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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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Optimizing PV-battery energy storage for netzero emission traction power system through artificial rabbit optimization method

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Journal of Energy Storage 2025年 126卷

作者： Li, Zhijian Hu, Yihua Ollerenshaw, Richard Li, Xinian He, Zengxiang Esfahani, Mohammad Nasr School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom Department of Engineering King's College London LondonWC2R 2LS United Kingdom Siemens Rail Electrification Siemens Mobility York United Kingdom School of Information and Electronics Engineering Shandong Technology and Business University Yantai264005 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

The transition from traditional fossil fuels to renewable energy is becoming increasingly feasible and is essential for addressing energy and environmental challenges. In transportation electrification, the variability of power demand and the intermittent nature of renewable energy generation present challenges to achieving high renewable energy utilization. Battery energy storage systems (BESS) integrated to renewable resources offer a viable solution to these intermittency issues, though their costs require careful optimization. This paper explores the cost implications of using on-site renewable energy to power a railway traction system. It proposes a photovoltaic (PV) energy storage strategy optimized with the Artificial Rabbit Algorithm (ARO). By applying railway load and regional PV data, we optimize the capacity of BESS and the rated power of the PV site to meet railway electricity demands while minimizing investment and operational costs. The study shows that the capacity of BESS and the size of the PV installation depend on different self-sufficiency ratio (SSR) requirements, leading to an optimized configuration for highly renewable systems. Additionally, the study highlights that considering battery aging is crucial for reducing costs in traction power systems using renewable energy and remains economically viable under time-of-use pricing policies. © 2025 Elsevier Ltd

关键词： Battery storage

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Event-triggered receding horizon control via actor-critic design

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Science China(Information Sciences) 2020年第5期63卷 131-145页

作者： Lu DONG Xin YUAN Changyin SUN School of Automation Southeast University College of Electronics and Information Engineering Tongji University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

In this paper, we propose a novel event-triggered near-optimal control for nonlinear continuoustime systems. The receding horizon principle is utilized to improve the system robustness and obtain better dynamic control performance. In the proposed structure, we first decompose the infinite horizon optimal control into a series of finite horizon optimal problems. Then a learning strategy is adopted, in which an actor network is employed to approximate the cost function and an critic network is used to learn the optimal control law in each finite horizon. Furthermore, in order to reduce the computational cost and transmission cost, an event-triggered strategy is applied. We design an adaptive trigger condition, so that the signal transmissions and controller updates are conducted in an aperiodic way. Detailed stability analysis shows that the nonlinear system with the developed event-triggered optimal control policy is asymptotically *** results on a single-link robot arm with different noise types have demonstrated the effectiveness of the proposed method.

关键词： event-triggered control receding horizon control actor-critic design optimal control nonlinear systems neural networks

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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal control

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1987-1998页

作者： Yisha Li Ya Zhang Xinde Li Changyin Sun School of Automation Southeast UniversityNanjing 210096 Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

关键词： Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control

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An Enhanced Local Ternary Patterns Method for Face Recognition 33

An Enhanced Local Ternary Patterns Method for Face Recogniti...

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第三十三届中国控制会议

作者： WANG Zhenyu HUANG Rong YANG Wankou SUN Changyin School of Automation Southeast University Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781479947249

Feature descriptor based methods(*** Binary Patterns,Local Ternary Patterns) have gained encouraging results in face *** one needs to manually set the threshold in Local Ternary Patterns(LTP).The threshold in LTP is not data adaptive and not robust to *** some cases,we may not give a suitable threshold for *** by Weber's Law,here a data adaptive threshold strategy is prosed for LTP and an enhanced LTP is given for face *** evaluate the enhanced LTP on ORL and FERET face databases and the results demonstrate that the enhanced LTP significantly improves the performances.

关键词： local ternary patterns,Weber's Law,feature descriptor,face recognition

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Distributed MPC with Application to AGC of Power System with Wind-Photovoltaic Hybrid Generation 36

Distributed MPC with Application to AGC of Power System with...

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第36届中国控制会议

作者： Yubin Jia Changyin Sun School of Automation Southeast University Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781538629185

In modern power system,renewable energy（e.g.,wind and solar energy） is an essential part of the smart *** problem of the fluctuate output of renewable energy generation and cooperation with traditional energy generation present significant challenges to the automatic generation control（AGC） in future smart *** paper presents a distributed model of AGC that contains thermal,hydro and wind-solar power *** on this model,we formulate a distributed model predictive control（DMPC） problem and propose a quadratic programming（QP） based approach to solve *** performance of the proposed method is demonstrated through simulations.

关键词： Distributed model predictive control wind and solar generation system automatic generation control

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Emulating manual welding process by two cooperative robots 33

Emulating manual welding process by two cooperative robots

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第三十三届中国控制会议

作者： GAN Yahui DAI Xianzhong DA Qi School of Automation Southeast University Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (纸本)9781479947249

The problem of how to emulate a manual welding process by two cooperative robotic manipulators is investigated in this *** emulating process is intrinsically a trajectory planning process for the two cooperative *** of skilled welders and technical requirements in a welding process have been modeled and integrated into the robot trajectory planning *** model of trajectory planing for two cooperative robots doing a welding task is in a constrained optimization form.A Genetic Algorithm solver has been adopted to solve such an optimization ***-connect-pipe welding experiment has been carried out at the end of the paper and the weld results verified the effectiveness of our method.

关键词： emulate manual welding,cooperative robot,Genetic Algorithm,Pipe-connect-pipe welding

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Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

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Science China(Information Sciences) 2017年第5期60卷 252-254页

作者： Ding WANG Chaoxu MU The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin University

Optimal feedback design of dynamical systems is a significant topic in automatic control community and information *** for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Jacobi-Bellman ***,it is intractable to acquire the analytic solution of the nonlinear Hamilton-JacobiBellman equation for general nonlinear systems.

关键词： Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism

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Semi-Global Stabilization for a Class of Nonlinear Time-Delay systems by Linear Output Feedback

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IFAC-PapersOnLine 2015年第12期48卷 185-190页

作者： Wang, Pan Chai, Lin Fei, Shumin Liu, Guquan Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education School of Automation Nanjing 210096 China

The semi-global stabilization problem for a class of nonlinear systems with state time-delay is addressed in this paper. Sufficient conditions are derived by using Lyapunov-Krasovskii functional method and adjusting the scaling gain to guarantee the systems as well as the resulted closed-loop systems under a linear output feedback controller to be semi-globally stable. Numerical example is presented to illustrate the effectiveness of the obtained results in this paper. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Closed loop systems

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Fault Localization and State Estimation of Power Grid under Parallel Cyber-Physical Attacks

arXiv

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arXiv 2025年

作者： Ren, Junhao Zhao, Kai Zhang, Guangxiao Liu, Xinghua Zhai, Chao Xiao, Gaoxi School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Institute of Catastrophe Risk Management Nanyang Technological University Singapore639798 Singapore Future Resilient Systems Singapore-ETH Centre Singapore138602 Singapore School of Electrical Engineering Xi’an University of Technology Xi’an710048 China Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Parallel cyber-physical attacks (PCPA) refer to those attacks on power grids by disturbing/cutting off physical transmission lines and meanwhile blocking transmission of measurement data to dwarf or delay the system protection and recovery actions. Such fierce hostile attacks impose critical threats to the modern power grids when there is a fusion of power grids and telecommunication technologies. In this paper, we investigate the fault diagnosis problem of faulty transmission lines under a broader spectrum of PCPA for a linearized (or DC) power flow model. The physical attack mechanism of PCPA includes not only disconnection but also admittance value modification on transmission lines, for example, by invading distributed flexible AC transmission system (D-FACTS). To tackle the problem, we first recover the information of voltage phase angles within the attacked area. Using the information of voltage phase angle and power injection of buses, a graph attention network-based fault localization (GAT-FL) algorithm is proposed to find the locations of the physical attacks. By capitalizing on the feature extraction capability of the GAT on graph data, the fault localization algorithm outperforms the existing results when under cyber attacks, e.g., denial of service (DoS) attacks. A line state identification algorithm is then developed to identify the states of the transmission lines within the attacked area. Specifically, the algorithm restores the power injection of buses within the attacked area and then identities the state of all the transmission lines within the attacked area by solving a linear programming (LP) problem. Experimental simulations are conducted on IEEE 30/118 bus standard test cases to demonstrate the effectiveness of the proposed fault diagnosis algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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