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High Voltage 2024年第2期9卷 275-286页

作者： Yi Luo Shiyi Mao Ju Tang Zijun Pan Cheng Pan State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and AutomationWuhan UniversityWuhanChina

The surface charge accumulation on the basin insulator is a particular problem in high-voltage direct current(HVDC)gas insulated switchgears(GIS)and gas insulated trans-mission lines(GIL),which restricts the development and application of extra/ultra high-voltage(EHV/UHV)technology.The surface charge distribution and anti-flashover performance are closely related to the insulator shape.To obtain the optimal basin insulator,a shape optimisation model based on artificial bee colony algorithm was established,and the minimisation objective functions based on capacitive(initial)electric field distribution and resistive(steady-state)electric field distribution were respectively constructed.The optimisation results of the two objective functions were calculated,and the electric field and charge distribution on the surface of the insulator before and after optimisation were evaluated and compared using the simulation model.The results indicated that the optimisation based on the capacitive electric field can significantly inhibit the surface charge accumulation,and the optimised insulator can significantly reduce the surface normal electric field and make the distribution of the tangential electric field more uniform,and the maximum tangential electric field can be reduced by 10%;the optimisation based on resistive electric field can achieve similar results,but requires more computational resources.Based on the results,the‘anti-charge’basin insulator structure suitable for the DC field was given.

关键词： insulator artificial charge

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 418-429页

作者： Yanzheng Zhu Nuo Xu Fen Wu Xinkai Chen Donghua Zhou IEEE College of electrical engineering and automation Shandong University of Science and TechnologyQingdao 266590China College of mechanical engineering and automation Huaqiao UniversityXiamen 361021China Department of mechanical and Aerospace engineering North Carolina State UniversityRaleigh NC 27695 USA Department of Electronic and Information Systems Shibaura Institute of TechnologySaitama 337-8570Japan

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

关键词： Current feedback fault estimation iterative learning observer Markov jump piecewise-affine system

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

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Science China Technological Sciences 2024年第7期67卷 2074-2086页

作者： WANG Rui LI Ming-Jia WANG YiBo SUN QiuYe ZHANG PinJia College of Information Science and engineering Northeastern University school of mechanical engineering Beijing Institute of Technology Department of electrical engineering Tsinghua University

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

关键词： regional integrated electric-thermal energy systems real-time power balance virtual asynchronous machine adaptive inertia small-signal stability

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

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Journal of Energy Chemistry 2024年第5期92卷 605-618页

作者： Qingguang Qi Wenxue Liu Zhongwei Deng Jinwen Li Ziyou Song Xiaosong Hu College of mechanical and Vehicle engineering Chongqing UniversityChongqing 400044China school of mechanical and electrical engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of mechanical engineering National University of SingaporeSingapore 117575Singapore

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

关键词： Electricvehicle Lithium-ion battery pack Capacity estimation Machine learning Field data

A novel adaptive coordinated tracking control scheme for a morphing aircraft with telescopic wings

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Chinese Journal of Aeronautics 2024年第2期37卷 148-162页

作者： Enmei WANG Hao LU Jianchun ZHANG Chenliang WANG Jianzhong QIAO Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China school of automation Science and electrical engineering Beihang UniversityBeijing 100191China

Inspired by flight biology,morphing flight technology has great potential to improve the adaptability and maneuverability of aircraft.This paper is devoted to the flight control problem of morphing aircraft,and aimed at safe and fuel-saving flight through morphing actively.Specifically,the longitudinal dynamics of a morphing aircraft with telescopic wings is modelled as a strict-feedback nonlinear system.Through fitting the expression of aerodynamic parameters by the mor-phing ratio,the model uncertainties induced by morphing errors are embedded in the dynamics.To meet the safety and fuel-saving requirements,an Adaptive Coordinated Tracking Control Scheme(ACTCS)is then proposed,which consists of a morphing control module and a tracking control module.For the morphing control module,an on-line morphing decision model is given in an optimization process with respect to the morphing ratio,and a second-order tracking filter is introduced to smooth the decision output and ensure the physical realizability.For the tracking control module,the novel adaptive controllers for the velocity and altitude subsystems are proposed based on the dynamic surface control method,in which adaptive mechanisms are designed to com-pensate for the model uncertainties.Finally,the proposed ACTCS is simulated in nine different cases of the test flight mission,to verify its effectiveness,robustness and fuel-saving effect.

关键词： Morphing aircraft Adaptive tracking control Morphing decision Dynamic surface control Morphing error

Review of Key Technologies for Developing Personalized Lower Limb Rehabilitative Exoskeleton Robots

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 16-28页

作者：陶璟周振欢 school of Mechatronic engineering and automation Shanghai UniversityShanghai 200444China school of mechanical engineering Shanghai Jiao Tong UniversityShanghai 200240China

Rehabilitative training and assistance to daily living activities play critical roles in improving the life quality of lower limb dyskinesia patients and older people with motor function degeneration.Lower limb reha-bilitative exoskeleton has a promising application prospect in support of the above population.In this paper,critical technologies for developing lower limb rehabilitative exoskeleton for individualized user needs are identi-fied and reviewed,including exoskeleton hardware modularization,bionic compliant driving,individualized gait planning and individual-oriented motion intention recognition.Inspired by the idea of servitization,potentials in exoskeleton product-service system design and its enabling technologies are then discussed.It is suggested that future research will focus on exoskeleton technology and exoskeleton-based service development oriented to an individual's physical features and personalized requirements to realize better human-exoskeleton coordination in terms of technology,as well as accessible and high-quality rehabilitation and living assistance in terms of utility.

关键词： powered exoskeleton lower limb dyskinesia rehabilitation personalization product-service system

Modular Soft Robotic Crawlers Based on Fluidic Prestressed Composite Actuators

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Journal of Bionic engineering 2024年第2期21卷 694-706页

作者： Zefeng Xu Linkai Hu Longya Xiao Hongjie Jiang Yitong Zhou Shien-Ming Wu school of Intelligent engineering South China University of TechnologyGuangzhou511442China school of mechanical and electrical engineering Guangzhou UniversityGuangzhou510006China

Soft robotic crawlers have limited payload capacity and crawling speed.This study proposes a high-performance inchworm-like modular robotic crawler based on fluidic prestressed composite(FPC)actuators.The FPC actuator is precurved and a pneumatic source is used to flatten it,requiring no energy cost to maintain the equilibrium curved shape.Pressurizing and depressurizing the actuators generate alternating stretching and bending motions of the actuators,achieving the crawling motion of the robotic crawler.Multi-modal locomotion(crawling,turning,and pipe climbing)is achieved by modular reconfiguration and gait design.An analytical kinematic model is proposed to characterize the quasi-static curvature and step size of a single-module crawler.Multiple configurations of robotic crawlers are fabricated to demonstrate the crawling ability of the proposed design.A set of systematic experiments are set up and conducted to understand how crawler responses vary as a function of FPC prestrains,input pressures,and actuation frequencies.As per the experiments,the maximum carrying load ratio(carrying load divided by robot weight)is found to be 22.32,and the highest crawling velocity is 3.02 body length(BL)per second(392 mm/s).Multi-modal capabilities are demonstrated by reconfiguring three soft crawlers,including a matrix crawler robot crawling in amphibious environments,and an inching crawler turning at an angular velocity of 2/s,as well as earthworm-like crawling robots climbing a 20 inclination slope and pipe.

关键词： Soft robot Soft crawler Fluidic prestressed composite Kinematic model Enhanced loading Multi-modal capability

Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si school of electrical engineering and automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution.However,there is,currently,nearly no standard technical framework for objective and quantitative intelligence evaluation.In this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation theories.On this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results.Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

Global output feedback regulation of time-varying nonlinear systems via the dual-gain method

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Science China(Information Sciences) 2024年第5期67卷 333-334页

作者： Xian-Long YIN Zong-Yao SUN Changyun WEN Institute of automation Qufu Normal University school of electrical and Electronic engineering Nanyang Technological University

Notably, owing to the limited information available, the problem of output feedback stabilization/regulation for nonlinear systems has been explored based on state observers.In addition, the sensors accounting for state measurement could fail to detect the system states accurately due to the limitations of manufacturing techniques and instruments.

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Dynamic modeling and RBF neural network compensation control for space flexible manipulator with an underactuated hand

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Chinese Journal of Aeronautics 2024年第3期37卷 417-439页

作者： Dongyang SHANG Xiaopeng LI Meng YIN Fanjie LI school of mechanical engineering and automation Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China

In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.

关键词： Space flexible manipulator RBF neural network Underactuated hand Dynamic models Model simplification