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arXiv 2022年

作者： Zengin, Rahman Salim Sezer, Volkan Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Autonomous Mobility Group Electrical and Electronics Engineering Faculty Istanbul Technical University Istanbul Turkey

Training of a Machine Learning model requires sufficient data. The sufficiency of the data is not always about the quantity, but about the relevancy and reduced redundancy. Data-generating processes create massive amounts of data. When used raw, such big data is causing much computational resource utilization. Instead of using the raw data, a proper Condensed Representation can be used instead. Combining K-means, a well-known clustering method, with some correction and refinement facilities a novel Condensed Representation method for Machine Learning applications is introduced. To present the novel method meaningfully and visually, synthetically generated data is employed. It has been shown that by using the condensed representation, instead of the raw data, acceptably accurate model training is possible. Copyright © 2022, The Authors. All rights reserved.

关键词： K-means clustering

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Explainable Fault Diagnosis Using Invertible Neural Networks-Part I: A Left Manifold-based Solution

TechRxiv

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TechRxiv 2023年

作者： Chen, Hongtian Huang, Biao Sun, Wenxin Zhang, Weidong Jiang, Bin Ding, Steven X. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The School of the Internet of Things Engineering Jiangnan University Wuxi214122 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Jiangsu Key Laboratory of Internet of Things and Control Technologies China University of Duisburg-Essen Duisburg47057 Germany The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada

The series includes two parts, articulating the two novel avenues of research on intelligent fault diagnosis (FD) for nonlinear feedback control systems. In Part I of the series, we design a novel FD paradigm by elaborating an invertible neural network (INN) for feedback control systems. With the aid of a left manifold, the core idea behind the INN-based FD scheme is (i) formulation of residual generator used for FD as a projection of system data onto the null space that has the same dimension as system outputs, (ii) in a topological space, elaboration of a homeomorphism that delivers an invertible relationship between system outputs and residual signals when the system input is given, (iii) skillful introduction of both the master and slave objective functions to achieve system/parameter identification with information loseless property. Comparing with the existing FD approaches, the three superior strengths of the proposed FD scheme deserving mentation are: 1) it specializes in nonlinear feedback control systems, 2) it can effectively avoid the overfitting problem when approximating or learning nonlinear system dynamics, and 3) control theory guides the whole design, ensuring the interpretability of the learning process. Finally, two studies on nonlinear systems demonstrate the feasibility of the invertible left manifold (ILM)-based FD strategy. Part I would contribute to the future development of machine learning (ML)based system identification and explainable FD approaches, and also benefits the right manifold-based FD designs in the Part II. © 2023, CC BY.

关键词： Failure analysis

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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On the room-temperature tensile deformation behavior of a cast dual-phase high-entropy alloy CrFeCoNiAl_(0.7)

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Journal of Materials Science & Technology 2021年第28期87卷 29-38页

作者： Qiang Wang Liangcai Zeng Tengfei Gao Hui Du Xinwang Liu Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering School of Machinery and AutomationWuhan University of Science and TechnologyWuhan 430081China State Key Laboratory of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhan 430074China

The microstructure and room-temperature tensile deformation behavior of the cast CrFeCoNiAl0.7 high-entropy alloy(HEA)were studied in *** cast HEA consisted of a dual-phase structure of 77.3 vol.%face-centered-cubic(FCC)phase plus 22.7 vol.%B2 phase,and exhibited excellent room-temperature tensile properties with a high yield strength of 876 MPa,ultimate tensile strength of 1198 MPa and a relatively large elongation to fracture of~9%.Dislocations gliding in the FCC phase governed the plastic deformation at the early stage of room-temperature tensile,and disordered dislocations were to form dislocation walls as the deformation *** further increase in strain to a high level,the stacking faults were generated through the dissociation of the geometrically necessary dislocations,serving as the potential heterogeneous nucleation sites for the deformation twins.

关键词： High-entropy alloy Microstructure Deformation mechanism Dislocation Stacking fault

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Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 668-673页

作者： Zhaorong Zhang Minyue Fu School of Electrical Engineering and Computer Science The University of Newcastle.University DriveNSW 2308Australia IEEE School of Automation Guangdong University of Technologyand Guangdong Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhou 510006China

Gaussian belief propagation algorithm(GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability(or generalised diagonal dominance). This paper extends this convergence result further by showing that the convergence is exponential under the generalised diagonal dominance condition,and provides a simple bound for the convergence rate. Our results are derived by combining the known walk summability approach for asymptotic convergence analysis with the control systems approach for stability analysis.

关键词： Belief propagation distributed algorithm distributed estimation Gaussian belief propagation Markov networks

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Regional Frequency Dynamics Modeling of Power Systems Incorporating Frequency Support from HVDC Links and Renewable Energy Sources

Regional Frequency Dynamics Modeling of Power Systems Incorp...

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IEEE General Meeting Power& Energy Society

作者： Jianguo Zhou Ye Guo Zichao Meng Lun Yang Hongbin Sun Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China State Key Laboratory of Power Systems Operation and Control (Tsinghua University) Tsinghua University Beijing China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The increasing penetration of Renewable Energy Sources (RESs) and High Voltage Direct Current (HVDC) challenges the system frequency stability and the frequency shows distinct spatial-temporal characteristics. A Physical-Informed Neural Network (PINN)-based Regional Frequency Security Metrics (RFSM) is proposed for power systems with frequency support from HVDC and RESs. First, the RFSM models are modeled based on the physical principle. Then, neural networks are used to determine the parameters informed with the developed physical models. The proposed model can be utilized to establish frequency constraints implemented in the scheduling model. This type of constraints can be easily integrated into the optimization model due to its linearity. Moreover, the proposed model enables the system operators to find the optimal location and frequency response to be procured to guarantee the frequency stability in every region of the power system, and make pricing schemes for the corresponding ancillary services. The case study shows that the model has better physical interpretation and higher accuracy.

关键词： Measurement Renewable energy sources HVAC HVDC transmission Optimization models Power system dynamics Pricing Power system stability Stability analysis Frequency control

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A Guideline for Humanoid Leg Design with Oblique Axes for Bipedal Locomotion

A Guideline for Humanoid Leg Design with Oblique Axes for Bi...

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IEEE-RAS International Conference on Humanoid Robots

作者： Konrad Fründ Anton Leonhard Shu Florian Christoph Loeffl Christian Ott German Aerospace Center (DLR) Institute of Robotics and Mechatronics Wessling Germany Faculty of Electrical Engineering and Information Technology TU Wien Automation and Control Institute Vienna Austria

ISBN: (纸本)9798350309805

The kinematics of humanoid robots are strongly inspired by the human archetype. A close analysis of the kinematics of the human musculoskeletal system reveals that the human joint axes are oriented within certain inclinations. This is in contrast to the most popular humanoid design with a configuration based on perpendicular joint axes. This paper reviews the oblique joint axes of the mainly involved joints for locomotion of the human musculoskeletal system. We elaborate on how the oblique axes affect the performance of walking and running. The mechanisms are put into perspective for the locomotion types of walking and running. In particular, walking robots can highly benefit from using oblique joint axes. For running, the primary goal is to align the axis of motion to the mainly active sagittal plane. The results of this analysis can serve as a guideline for the kinematic design of a humanoid robot and a prior for optimization-based approaches.

关键词： Legged locomotion Musculoskeletal system Actuators Torque Robot kinematics Humanoid robots Kinematics

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Insulators Defect Detection for Power Grid Based on Improved Yolov7 Algorithm

Insulators Defect Detection for Power Grid Based on Improved...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Zhiwei Zhang Yan Zhuang Zhida Liang Shuai Kong Shengyi Zhu School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province Zhejiang Academy of Special Equipment Science Hangzhou China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

High-current out-of-phase closed bus is an essential link in the transmission of electric energy and is widely used in thermal power stations, hydropower stations and nuclear power plants. Although these busbars have the advantages of excellent dustproof effect and strong magnetic shielding, they are susceptible to insulator aging and damage in long-term operation, posing a potential safety risk to the power grid. Due to the enclosed space inside the bus and the complexity of external maintenance tasks, manual inspection has problems such as inefficiency and low safety. In order to solve these problems, this paper proposes an improved detection algorithm based on Yolov7 for insulator defect detection in hydropower station scenarios. Because of the particularity of Yolov7 network structure and application scenarios, this paper proposes a Channel Feature Enhancement module to enhance the channel representation ability of the network. In addition, an Edge Feature Enhancement module is introduced to improve the boundary detection of insulators by enhancing the contrast between the target and the background. Considering the limitations of existing studies in specific scenarios and the scarcity of dataset, this paper constructs and accurately annotates an insulator defect detection dataset applied to hydropower station scenarios. Experimental results show that the improved-Yolov7-tiny algorithm in this paper achieves a balance between accuracy and inference speed.

关键词： Accuracy Image edge detection Space power stations Hydroelectric power generation Inspection Insulators Feature extraction Power grids Inference algorithms Defect detection

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Reset-control-based current tracking for a solenoid with unknown parameters

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IFAC-PapersOnLine 2021年第5期54卷 199-204页

作者： R. Bertollo M. Schwegel A. Kugi L. Zaccarian Dept. of Industrial Engineering University of Trento Italy Automation and Control Institute TU Wien Austria AIT Austrian Institute of Technology GmbH Center for Vision Automation & Control Vienna Austria LAAS-CNRS Université de Toulouse CNRS Toulouse France

We propose a hybrid controller for reference tracking for the output current of a solenoid. The solenoid can be modeled as a first-order linear plant with unknown parameters. The proposed hybrid controller comprises 1) a feed forward action, based on an estimate of the unknown plant parameters exploiting a hybrid formulation of the recursive least-squares method, and 2) a feedback action, exploiting a reset control scheme based on a first-order reset element. We prove stability properties for the closed-loop system and we show through simulations that the current output converges to the reference for sufficiently exciting reference signals.

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Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Generation based on Variable Speed Pumped Storage

Optimal Portfolio of Wind-solar-hydro Hybrid Renewable Gener...

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China International Electrical and Energy Conference (CIEEC)

作者： Shizhe Xiang Da Xu Menglu Gao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China DC Company of State Grid Hubei Electric Power Co. Ltd. Yichang China

Compared with separate solar or wind generation, the hybrid wind-solar-hydro renewable portfolio can achieve better generation characteristics due to their temporal-spatial complementarities. This paper proposes optimal portfolio of wind-solar-hydro hybrid renewable generation based on variable speed pumped storage (VSPS). In this portfolio, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via various conversion and storage devices for zero carbon 100% renewable supplies. Compared to traditional fixed speed pumped storage (FSPS), the VSPS has obvious advantages in power regulation and renewable accommodation, which is comprehensively modeled to capture its dynamic operational dispatchability and flexibility. The portfolio production, conversion, storage, and consumption are formulated via a two-stage framework, which takes the minimum system investment cost as the objective function. Case studies on southwest China are performed to show the effectiveness and superiority of the proposed methodology on cost-effective accommodation.

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