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Chaos synchronization of semiconductor lasers over 1040-km fiber relay transmission with hybrid amplification

Photonics Research 2023年第6期11卷 953-960页

作者： LONGSHENG WANG JUNLI WANG YUSHAN WU YUEHUI SUN SONGSUI LI LIANSHAN YAN YUNCAI WANG ANBANG WANG Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan 030024China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Photonics Information Technology Guangzhou 510006China Center for Information Photonics&Communications School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China

Optical chaos communication and key distribution have been extensively demonstrated with high-speed advantage but only within the metropolitan-area network range of which the transmission distance is restricted to around 300 *** secure-transmission requirement of the backbone fiber link,the critical threshold is to realize long-reach chaos ***,we propose and demonstrate a scheme of long-reach chaos synchronization using fiber relay transmission with hybrid amplification of an erbium-doped fiber amplifier(EDFA)and a distributed fiber Raman amplifier(DFRA).Experiments and simulations show that the hybrid amplification extends the chaos-fidelity transmission distance thanks to that the low-noise DFRA suppresses the amplified spontaneous emission noise and self-phase *** of the hybrid-relay conditions are studied,including launching power,gain ratio of DFRA to EDFA,single-span fiber length,and number of fiber span.A 1040-km chaos synchronization with a synchronization coefficient beyond 0.90 is experimentally achieved,which underlies the backbone network-oriented optical chaos communication and key distribution.

关键词： synchronization fiber amplifier

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Identification of Transient Voltage Stability Weakness in Power systems Based on Grad-CAM 7

Identification of Transient Voltage Stability Weakness in Po...

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7th IEEE International Conference on Power and Renewable Energy, ICPRE 2022

作者： Qin, Changlong Jia, Yujian Li, Zhendong Li, Guang Li, Li Zhu, Yuanzhen State Grid Jinan Power Supply Company Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch Control of Ministry of Education Jinan China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

With the changes in generation and load characteristics, transient voltage problems have a serious impact on the safe and stable operation of the power system. In this paper, on the basis of constructing a fast evaluation model of transient voltage stability based on convolutional neural network, the key electrical quantities that the model focuses on in the evaluation process are pointed out by Grad-CAM algorithm, and the weakness of system voltage stability are identified. The simulation results show that the proposed method can explore the links that are neglected by traditional methods, and the identified weakness consider both topological and state information, which verifies the superiority of the method in this paper. © 2022 IEEE.

关键词： Convolution

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Online Cascading Failure Searching Based on Gradient Boosting Decision Tree

Online Cascading Failure Searching Based on Gradient Boostin...

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Power Tech Conference

作者： Tianhao Liu Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With the penetration of renewable energy sources (RES) increasing rapidly, cascading failures become more complex due to the uncertainty and vulnerability of RES. This paper presents an online cascading failure searching method based on gradient boosting decision tree (GBDT) for the hybrid AC/DC system with high penetration of wind power. First, the cascading failure risk index is established, which considers the impact, probability, and loss of failures. Then, a cascading failure searching based on Monte Carlo tree search and dynamic simulation is performed to acquire the high-risk cascading failures, which are utilized as the training samples. Finally, GBDT is deployed to construct the relationship between operating conditions and failure propagation directions. Online cascading failure searching is realized by combining GBDT failure estimation and dynamic failure simulation. Simulation results of the modified New England 39-bus test system demonstrate that the proposed method can fast and accurately online screen the high-risk cascading failures considering the uncertainty of wind power.

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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EV Charging Load Forecasting Method Considering Time-of-Use Price and Meteorological Factors 7

EV Charging Load Forecasting Method Considering Time-of-Use ...

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7th International Conference on Power and Energy Applications, ICPEA 2024

作者： Li, Guangxiao Yang, Yilu Wang, Chunyi Xu, Ziyu Wang, Yue Liu, Yutian State Grid Jining Power Supply Company Development Planning Department Jining China State Grid Shandong Electric Power Company Development Planning Department Jinan China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350356113

The randomness of load fluctuations at electric vehicle (EV) charging stations presents a significant challenge for the accurate load forecasting. Considering time-of-use (TOU) price and meteorological factors, EV charging load forecasting method for EV charging station is proposed. First, the factors affecting charging load are analyzed using the Pearson correlation coefficient and the maximum information coefficient (MIC). key factors such as TOU price, temperature, weekday type, and recent historical load data are determined. Next, convolutional neural network (CNN) is used to extract important features and capture complex nonlinear relationships from key factors. Finally, the extracted features are processed by gated recurrent unit (GRU) network to forecast charging load at different time scales. Case study is conducted using load data from EV charging station with a capacity of 1800 kW. The results demonstrate that the CNN-GRU model significantly improves forecasting accuracy at 4h, 24h, 72h, and 168h forecasting lengths compared to other models. © 2024 IEEE.

关键词： Charging stations

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New Adaptive ESO Based Data-Driven Anti-Disturbance control for Nonlinear systems with Convergence Guarantee ⁎

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IFAC-PapersOnLine 2024年第14期58卷 397-402页

作者： Shoulin Hao Yihui Gong Naseem Ahmad Shuhao Yue Tao Liu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian 116024 China Institute for Advanced Control Technology Dalian University of Technology Dalian 116024 China

In this paper, a new adaptive extended state observer based data-driven anti-disturbance control (AESO-DDADC) design is proposed for industrial nonlinear systems with unknown dynamics subject to external disturbances. By reformulating such system description into a compact-form dynamic linearization model with a residual term, a new AESO is firstly constructed to estimate the residual term using the partial derivative (PD) estimation from the previous time step, such that the residual term could be proactively counteracted by the feedback control law, in contrast to the existing data-driven ESO where the residual term in the PD estimation is absolutely neglected to facilitate the convergence analysis. Then, the bounded convergence of PD estimation and AESO is jointly analyzed by the Gerschgorin disk theorem, followed by robust convergence analysis of the established closed-loop system. Moreover, another AESO-DDADC scheme is developed using a partial-form dynamic linearization model of the system, along with rigorous robust convergence analysis. Finally, an illustrative example is shown to confirm the efficacy and advantages of the proposed designs.

关键词： Nonlinear systems data driven adaptive extended state observer anti-disturbance control convergence analysis

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Model Predictive control Based on Amplitude Decaying Aggregation Strategy for Hydrogen Gas Turbine

Model Predictive Control Based on Amplitude Decaying Aggrega...

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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on intelligent Autonomous systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 IEEE.

关键词： Semantics

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Optimal Reactive Power Adjustment Based on Jacobi Matrix Decomposition 6

Optimal Reactive Power Adjustment Based on Jacobi Matrix Dec...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zou, Shihao Yu, Lei Cao, Yongji Zhang, Hengxu Zhou, Tao Ma, Ruicong Wang, Jifei Zhang, Xinyu Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Jinan China State Grid Zhangjiakou Electric Power Company State Grid Jibei Electric Power Company Zhangjiakou China Shandong University Academy of Intelligent Innovation Jinan China

ISBN: (纸本)9798350346671

To solve the problem of excessive reactive power and high voltage caused by renewable energy connection and the increase of the proportion of cables, a reactive power optimization approach based on the Jacobi matrix decomposition is proposed. The voltage sensitivity is obtained based on the Jacobi matrix decomposition. The reactive power resources sensitivity and reactive power network loss sensitivity are combined to determine the buses where the reactive power adjustment is carried out. The scheme of reactive power optimization that meets the voltage adjustment requirements is obtained through an iterative convergence calculation method. An urban power system is taken as the case study, which shows that the proposed method can optimize the reactive power voltage optimization effectively. © 2023 IEEE.

关键词： Reactive power

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