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Steady-state Voltage Security-constrained Optimal Frequency Control for Weak HVDC Sending-end AC Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 658-669页

作者： Qiangqiang Wang Liangzhong Yao Jian Xu Yuping Zheng Wei Li Wei Wang School of Electrical Engineering and automation Wuhan UniversityWuhan 430072China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network(Wuhan University) Wuhan 430072China state key laboratory of Smart Grid Protection and Control NARI Group Corporation(State Grid Electric Power Research Institute)Nanjing 211106China

Due to the fact that a high share of renewable energy sources(RESs)are connected to high-voltage direct current(HVDC)sending-end AC power systems,the voltage and frequency regulation capabilities of HVDC sending-end AC power systems have diminished.This has resulted in potential system operating problems such as overvoltage and overfrequency,which occur simultaneously when block faults exist in the HVDC link.In this study,a steady-state voltage security-constrained optimal frequency control method for weak HVDC sending-end AC power systems is proposed.The integrated virtual inertia control of RESs is employed for system frequency regulation.Additional dynamic reactive power compensation devices are utilized to control the voltage of all nodes meet voltage security constraints.Then,an optimization model that simultaneously considers the frequency and steady-state voltage security constraints for weak HVDC sending-end AC power systems is established.The optimal control scheme with the minimum total cost of generation tripping and additional dynamic reactive power compensation required is obtained through the optimization solution.Simulations are conducted on a modified IEEE 9-bus test system and practical Qing-Yu line commutated converter based HVDC(LCC-HVDC)sending-end AC power system to verify the effectiveness of the proposed method.

关键词： Renewable energy source(RES) high-voltage direct current(HVDC) AC power system optimal frequency control steady-state voltage security dynamic reactive power compensation

A comparative study of data-driven battery capacity estimation based on partial charging curves

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Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li Xuesong Mei state key laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi key laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

关键词： Lithium-ion battery Partial charging curves Capacity estimation Data-driven Sampling frequency

Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

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Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu state key laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are neglected.It is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains unsolved.Meanwhile,the channel information in high-level feature is also not well mined.Inevitably,the performance of grasp detection is degraded.To solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual.Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion.Such a hierarchical fusion guarantees the quality of grasp prediction.Furthermore,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches.By such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the network.Besides,an information enhancement module is added before the encoder to reinforce input information.The proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

Adaptive PI Control Based Stability Margin Configuration of Aircraft Control Systems with Unknown System Parameters and Time Delay

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Journal of Systems Science & Complexity 2024年第4期37卷 1507-1525页

作者： ZHANG Zhibing ZHOU Dapeng WANG Yeguang GAO Wanxin ZHANG Yanjun AVIC Shenyang Aircraft Design and Research Institute School of automation Beijing Institute of Technology state key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology

The stability margin is a vital indicator for assessing the safety level of aircraft control systems. It should maintain sufficient stability margin to ensure safety during flight, especially in the process of large maneuver operations. The stability margin is generally quantified by the Bode diagram,which strictly depends on the system parameters and the open-loop transfer function. However, due to the uncertain flight environments, transmission delays of sensors and mode switchings, etc., there exist large parameter and structure uncertainties in the aircraft control systems, which make it difficult to precisely configure the stability margin to the desired value by the usual control methods. To address this problem, an indirect adaptive control strategy is proposed in this paper, where an adaptive PI control law with the capability of self-configuration of stability margin is developed. The developed control law not only achieves stable time-varying command tracking in the time domain, but also is able to automatically configure the phase margin and gain margin in the frequency domain. Finally,the simulation of the one-degree-of-freedom roll rate control model of the air vehicle verifies the validity of the proposed control method.

关键词： Adaptive control aircraft control parameter uncertainty stability margin configuration

Parallel Vision ■ Image Synthesis/Augmentation

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 782-784页

作者： Wenwen Zhang Wenbo Zheng Qiang Li Fei-Yue Wang the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the School of Computer Science and Technology Wuhan 430070China the state key laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,Scene understanding is an essential task in computer vision.The ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans do.Parallel vision is ... 详细信息

关键词： instru explanation computer

Semantic segmentation via pixel-to-center similarity calculation

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 87-100页

作者： Dongyue Wu Zilin Guo Aoyan Li Changqian Yu Nong Sang Changxin Gao National key laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Meituan Inc. BeijingChina

Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.

关键词： computer vision deep neural networks image segmentation scene understanding

Convolution Neural Network-based Load Model Parameter Selection Considering Short-term Voltage Stability

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1064-1074页

作者： Ying Wang Chao Lu Xinran Zhang School of Technology Beijing Forestry UniversityBeijing 100083China state key laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of automation Science and Electrical Engineering Beihang UniversityBeijing 100083China IEEE

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads.Meanwhile,it also brings new problems.Since the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application difficult.In order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this paper.Meanwhile,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage stability.To begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of LMPS.Meanwhile,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.

关键词： Ambient signal CNN field PMU data load model parameter selection short-term voltage stability

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong state key laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina key laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

On extended state Kalman filter-based identification algorithm for aerodynamic parameters

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Control Theory and Technology 2024年第2期22卷 235-243页

作者： Wenyan Bai Ruizhe Jia Peng Yu Wenchao Xue Beijing Aerospace Automatic Control Institute Beijing100854China School of automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China key laboratory of Knowledge automation for Industrial Processes Ministry of EducationBeijing100083China key laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately identify aerodynamic parameters, and has desired convergence speed and convergence precision.

关键词： Aerodynamic parameters Parameter identification Extended state Kalman filter