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Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

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arXiv 2023年

作者： Qu, Jiaqi Wei, Lu Sun, Qiang Zareipour, Hamidreza Qian, Zheng School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability. Copyright © 2023, The Authors. All rights reserved.

关键词： Failure analysis

Multi-hop Multi-RIS Wireless Communication Systems: Multi-reflection Path Scheduling and Beamforming

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arXiv 2024年

作者： Ma, Xiaoyan Zhang, Haixia Chen, Xianhao Fang, Yuguang Yuan, Dongfeng Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China School of Information Science and Engineering Shandong University Qingdao266237 China The School of Control Science and Engineering Shandong University Jinan250061 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Computer Science City University of Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on

关键词： Beamforming

Investigating the Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration

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SSRN

SSRN 2024年

作者： Guo, Hao Wu, Bangan Li, Qi Ding, Zhen Jiang, Feng Yi, Chunzhi School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Management Harbin Institute of Technology Harbin China School of Mechatronics Engineering Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing China School of Medicine and Health Harbin Institute of Technology Harbin China

Human trust plays a crucial role in the effectiveness of human-robot collaboration. Despite its significance, the development and maintenance of an optimal trust level are obstructed by the complex nature of influencing factors and their mechanisms. This study investigates the effects of cognitive load on human trust within the context of a hybrid human-robot collaboration task. An experiment is conducted where the humans and the robot, acting as team members, collaboratively construct pyramids with differentiated levels of task complexity. Our findings reveal that cognitive load exerts diverse impacts on human trust in the robot. Notably, there is an increase in human trust under conditions of high cognitive load. Furthermore, the rewards for performance are substantially higher in tasks with high cognitive load compared to those with low cognitive load, and a significant correlation exists between human trust and the failure risk of performance in tasks with low and medium cognitive load. By integrating interdependent task steps, this research emphasizes the unique dynamics of hybrid human-robot collaboration scenarios. The insights gained not only contribute to understanding how cognitive load influences trust but also assist developers in optimizing collaborative target selection and designing more effective human-robot interfaces in such environments. © 2024, The Authors. All rights reserved.

关键词： Behavioral research

Dynamic Strongly Convex Sparse Operator with Learning Mechanism for Sparse Large-Scale Multi-Objective Optimization

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Dynamic Strongly Convex Sparse Operator with Learning Mechan...

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Xiaodong Huang Hanyu Hu Jian Wang Bin Yuan Caili Dai Sergey V. Ablameyk College of Control Science and Engineering China University of Petroleum (East China) Qingdao China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of science China University of Petroleum (East China) Qingdao China School of Petroleum Engineering China University of Petroleum (East China) Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Sparse large-scale multi-objective optimization problems (SLSMOPs) hold significant practical relevance across various domains. However, the efficacy of existing evolutionary algorithms (EAs) in tackling these optimization challenges is limited due to the high-dimensional search space and the sparsity inherent in Pareto optimal solutions. To overcome these difficulties, a dynamic strongly convex sparse operator with learning mechanism (DSCSOLM) is proposed. We design a novel strongly convex function that can effectively generate sparse solutions and enable the newly generated sparse solutions to learn knowledge from the Pareto optimal solutions, making the obtained sparse solutions more in line with the sparse distribution of the Pareto optimal solutions. Moreover, dynamic parameter is used within the proposed strongly convex function during the execution of the algorithm. Experimental results of benchmark and neural network training problems validate that DSCSOLM outperforms state-of-the-art (SOTA) comparative algorithms.

关键词： Learning systems Training Heuristic algorithms Neural networks Evolutionary computation Pareto optimization Benchmark testing Convex functions Complex systems Optimization

Long-fiber Sagnac interferometers for twin-field quantum key distribution networks

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Physical Review Applied 2025年第3期23卷 034040-034040页

作者： Reem Mandil Li Qian Hoi-Kwong Lo Centre for Quantum Information and Quantum Control Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Centre for Quantum Information and Quantum Control Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada

A Sagnac-loop structure can help overcome the major difficulty in the practical implementation of a twin-field quantum key distribution (TFQKD) network, namely, the need to stabilize the phase of a quantum state over many kilometers of fiber. Unfortunately, Rayleigh backscattering noise limits the signal-to-noise ratio for Sagnac systems containing long fibers and lossy photonic devices. Here, we solve this problem by sending optical pulses in long on-off bursts and using time postselection on measurements taken with free-run single-photon avalanche detectors. We also investigate the impact of the residual phase noise uncompensated by the Sagnac structure and find that the variance of the phase noise scales as loop length to the third power, verifying an existing calculation in the literature. We measure the interference visibility in Sagnac loops of varying length without active phase stabilization and achieve >97% visibility in 200-km ultralow-loss fiber, which is, to our knowledge, the longest fiber Sagnac interferometer demonstrated. Our results suggest that a Sagnac system is feasible for long-distance TFQKD networks, a key step towards the practical implementation of metropolitan quantum networks.

关键词： Interferometers

Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration

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arXiv 2024年

关键词： Man machine systems

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

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arXiv 2024年

作者： Tian, Jiwei Shen, Chao Wang, Buhong Xia, Xiaofang Zhang, Meng Lin, Chenhao Li, Qian Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Air Traffic Control and Navigation College Air Force Engineering University Xi’an China Information and Navigation College Air Force Engineering University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

—Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids. © 2024, CC BY.

关键词： Location

A Model-Based Approach for Voltage and State-of-Charge Estimation of Lithium-ion Batteries

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A Model-Based Approach for Voltage and State-of-Charge Estim...

Sustainable Power and Energy Conference (iSPEC)

作者： Milad Andalibi Seyed Saeed Madani Carlos Ziebert Farshid Naseri Mojtaba Hajihosseini Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany Electrical and Computer Engineering Department Electrical Energy Technology Aarhus University Aarhus Denmark

ISBN: (纸本)9781665485234

Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

关键词： Lithium-ion batteries Machine learning algorithms Computational modeling Estimation Voltage Safety State of charge

Leakage Localization in Water Distribution Networks: A Model-Based Approach

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arXiv 2022年

作者： Lindström, Ludvig Gracy, Sebin Magnússon, Sindri Sandberg, Henrik The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden The Department of Computer and Systems Sciences Stockholm University Sweden

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak constant. For the aforementioned setting, but with noisy pressure and flow measurements, an expression for estimating the location of the leak is provided. Finally, assuming the existence of a single leak, for a network comprising of more than one pipe and assuming that the network has a tree structure, we provide a systematic procedure for determining the leak location, the leak exponent, and the leak constant. © 2022, CC BY.

关键词： Water distribution systems