leveraging this exponential convergence result, we will solve the exponential synchronization problem for general linear multi-agent systems over jointly connected switching networks. It is noted that the exponential synchronization problem was also studied recently by some more sophisticated methods. Compared with these methods, our method is much simpler. Moreover, our method leads to an explicit characterization of the exponential synchronization rate. © 1963-2012 IEEE.

关键词： Linear time-invariant system

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Direction-Constrained control for Efficient Physical Human-Robot Interaction under Hierarchical Tasks

arXiv

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arXiv 2024年

作者： Xu, Mengxin Wan, Weiwei Wang, Hesheng Harada, Kensuke Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of System Innovation Graduate School of Engineering Science Osaka University Toyonaka Osaka560-0043 Japan Department of Automation the Key Laboratory of System Control and Information Processing of Ministry of Education the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustments. To resolve this limitation, we introduce the concept of directional constraints and develop a direction-constrained optimization algorithm to handle the nonlinearities induced by these constraints. The algorithm solves two sub-problems, minimizing the error and minimizing the deviation angle, in parallel, and combines the results of the two sub-problems to produce a final optimal outcome. The mutual influence between these two sub-problems is analyzed to determine the best parameter for combination. Additionally, the velocity objective in our control framework is computed using a variable admittance controller. Traditional admittance control does not account for constraints. To address this issue, we propose a variable admittance control method to adjust control objectives dynamically. The method helps reduce the deviation between robot velocity and human intention at the constraint boundaries, thereby enhancing interaction efficiency. We evaluate the proposed method in scenarios where a human operator physically interacts with a 7-degree-of-freedom robotic arm. The results highlight the importance of incorporating directional constraints in pHRI for hierarchical tasks. Compared to existing methods, our approach generates smoother robotic trajectories during interaction while avoiding interaction delays at the constraint boundaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Demand-Side Joint Electricity and Carbon Trading Mechanism

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 14-25页

作者： Hua, Haochen Chen, Xingying Gan, Lei Sun, Jiaxiang Dong, Nanqing Liu, Di Qin, Zhaoming Li, Kang Hu, Shiyan College of Energy and Electrical Engineering Hohai University Nanjing211100 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Automation Tsinghua University Beijing100084 China Automatic Control Laboratory at Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users' optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method. © 2023 IEEE.

关键词： Real time systems

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Adaptive Fault-Tolerant control for Stochastic High-Order Fully Actuated Systems with Local Faults

Adaptive Fault-Tolerant Control for Stochastic High-Order Fu...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xueqing Liu Maoyin Chen Li Sheng Donghua Zhou Department of Automation Tsinghua University Beijing China College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

This paper portrays a class of local faults, whose basic characteristic is that the fault occurs only when the system is in a certain state domain. In engineering practice, such faults are prevalent and could affect efficiency greatly. To accurately tolerate these local faults, a novel control strategy is proposed. The globally asymptotical stability in probability is guaranteed by using time-varying gains on pre-divided fields. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed framework.

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Virtual Synchronous Generator Droop control for Renewable Energy Sources

Virtual Synchronous Generator Droop Control for Renewable En...

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Global Energy Conference (GEC)

作者： Fevzi Cakmak Sukru Aykat Mehmet Cemil Kazanbaş Sabahattin Akgul Department of Electricity and Energy Mardin Atuklu University Mardin Turkey Department of Computer Engineering Mardin Atuklu University Mardin Turkey Department of Control and Automation Engineering Yildiz Technical University Istanbul Turkey Department of Handicrafts Mardin Atuklu University Mardin Turkey

ISBN: (数字)9798331532611

ISBN: (纸本)9798331532628

The connection of large-scale RES (renewable energy source) to the grid and the increase in the efficiency of energy production also causes different power quality problems. It has become mandatory for these inverter-based systems to participate in load frequency control. For this purpose, the design and simulation of a virtual synchronous generator (VSG), which enables RES to operate like synchronous generators, has been carried out in Matlab/Simulink environment. It is observed that the VSG keeps the load frequency stable according to different load conditions.

关键词： Renewable energy sources Power quality Production Synchronous generators MATLAB Frequency synchronization Frequency control

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Time-Attention Graph Convolutional Network Soft Sensor in Biochemical Processes 4

Time-Attention Graph Convolutional Network Soft Sensor in Bi...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

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Day-ahead Unit Commitment Incorperating Deep Peak Regulation 3

Day-ahead Unit Commitment Incorperating Deep Peak Regulation

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3rd International Conference on Energy engineering and Power Systems, EEPS 2023

作者： Xiao, Yang Ding, Tao Liu, Yuzheng Zhang, Biyuan Su, Bo Guo, Xiaorui Xi'an Jiaotong University School of Electrical Engineering Xi'an China State Grid Ningxia Electric Power Co. Ltd Center of Electric Power Control Yinchuan China China Electric Power Research Institute Electric Power Automation Department Beijing China

ISBN: (纸本)9798350313857

To give the potential regulation capacity of the thermal power generator for renewable energy integration by peak regulation, this paper has investigated in developing a comprehensive model for day-ahead unit commitment, taking into account deep peak regulation. Specifically, the investigation entails dividing the peak regulation into distinct modes and establishing corresponding operational cost functions. Subsequently, a mixed-integer linear programming model is presented for power scheduling, leveraging the defined cost function. To validate the effectiveness of the proposed model and highlight the superiority of deep peak regulation in accommodating renewable energy sources, a modified IEEE-118 case has been conducted. © 2023 IEEE.

关键词： Cost functions

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