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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Liu, Xinyue Ji, Yuehui Shi, Shaobo Liu, Junjie Gao, Qiang Xu, Zhao Tianjin Key Laboratory of Complex System Control Theory and Application TJ 300384 China School of Electrical Engineering and Automation Tianjin University of Technology TJ 300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Due to the dispersed and disorderly feathers of distributed photovoltaic (PV) power generation on the user side, the difficulties of the low-carbon transformation of the power grid and sustainable development of large-scale distributed PV power generation is increased. Based on a master-slave game, this article proposes an optimizing scheme for the operation of microgrids, where the users built PV and shared energy storages. Firstly, this paper introduces the operational framework of the system and the functions of micro-network operator (MNO) and load aggregator (LA). The optimization operation models for MNO and LA are modelled in the framework of Stackelberg master-slave game. The game relationship between MNO and LA is analyzed. Yalmip tool with the CPLEX solver are introduced to solve the differential evolution algorithm. Final, the effectiveness of the proposed method is verified in numerical examples. © 2024 SPIE.

关键词： Energy storage

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Pantograph Structure Anomaly Detection Based on Computer Vision 6

Pantograph Structure Anomaly Detection Based on Computer Vis...

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6th International Conference on Electronics Technology, ICET 2023

作者： Liu, Libo Liu, Quanli Wang, Wei Yu, Zichen Zhao, Xiaoguang Dalian University of Technology School of Control Science and Engineering Dalian China Dalian Seasky Automation Co. Ltd Dalian China

ISBN: (纸本)9798350337693

Pantograph is the only equipment for trains to obtain power. As the core component of the train power supply system, real-time and accurate detection of the pantograph structure state is of great significance to ensure the safety of train operation. Aiming at the shortcomings of current pantograph structure anomaly detection method, such as high cost, narrow application range and strong dependence on abnormal data, this paper designs a pantograph structure anomaly detection method based on computer vision technology, which has a wide range of application and does not rely on abnormal data. Firstly, the improved YOLOv5s object detection model is used to locate the pantograph area in the vehicle pantograph surveillance camera in real time. Then, the semantic segmentation method based on UNet is used to accurately segment the pantograph structure. Finally, the state of the pantograph structure is judged by calculating the similarity with the normal state of the pantograph structure. The experimental results show that the detection method can realize the detection of pantograph structural anomalies with high accuracy and speed. © 2023 IEEE.

关键词： Object detection

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CAPACITY ALLOCATION METHOD OF BATTERY ENERGY STORAGE SYSTEM CONSIDERING MULTI-SERVICE TARGET LEVEL ASSESSMENT

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International Journal of Electrical engineering 2022年第5期29卷 141-153页

作者： Ma, Suliang Wu, Yiwen Jiang, Yuan Li, Yaxin Li, Jianlin School of Electrical and Control Engineering North China University of Technology Shijingshan District Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Haidian District Beijing China

Rational planning of battery energy storage system is the key technology to solve the problem of high proportion of new energy consumption and the requirements of high performance power supply. Starting from the multiple application dimensions of power supply, power grid, load and battery energy storage system, this paper proposes a method of battery energy storage system capacity allocation based on multi-objective comprehensive evaluation. Firstly, the capacity configuration model of battery energy storage system is established from two aspects of service objectives and operation constraints;Then, under the two-level optimization framework, the capacity allocation scheme of the battery energy storage system is formed by using particle swarm optimization and multi- attribute decision-making methods;Finally, the configuration results of IEEE33 nodes with high proportion of distributed new energy access are analyzed, and the influence of model parameters and target orientation on the capacity of battery energy storage system is discussed. The simulation results show that the proposed method can flexibly and effectively configure the battery energy storage system, complete multiple different target service requirements, and promote the scientific application of the battery energy storage system. © 2022, Chinese Institute of Electrical engineering. All rights reserved.

关键词： Battery storage

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Automatic Schizophrenia Detection from Scalp EEG Using CNN with Wide Convolutional Kernel and ELM 5

Automatic Schizophrenia Detection from Scalp EEG Using CNN w...

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5th Asia Symposium on Image Processing, ASIP 2023

作者： Wei, Chun Qin, Yingmei Liu, Jing Jiang, Lijie Che, Yanqiu Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

Detection of schizophrenia is of great significance in clinical practice. In conventional machine learning methods, manual feature extraction is required, which is a hard and time-consuming task. This paper proposes a hybrid model consisting of a convolutional neural network (CNN) with a wide convolution kernel and an extreme learning machine (ELM). By changing the size of the convolution kernel to form a suitable wide convolution kernel, and automatically extracting and optimizing the features of the scalp electroencephalogram (EEG), the performance of the model is effectively improved and accurate detection of schizophrenia is achieved. The input of this model is the original multi-channel EEG picture after short-time Fourier transform, and the output are two types of classification labels for schizophrenia and healthy people. In the end, the detection accuracy of schizophrenia reached 95.59%. This model has a powerful advantage in detecting schizophrenia. © 2023 IEEE.

关键词： Electroencephalography

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Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults 6

Sneak Circuit Analysis Covering Interface Circuits in Spacec...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Liu, Weiming Chen, Cen Xiao, Kaiwen Feng, Mingtao Zhang, Haonan Zhai, Guofu Harbin Institute of Technology Department of Electrical Engineering & Automation Harbin China Beijing Aerospace Automatic Control Institute Beijing China Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350356083

Sneak circuit analysis is a crucial reliability design process. In the design of spacecraft electronic systems, it is challenging to identify sneak circuits related to integrated chip interface circuits, and there are few targeted researches. In extreme working conditions, the coupling sneak circuits caused by the failure of integrated chips are difficult to identify, affecting the design optimization. The paper proposes a typical interface information specification model for integrated chips to simulate the electrical information characteristics of interfaces. And the typical failure models of intrinsic components inside the chip are determined through multi-physics field analysis. Then, based on the digital model, a sneak circuit diagnosis process based on excitation and fault injection are proposed, and a sneak circuit screening method based on Python virtual prototyping is implemented in the case study. Finally, the design constraint clues for the chip's external circuit are obtained, which provides a reference for related fault diagnosis and reliability optimization works. © 2024 IEEE.

关键词： Integrated circuit design

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T-S fuzzy model-based fault tolerant tracking controller design of a quadrotor: A fuzzy lyapunov functional approach

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Journal of Institute of control, Robotics and Systems 2021年第2期27卷 154-160页

作者： An, Ji Ho Han, Tae Joon Kim, Han Sol Department of Control and Automation Engineering Korea Maritime and Ocean University Korea Republic of

This paper proposes a fault tolerant tracking controller design method for a quadrotor based on the T-S (Takagi-Sugeno) fuzzy model. First, the nonlinear dynamics of the quadrotor is represented as a T-S fuzzy model, and its tracking error dynamics is obtained to design the tracking controller. The fault matrix is employed to express the actuator fault, and the upper and lower bounds of each element of the fault matrix are assumed to be known. The conservativeness of a stabilization condition is reduced by using the fuzzy Lyapunov function. Also, the tracking controller design condition is derived in terms of LMIs (Linear Matrix Inequalities). Finally, a simulation example is considered to validate the tracking performance of the proposed method. © ICROS 2021.

关键词： Linear matrix inequalities

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Algorithm for High-Dimensional Data Based on Comprehensible Deep Progressive Fuzzy Network 7

Algorithm for High-Dimensional Data Based on Comprehensible ...

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2023 7th International Symposium on Computer Science and Intelligent control, ISCSIC 2023

作者： Qian, Jinyang Qi, Ruiyun Nanjing University of Aeronautics and Astronautics State Key Lab. of Mechanics and Control for Aerospace Structures College of Automation Engineering Nanjing China

ISBN: (纸本)9798350342987

Aiming at the 'dimension explosion' challenge of fuzzy systems in high-dimensional data processing, this paper proposes an algorithm of DPFS (Deep progressive fuzzy system), whose structure is the connection of many low-dimensional fuzzy systems. Through the comparison of experiments with different data sets, it is found that: *** algorithm retains the advantages of strong interpretability of the fuzzy system. *** DPFS algorithm, characterized by its progressive structure, exhibits a superiority of 7.9% over machine learning algorithms like BP and RBF. Moreover, it outperforms fuzzy system algorithms such as DNFS by more than 31.5% in terms of accuracy on the BF dataset. The research demonstrates that DPFS serves as an effective fuzzy system inference algorithm capable of effectively handling high-dimensional data, showcasing robust comprehensive capabilities. © 2023 IEEE.

关键词： Fuzzy systems

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Impact Time Cooperative Guidance for Multi-Missile System Based on Incremental Learning 42

Impact Time Cooperative Guidance for Multi-Missile System Ba...

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42nd Chinese control Conference, CCC 2023

作者： Li, Xuheng Yu, Jianglong Dong, Xiwang Ren, Zhang Feng, Zhuo Liang, Biao School of Automation Science and Electrical Engineering Beihang University Science and Technology on Aircraft Control Laboratory Beijing100190 China Beijing Institute of Control and Electronic Technology Beijing100038 China

ISBN: (纸本)9789887581543

An impact time cooperative guidance method based on incremental learning is proposed for multi-missile cooperative guidance problem in the attack scenario. Firstly, a guidance model is established to represent the relationship of the missiles and the target. Secondly, an impact time coordinated guidance law based on the consensus method is designed. Then, time-to-go of the missile is predicted using an incremental learning algorithm, so that the accuracy of time-to-go can be better than the results calculated by traditional methods. Finally, the trained neural network is used to predict time-to-go. The results show that the algorithm can be used in the cooperative guidance law to complete the coordination in time, which is superior to the traditional guidance rate in coordination and has advantage in the scene of large maneuvering target. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Learning algorithms

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Tensorized Timeline Alignment for Neural Dynamical Systems in Irregularly Sampled Time Series Prediction 42

Tensorized Timeline Alignment for Neural Dynamical Systems i...

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42nd Chinese control Conference, CCC 2023

作者： Qin, Linxiao Sun, Tao Zhang, Shuo Sun, Xi-Ming School of Control Science and Engineering Dalian University of Technology Dalian116024 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9789887581543

For many real-world scenarios, time series prediction needs to be performed on irregularly sampled data. Neural ordinary differential equations have successfully introduced neural dynamical system (NDS) methods into such tasks. However, these models suffer from a large computational cost, and cannot effectively utilize the temporal information in mini-batches, due to mismatched timestamps of samples. In this work, a unified architecture for NDS is developed, with two novel implementations on discrete input sequences (named TDINDS) and continuous sequences (named TCINDS). The reuse of network modules effectively reduces the scale of parameters and time consumption, while maintaining accuracy. In addition, a tensorized timeline alignment method is proposed, which preserves the temporal information of each training sample via reversible mappings, improving prediction performance and reducing time complexity by at least one order of magnitude. Experiments are conducted on the GoogleStock dataset, where we evaluate model accuracy and computational cost by predicting irregular future sequences. The model robustness is tested using incomplete inputs. The results demonstrate the superior performance of TDINDS and TCINDS, with mean square error reduced by 44% and 50% respectively. Robustness evaluation shows that tensorized timeline alignment is also a key factor to enhance irregular time series prediction ability. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Ordinary differential equations

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