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Bidding Strategy for Coordinated Operation of Wind Power Plants and NGG-P2G Units in Electricity Market

CSEE Journal of Power and Energy systems 2022年第1期8卷 212-224页

作者： Xiang Gao Ka Wing Chan Shiwei Xia Xian Zhang Guibin Wang Shenzhen Polytechnic ShenzhenChina and also with the Department of Electrical Engineering The Hong Kong Polytechnic UniversityHung HomHong KongChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHung HomHong KongChina School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China

The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity *** this context,this paper proposes a bi-level stochastic optimization model for the joint operation of a coordinated wind power plant(WPP)and natural gas generating(NGG)-power to gas(P2G)suppliers participating in the day-ahead(DA)market and real-time(RT)market as well as providing real-time auxiliary *** coordinated supplier’s payoff is maximized in the upper level with consideration of the uncertainties of WPP output capacity and RT electricity price,while the social welfare of the grid is maximized in the lower *** results demonstrate the effectiveness of the proposed bidding model of the coordinated WPPs and NGG-P2G suppliers by examining its bidding behaviors and benefits with comparisons of four other bidding models.

关键词： Bi-level model bidding strategy WPP NGGP2G uncertainties

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Deciphering decadal urban ozone trends from historical records since 1980

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National Science Review 2024年第11期11卷 117-129页

作者： Haolin Wang Xiao Lu Paul I.Palmer Lin Zhang Keding Lu Ke Li Tatsuya Nagashima Ja-Ho Koo Hiroshi Tanimoto Haichao Wang Meng Gao Cheng He Kai Wu Shaojia Fan Yuanhang Zhang School of Atmospheric Sciences Sun Yat-sen University and Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Education Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) School of Geo Sciences University of Edinburgh National Centre for Earth Observation University of Edinburgh Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University State Key Joint Laboratory of Environmental Simulation and Pollution Control The State Environmental Protection Key Laboratory of Atmospheric Ozone Pollution ControlCollege of Environmental Sciences and Engineering Peking University Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science &Technology National Institute for Environmental Studies Department of Atmospheric Sciences Yonsei University Department of Geography Hong Kong Baptist University Department of Civil and Environmental Engineering University of California

Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade（2013–2022） in the northern hemisphere, warm-season（April–September） mean daily 8-h average maximum ozone increases much faster in urban regions with top ozone levels(mainly in the North China Plain, 1.2 ± 1.3 ppbv year-1) than in other, low-ozone regions(0.2 ± 0.9 ppbv year-1). These trends widen the ozone differences across urban regions, and increase extreme pollution levels and health threats from a global perspective. Comparison of historical trends in different urban regions reveals that ozone increases in China during 2013–2022 differ in magnitude and mechanisms to historical periods in other regions since *** reflects a unique chemical environment characterized by exceptionally high nitrogen oxides and aerosol concentrations, where reducing ozone precursor emissions leads to substantial ozone *** increase in China has slowed down in 2018–2022 compared to 2013–2017, driven by ongoing emission reductions, but with ozone-favorable weather conditions. Historical ozone evolution in Japan and South Korea indicates that ozone increases should be suppressed with continuous emission *** temperature and associated wildfires have also reversed ozone decreases in the USA and Europe,with anthropogenic ozone control slowing down in recent decades.

关键词： air quality surface ozone trends

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Rail Fastener Defect Detection of Heavy Haul Railway Based on Improved YOLOv8 27

Rail Fastener Defect Detection of Heavy Haul Railway Based o...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Li, Xinman Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun School of Automation and Intelligence Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation Control System Beijing100044 China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

Rail fasteners play a crucial role in the heavy haul railway lines, serving as vital components of railway infrastructure. However, due to the intricate nature of the heavy haul railway environment, fasteners can often be obscured, leading to misjudgments and inaccuracies for the existing defect detection algorithms. To address this challenge, a novel method to detect rail fastener defects based on an improved YOLOv8 model is proposed. Initially, a new dataset specifically tailored for fastener defect detection in heavy haul railway lines is constructed utilizing onsite data. To enrich feature information and enhance detection accuracy, multiple improvements are introduced, including the addition of a P6 feature layer, the design of a cross-layer connection neck (CCNeck) feature fusion network, and the introduction of the DySample up-sampling operator. Furthermore, the Quality Focal Loss (QFL) function is introduced to address class imbalance problem within the datasets. Finally, experiments are conducted and the results demonstrate significant enhancements in detection mean average precision (mAP) compared to existing state-of-the-art networks. © 2024 IEEE.

关键词： Rails

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Global Update Guided Federated Learning 41

Global Update Guided Federated Learning

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第41届中国控制会议

作者： Qilong Wu Lin Liu Shibei Xue Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Electronic Information Engineering Shanghai Dianji University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Federated learning protects data privacy and security by exchanging models instead of ***,unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning *** alleviate this problem,unlike previous studies that limit the distance of updates for local models,we propose global-updateguided federated learning(FedGG),which introduces a model-cosine loss into local objective functions,so that local models can fit local data distributions under the guidance of update directions of global ***,considering that the update direction of a global model is informative in the early stage of training,we propose adaptive loss weights based on the update distances of local *** simulations show that,compared with other advanced algorithms,FedGG has a significant improvement on model convergence accuracies and ***,compared with traditional fixed loss weights,adaptive loss weights enable our algorithm to be more stable and easier to implement in practice.

关键词： Federated Learning Cosine Similarity Adaptive Loss Weights

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 379-391页

作者： Goudarzi, Hamed Hesamzadeh, Mohammad Reza Bunn, Derek Fotuhi-Firuzabad, Mahmud Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Management and Control Tehran11365-11155 Iran Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden London Business School Department of Management Science and Operations LondonNW1 4SA United Kingdom Illinois Institute of Technology Electrical and Computer Engineering Department ChicagoIL60616 United States

This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm. © 2023 IEEE.

关键词： Power markets

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A Data-Driven Passive Islanding Detection Scheme

A Data-Driven Passive Islanding Detection Scheme

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作者： De, Sourav Reddy, Motakatla Venkateswara Sodhi, Ranjana Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar140001 India Grid Automation and Control National Renewable Energy Laboratory GoldenCO80401 United States

This paper proposes a simple yet effective statistical parameters based passive islanding scheme. The proposed scheme relies only the voltage data, measured at the point of common coupling (PCC), and is comprised of three stages. The Stage-1 quickly computes what is termed as mean based coarse islanding detection (MID), Stage-2 computes the decaying DC detector (DDCD), and 3) finally, MID is used in conjunction with DDCD as per the statistical digital relay logic (SRDL) is devised in Stage-3 to distinguish between islanding events from non islanding events. Various tests are investigated on the Banshee’s industrial Microgrid (MG), modelled in the Real Time Digital Simulator (RTDS), on the basis of the IEEE-1547, UL 1741 standards. The validation of the proposed islanding detection methodology (PIDM) in RTDS platform ensures the viability of the proposed scheme in practical systems. The test results reveal that the PIDM can detect islanding within 2 cycles, and results in a very small non detection zone (NDZ). Moreover, the scheme can very well be used for single-phase MG too as it relies only on the single phase voltage information. © 2023 IEEE.

关键词： Electric circuit breakers

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control 5

Adversarially Robust Reinforcement Learning for Urban Traffi...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Hamzah, Muhammad Shafly Joelianto, Endra Sutarto, Herman Yoseph Putri, Adiyana Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia PT. Pusat Riset Energi Department of Intelligent System Bandung Indonesia Institut Teknologi Bandung Doctoral Program of Engineering Physics Bandung Indonesia

ISBN: (纸本)9798350364743

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems. © 2024 IEEE.

关键词： Traffic signals

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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A Differential Game Approach to Collision Avoidance in Multi-Agent systems 41

A Differential Game Approach to Collision Avoidance in Multi...

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第41届中国控制会议

作者： Wenyan Xue Zhihong Wu Siyuan Zhan Yutao Chen Jie Huang College of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Department of Electronic Engineering Maynooth University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper gives a novel differential game scheme to solve the collision avoidance problem for multi-agent *** on the concept of artificial potential field(APF),we combine obstacle avoidance objectives with trajectory optimization targets as the performance *** feedback strategies are based on the solutions of coupled Riccati ***,it is proved that the feedback strategies will converge to a Nash equilibrium(NE).Finally,simulation results are provided to show the advantages,which make agents arrive at the targeted position collision-free with a reduced time.

关键词： Collision avoidance differential game trajectory optimization Nash equilibrium

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