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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1644 条记录，以下是651-660 订阅

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A Method for Ensuring Technical Feasibility of Distributed Balancing in Power Systems, Considering Peer-to-Peer Balancing Energy Trade

A Method for Ensuring Technical Feasibility of Distributed B...

引用

3rd International conference on Intelligent Computing and optimization, ICO 2020

作者： Drabecki, Mariusz Institute of Control and Computation Engineering Warsaw University of Technology 15/19 Nowowiejska Street Warsaw Poland

ISBN: (纸本)9783030681531

In this paper a method for ensuring network feasibility of power flow (in terms of all network constraints), when energy in power system is balanced via peer-to-peer contracts, is proposed and analyzed. The method considers subjective benefits (utility functions) of market participants. The method is based on two optimization problems originated from Optimal Power Flow standard formulations, which can be solved by system’s operator. It possibly can be used in power systems with high penetration of distributed energy resources (DERs) to give incentive to build and actively control those resources by market participants. The method was tested on 9-bus test system, under three different preference scenarios. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric load flow

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Active and Reactive Power Coordinated Optimal Dispatch in Active Distribution network Considering Spatial-Temporal Correlation of Wind Power 3

Active and Reactive Power Coordinated Optimal Dispatch in Ac...

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3rd International conference on Environmental Prevention and Pollution control Technologies, EPPCT 2021

作者： Qu, Hanbing Yang, Fu Lin, Qirong Jia, Yujian State Grid Jinan Power Supply Company Shandong Province Jinan250001 China

The high penetration of renewable energy increases the difficulty of optimal dispatch and brings challenges to the safe and economic operation of the distribution network. This paper proposed an active and reactive power coordinated optimal dispatch model, and the spatial-Temporal correlation of wind power output is considered. Firstly, the copula function and non-parametric estimation methods are adopted to model the spatial-Temporal correlation of wind power. Secondly, the parameters in the copula fitting function are estimated based on the maximum likelihood estimation method. Besides, the active power and reactive power outputs of various flexible resources in the active distribution network (ADN) are coordinated. The proposed model can significantly reduce the network loss of the ADN, improve efficiency, and optimize the power purchase plan. A case study on a modified IEEE 33-bus system verified the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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KPC: Learning-Based Model Predictive control with Deterministic Guarantees 3

KPC: Learning-Based Model Predictive Control with Determinis...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Maddalena, Emilio T. Scharnhorst, Paul Jiang, Yuning Jones, Colin N. Ecole Polytech Federale Lausanne Lausanne Switzerland Swiss Ctr Elect & Microtechnol Neuch atel Switzerland

We propose Kernel Predictive control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-parametric kernel regression. By treating each prediction step individually, we dispense with the need of propagating sets through highly non-linear maps, a procedure that often involves multiple conservative approximation steps. Finite-sample error bounds are then used to enforce state-feasibility by employing an efficient robust formulation. We then present a relaxation strategy that exploits on-line data to weaken the optimization problem constraints while preserving safety. Two numerical examples are provided to illustrate the applicability of the proposed control method.

关键词： Model Predictive control Safe Learning Deterministic Guarantees Non-Parametric Kernel Regression

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Tie-line Power Adjustment Method Based on Proximal Policy optimization Algorithm 3

Tie-line Power Adjustment Method Based on Proximal Policy Op...

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2020 3rd International Symposium on Power Electronics and control Engineering, ISPECE 2020

作者： Hou, Jinxiu Yu, Zhihong Zheng, Qingping Xu, Huating Li, Shufang China Electric Power Research Institute Beijing100192 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

The tie-line power adjustment is an essential part of the power system operation state calculation. Various existing algorithms to solve the tie-line power adjustment problem are mainly implemented by introducing tie-line power equation constraints into conventional power flow calculations. Such methods have low calculation efficiency, not enough automation, and are prone to non-convergence in the power flow calculation. In this paper, the tie-line power adjustment problem is formulated as a Markov decision process, and the proximal policy optimization algorithm is introduced to optimize the decision policy. In order to enhance the effectiveness of the proposed method, a new deep neural network structure suitable for the proximal policy optimization algorithm is designed. The proposed method is verified with the IEEE 39-bus system. © Published under licence by IOP Publishing Ltd.

关键词： Markov processes

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The benefits of sharing: a cloud-aided performance-driven framework to learn optimal feedback policies 3

The benefits of sharing: a cloud-aided performance-driven fr...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Ferrarotti, Laura Breschi, Valentina Bemporad, Alberto IMT Sch Adv Studies Lucca I-55100 Lucca Italy Politecn Milan I-20133 Milan Italy

Mass-produced systems are constructed and calibrated to be nominally the same, and they usually have similar goals. When several of these systems can share information with the cloud, one can exploit their similarities to improve the design of individual control policies. In this framework, we aim to exploit these similarities and the connection to the cloud to solve a sharing-based control policy optimization problem, leveraging on information provided by trustworthy agents. In this paper, we propose to combine the optimal policy search method introduced in (Ferrarotti and Bemporad, 2019) with the Alternating Direction Method of Multipliers, by relying on a weighted surrogate of the experiences of each device shared within the cloud. A preliminary example shows the effectiveness of the proposed sharing-based method, which results in improved performance with respect to the ones attained when neglecting the similarities among devices and when enforcing consensus among their policies.

关键词： Reinforcement Learning control Sharing control over networks

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3rd International conference on Innovative Computing and Communication, ICICC 2020

3rd International Conference on Innovative Computing and Com...

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3rd International conference on Innovative Computing and Communication, ICICC 2020

ISBN: (纸本)9789811551123

The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: A Primer on Opinion Mining: The Growing Research Area;a Generic Framework for Evolution of Deep Neural networks Using Genetic Algorithms;multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm;driver Fatigue and Distraction Analysis Using Machine Learning Algorithms;a Hybridized Auto-encoder and Convolution Neural network-Based Model for Plant Identification;curaBand: Health Monitoring and Warning System;optimization Algorithm-Based Artificial Neural network control of Nonlinear Systems;mHealth for Mental Health;elman and Jordan Recurrence in Convolutional Neural networks Using Attention Window;state of Charge Estimation Using Data-Driven Techniques for Storage Devices in Electric Vehicles;Application of Stochastic Approximation for Self-tuning of PID in Unmanned Surface Vehicles;dark web Activity on Tor—Investigation Challenges and Retrieval of Memory Artifacts;A Heterogeneous Dynamic Scheduling Minimize Energy—HDSME;face Recognition-Based Automated Attendance System;Human Emotion Recognition from Spontaneous Thermal Image Sequence Using GPU Accelerated Emotion Landmark Localization and Parallel Deep Emotion Net;delay Based Multi-controller Placement in Software Define Tactical network;security in Cloud Computing for Sensitive Data: Challenges and Propositions;Utility of Game Theory in Defensive Wireless Sensor networks (WSNs);robust Approach for Emotion Classification Using Gait;routing in Internet of Things Using Cellular Automata;depression Detection Among Social Media Users Using Machine Learning;Security-Based LEACH Protocol for Wireless Sensor network;Design of High-Speed FPGA Based CASU Using CBNS Arithmetic: Extension to CFFT Processor;blockchain-Based Boothless E-Voting System;preface.

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Sample Complexity of Linear Quadratic Gaussian (LQG) control for Output Feedback Systems 3

Sample Complexity of Linear Quadratic Gaussian (LQG) Control...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Zheng, Yang Furieri, Luca Kamgarpour, Maryam Li, Na Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Ecole Polytech Fed Lausanne Lab Automat Lausanne Switzerland Univ British Columbia Elect & Comp Engn Vancouver BC Canada

This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems with unknown dynamics. We establish an end-to-end sample complexity bound on learning a robust LQG controller for open-loop stable plants. This is achieved using a robust synthesis procedure, where we first estimate a model from a single input-output trajectory of finite length, identify an H-infinity bound on the estimation error, and then design a robust controller using the estimated model and its quantified uncertainty. Our synthesis procedure leverages a recent control tool called Input-Output Parameterization (IOP) that enables robust controller design using convex optimization. For open-loop stable systems, we prove that the LQG performance degrades linearly with respect to the model estimation error using the proposed synthesis procedure. Despite the hidden states in the LQG problem, the achieved scaling matches previous results on learning Linear Quadratic Regulator (LQR) controllers with full state observations.

关键词： Convex optimization

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Research on Dynamic Analysis Technology of Economic Evaluation of Power Grid Digital Technology 3

Research on Dynamic Analysis Technology of Economic Evaluati...

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3rd International conference on Applied Machine Learning, ICAML 2021

作者： Wang, Gang Dong, Aidi Pan, Jianhong Global Energy Interconnection Research Institute Co. Ltd Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Jilin Electric Power Co. Ltd Changchun130021 China

ISBN: (纸本)9781665421256

Digitalization plays a more and more important role in the development of power grid. Traditional technical and economic evaluation methods are difficult to meet the needs of Digital Construction Evaluation under the new situation. Based on Zachman and other enterprise architecture theories, aiming at the business demand scenarios such as basic resource operation, power data value-added services, digital platform ecological construction, this paper studies the optimization technology of power grid digital architecture meta model, including business architecture, data architecture, application architecture, technology architecture meta model optimization, etc. Research on dynamic analysis technology of intelligent quantitative control, including technology and economic analysis and evaluation technology of digital economic benefits of power grid enterprises for multi application scenarios, digital technology and economic evaluation model based on multi-dimensional index sensitivity analysis, technology and economic analysis model of multi-source correlation between business demand scenarios and digital architecture, and dynamic analysis model technology of intelligent quantitative control. This paper innovatively studies the relevance of business demand scenarios, technical and economic analysis rules and enterprise digital architecture, and constructs a multi-source digital benefit analysis model based on the integration of variables and factors. According to the different business scenarios such as basic resource operation, power data value-added services, and digital platform ecological construction, this paper analyzes the relationship between business demand scenarios and digital architecture, Dynamic adjustment of technical and economic evaluation index and weight can more accurately complete the digital economic benefit evaluation of power grid enterprises. © 2021 IEEE

关键词： Sensitivity analysis

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Approximate Midpoint Policy Iteration for Linear Quadratic control 3

Approximate Midpoint Policy Iteration for Linear Quadratic C...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Gravell, Benjamin Shames, Iman Summers, Tyler Univ Texas Dallas Richardson TX 75083 USA Univ Melbourne Melbourne Vic Australia

We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings. The algorithm is a variation of Newton's method, and we show that in the model-based setting it achieves cubic convergence, which is superior to standard policy iteration and policy gradient algorithms that achieve quadratic and linear convergence, respectively. We also demonstrate that the algorithm can be approximately implemented without knowledge of the dynamics model by using least-squares estimates of the state-action value function from trajectory data, from which policy improvements can be obtained. With sufficient trajectory data, the policy iterates converge cubically to approximately optimal policies, and this occurs with the same available sample budget as the approximate standard policy iteration. Numerical experiments demonstrate effectiveness of the proposed algorithms.

关键词： Optimal control linear quadratic regulator (LQR) optimization Newton method midpoint Newton method data-driven model-free

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Reconfigurable Intelligent Surfaces in Dynamic Rich Scattering Environments: BiLSTM-Based optimization for Accurate User Localization

Reconfigurable Intelligent Surfaces in Dynamic Rich Scatteri...

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6G networking (6GNet), International conference on

作者： Anum Umer Ivo Müürsepp Muhammad Mahtab Alam Thomas Johann Seebeck Department of Electronics Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350378597

ISBN: (纸本)9798350378603

The integration of reconfigurable intelligent surfaces (RIS) in wireless environments offers channel programmability and dynamic control over propagation channels, which is expected to play a crucial role in sixth generation (6G) networks. The majority of RIS-related research has focused on simpler, quasi-free-space conditions, where wireless channels are typically modeled analytically. However, many practical localization scenarios unfold in environments characterized by rich scattering that also change over time. These dynamic and complex conditions pose significant challenges in determining the optimal RIS configuration to maximize localization accuracy. In this paper, we present our approach to overcoming this challenge. This paper introduces a novel approach that leverages a bidirectional long-short term memory (biLSTM) network, trained with a simulator that accurately reflects wave physics, to capture the relationship between wireless channels and the RIS configuration under dynamic, rich-scattering conditions. We use this approach to optimize RIS configurations for enhanced user equipment (UE) localization, measured by mean squared error (MSE). Through extensive simulations, we demonstrate that our approach adapts RIS configurations to significantly improve localization accuracy in such dynamically changing rich scattering environments.

关键词： Location awareness Wireless communication 6G mobile communication Wireless sensor networks Analytical models Accuracy Heuristic algorithms Scattering Reconfigurable intelligent surfaces Numerical models

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