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Security Constrained Unit Commitment Problem with Energy Storage system and Demand Flexibility Constraints

Security Constrained Unit Commitment Problem with Energy Sto...

引用

IEEE India conference (INDICON)

作者： Sugata Bhattacharya Attoti Bharath Krishna Abhijit R. Abhyankar Alok Kumar Somara Lakra R K Porwal System Operation Grid India India Electrical Engineering Department IIT Delhi India

Unit commitment is a challenging optimization problem that is critical in power system operational planning. Considering N − 1 security criteria, along with transmission lines and operational limitations, leads to a generic security constrained unit commitment (SCUC) problem. Traditional SCUC formulations for large power systems using injection sensitivity factors are computationally intensive and thus are restrictive. this work proposes an efficient framework for SCUC with energy storage systems (ESSs) and flexible loads, using the line outage distribution factors. Case studies analyze the impact of ESSs and flexible loads on the SCUC results and form a useful guideline for research community. Presented case studies accentuate the individual and combined impact of ESS and flexible demands on the total operation cost, unit commitment status, and power schedules of thermal units of a modified 118-bus system at two different loading levels.

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An Implementation and optimization Method of RTLS Based on UWB for Underground Mine 20

An Implementation and Optimization Method of RTLS Based on U...

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20th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Fang, Zerui Cui, Shiyao Su, Qing Wang, Suzhen Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China Univ Jinan Sch Informat Sci & Engn Dezhou Peoples R China Univ Jinan Sch Informat Sci & Engn Suzhou Peoples R China

ISBN: (纸本)9781665416580

To obtain the specific location information of personnel in the underground mine, this paper describes a real-time locating system (RTLS) based on ultra-wideband (UWB) technology. the system consists of wearable active tags, main and sub anchors, servers, and clients. Firstly, alternative double-sided two-way ranging (ADS-TWR) combined with digital filter and linear fitting is used to pre-process the noise and bias of the ranging results, obtaining the anchor-tag distance with an error less than 10cm. In order to resolve the problem of position outliers caused by random factors in the mine, the position of the moving target is estimated by the extended Kalman filter (EKF) initially. then the mean filter (MF) will perform a secondary optimization of the results. Experimental results show that the positioning accuracy of the system reaches 0.06m in the line-of-sight environment. the positioning accuracy is improved effectively and the system shows a strong anti-noise performance.

关键词： UWB underground-mine RTLS DS-TWR EKF Mean Filtering

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20th International conference on Informatics in Economy, IE 2021

20th International Conference on Informatics in Economy, IE ...

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20th International conference on Informatics in Economy, IE 2021

ISBN: (纸本)9789811688652

the proceedings contain 35 papers. the special focus in this conference is on Informatics in Economy. the topics include: Research on Data Analysis (Environmental, Social and Economic) in the Context of Implementing the Circular Economy;applying a Sustainable Vector Model to Generate Innovation;optimal Employment Contracts with Several Types of Agents;Labor Market Trends During the COVID-19 Pandemic;the Labor Market in Relation to Digitalization—Perspectives on the European Union;the Impact of Bitcoin in the Financial Market. A Cybernetics Approach;privacy-Preserving Framework for Deep Learning Cybersecurity Solutions;cyber Security Maturity Model for Critical Infrastructures;the Effectiveness of a Multimedia Mobile Application;A GIS-Based Approach in Support of Monitoring Sustainable Urban Consumption Variables;data Mining in Smart Agriculture;machine Learning and Data Mining Techniques for Human Resource optimization Process—Employee Attrition;machine Learning Techniques for Network Intrusion Detection—A systematic Analysis;web Scraping and Ethics in Automated Data Collection;classical Machine-Learning Classifiers to Predict Employee Turnover;assessing the Share of the Artificial Ad-Related Traffic: Some General Observations;experimental Results Regarding the Efficiency of Business Activities through the Use of Chatbots;agile Perspectives in Higher Education;Digitalization of Business and Public Organizations—Communication Problems with IT Companies and Possible Solutions;an Analysis of Different Browser Attacks and Exploitation Techniques;an Assisted Instruction system Designed for Online Teaching and Learning;building Resilience through Digital Transformation;visual Tool for Stimulating Employee Intelligent Attitude;management Information systems in Knowledge Society;analyzing Business Performances with a Multicriteria Decision Method;a General Cost Model in a Cloud Data Center;preface.

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Fast Sparse Magnetic Resonance Image Reconstruction via a Dual Formulation

Fast Sparse Magnetic Resonance Image Reconstruction via a Du...

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International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Maimoona Siddiq Syed Ahmed Pasha Department of Electrical and Computer Engineering Air University Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

In recent years, there has been a growing trend in sparse modeling to reduce the scan time in magnetic resonance imaging while maintaining the diagnostic quality of the images. In this paper, we reformulate the $\ell_optimization$ -penalized regression problem with a total variation constraint for magnetic resonance image reconstruction via the Lagrange dual function and develop an efficient algorithm for the dual problem. Furthermore, we discuss model selection via an information criterion to find the optimal regularization parameters in the optimization problem. Empirical studies using spatial brain magnetic resonance images demonstrate that the algorithm exhibits superior convergence properties and achieves higher quality reconstruction in comparison to existing methods.

关键词： Magnetic resonance imaging Magnetic resonance Brain modeling Market research Image reconstruction optimization Convergence Magnetic properties

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Dynamic user optimal model for shared autonomous vehicles system: Development and systematic comparison with social optimal model 25

Dynamic user optimal model for shared autonomous vehicles sy...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Maruyama, Ryota Seo, Toru Tokyo Inst Technol Dept Civil & Environm Engn Tokyo 1528552 Japan

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Shared autonomous vehicle (SAV) system is a new type of public transportation in which autonomous vehicles shared by the society transport travelers using optimal routes and ridesharing matching. In the literature, the dynamic system optimum (DSO), in which travelers' behavior is completely controlled by some transportation authority to maximize the entire society's benefit, is mainly considered for SAV system analysis. However, it is known that DSO could cause unfairness among travelers and may not be acceptable to some travelers due to the lack of freedom. On the other hand, the dynamic user optimal (DUO) assignment computes transportation system's state based on travelers' free and selfish behavior, and is useful to simulate traffic congestion with a realistic traveler behavior. this study develops a novel DUO assignment model for SAV systems. It is formulated as multi-step linear optimization problems to describe the multi-layer structure of SAV systems (i.e., travelers and SAVs follow different behavioral principles) while keeping the computational efficiency. the model is evaluated using actual network and demand data in Japanese urban area, and systematic comparison with a DSO model for SAV systems is conducted. As results, we confirm that overall performance of the SAV system was degraded in the DUO model, because the congestion level of some popular links were significantly increased due to the selfish behavior of travelers. Qualitatively, this is an expected result;the contribution of this study is to develop a method for quantitative analysis of this phenomenon. Such quantitative results would be useful to develop appropriate management schemes for SAV systems such as dynamic pricing, which is the most important future work.

关键词： systematics Statistical analysis Computational modeling Urban areas Pricing Data models Behavioral sciences

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the Optimal Design and Control of a Low-emission Methanol-Fueled Hybrid Electric Boat

The Optimal Design and Control of a Low-emission Methanol-Fu...

引用

IEEE/ASME International conference on Mechtronic and Embedded systems and Applications, MESA

作者： Siyang Liu Zuomin Dong Department of Mechanical Engineering Institute for Integrated Energy System University of Victoria Victoria Canada

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Combining clean alternative fuel, hybrid electric propulsion, and advanced real-time optimal control provides a very promising approach to clean energy transition and decarbonization. However, a hybrid electric ship's flexible electrified propulsion system is a complex mechatronic system that demands system optimization and real-time optimal control to reach its full potential. Accurate prediction of needed propulsion power is required to develop the integrated propulsion system design and control solutions. this study introduces an advanced propulsion power prediction method and an integrated model-based design and optimization approach for the design and control optimizations of the vessel propulsion system. After introducing the engine performance models of a methanol-fueled engine based on literature data, a case study of a representative tour boat is conducted to design and assess the balanced performance and emissions of the methanol-fueled hybrid electric marine vessel. Combining clean methanol fuel, hybrid electric propulsion, and optimal real-time control effectively reduces the overall carbon dioxide equivalent emissions from the vessel.

关键词： Mechatronics Optimal control Propulsion Predictive models Real-time systems Fuels optimization Engines system analysis and design Methanol

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CurL-AutoML: Curriculum Learning-based AutoML 20

CurL-AutoML: Curriculum Learning-based AutoML

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20th IEEE International conference on Machine Learning and Applications (ICMLA)

作者： dos Santos Silva, Lucas Nildaimon Silva, Lucas Cardoso Zagatti, Fernando Rezende Sette, Bruno Silva Caseli, Helena de Medeiros Lucredio, Daniel Silva, Diego Furtado Americanas SA Digital Lab Sao Paulo SP Brazil Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil

ISBN: (纸本)9781665443371

AutoML aims to find the best Machine Learning (ML) pipeline in a complex and high-dimensional search space by evaluating multiple algorithm configurations. However, training multiple ML algorithms is time-consuming, and as AutoML tools are frequently time-constrained, the exploration of the search space may find sub-optimal results. In this work, we explore the application of curriculum learning techniques to overcome this limitation. Curriculum and anti-curriculum learning have improved model performance and accelerated the training process on previous empirical investigations using optimization-based models by ordering examples during model training based on their difficulty. We apply and compare curriculum strategies on an AutoML system to accelerate the search space exploration and find good-performing machine learning pipelines efficiently. the results indicate that AutoML can benefit from a curriculum strategy. Furthermore, in most of the evaluated scenarios, the curriculum strategies led to better classification results.

关键词： Automated Machine Learning Curriculum Learning Hyper-Parameter optimization

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Control of an Electrical system by Recurrent Neural Network RSOM map

Control of an Electrical System by Recurrent Neural Network ...

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Nejib Khalfaoui Mohamed Salah Salhi Tahar Bahi LR SITI – ENIT ISET Jendouba Tunisia LR SITI – ENIT ISSAT Mateur Tunisia Electrical Department Badji Mokhtar University Algeria

this paper discusses an intelligent strategy using the Recurrent Self Organizing Map RSOM for anomaly detection over a motor pump. the proposed idea involves the most significant features of RSOM to locate and appreciate the fault origin. It concerns the topological structure of the map, the unsupervised deep learning algorithm, as well as the Unified modeling Language (UML) activity diagram. the measurement and treatment of the coming signal from an appropriate sensor fixed on the experimental bench, was doneat a precise moment in the Automatism and Industrial Computing Laboratory. A comparative analysis of the flaw detection performance was carried out using the RSOM neural map and the spectral analysis method.

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Model-free Adaptive Sliding Mode Control for Interconnected Power systems under DoS Attacks 11

Model-free Adaptive Sliding Mode Control for Interconnected ...

引用

11th IEEE Data Driven Control and Learning systems conference, DDCLS 2022

作者： Chen, Zongyao Bu, Xuhui Guo, Jinli Henan Polytechnic University School of Electrical Engineering and Automation Jiaozuo454003 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a new model-free adaptive sliding mode load frequency control (LFC) scheme is designed for inter-connected power systems, where modeling is difficult and suffers from load change disturbances and denial of service (DoS) attacks. the proposed algorithm only uses real-time I/O data of the power system to achieve a high control performance. Firstly, the dynamic linearization strategy is used to build a data-based model of the power system, and intermittent DoS attacks are modeled by limiting their duration and frequency. Secondly, the model-free adaptive sliding mode control (MFASMC) scheme is designed based on optimization theory and sliding mode reaching law, and its stability is analyzed. Finally, the three-area interconnected power system was selected to test the presented MFASMC scheme. Simulation data shows the effectiveness of the LFC algorithm in this paper. © 2022 IEEE.

关键词： Electric power system interconnection

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SLIVER: A SLIPT-enabled IRS-assisted VLC system for Energy optimization

SLIVER: A SLIPT-enabled IRS-assisted VLC System for Energy O...

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IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Kapila W. S. Palitharathna Anna Maria Vegni Himal A. Suraweera Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy Dept. of Electrical and Electronic Engineering University of Peradeniya Peradeniya Sri Lanka

this paper deals with a simultaneous lightwave power transfer (SLIPT) indoor visible light communication system, supported by intelligent reflective surfaces (IRS). the proposed system, namely SLIVER, aims to reduce the energy consumption, by means of an optimization problem formulated to minimize the average transmit power from luminaries under data rate and energy harvesting constraints at users, and exploiting mirror-based IRS. the complexity in this problem is due to the large combinations from IRS element rotations. To this end, the problem is transformed into an IRS assignment and spot-finding problem. To solve this, a blockwise efficient artificial neural network is proposed. the proposed architecture can predict the user positions and receiver orientations and accordingly find the optimal beamforming matrix, average transmit powers at luminaries, and IRS assignments. Results reveal that SLIVER system is helpful to achieve better performance under a wide range of user mobility, receiver orientations, and blockage conditions. Specifically, up to 60% of the transmit power can be reduced with the use of the IRS, as compared to traditional approaches (i.e., no IRS) and without complex algorithms.

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