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Autonomous control management of synchronous generators in microgrid system

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Autonomous control management of synchronous generators in m...

International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Hafiz Ghulam Murtza Qamar Adil Ashraf Fareed Ahmad Ayesha Riaz Muhammad Imran Muhammad Zain Yousaf Imran Khan School of Electrical Engineering Yanshan University Qinhuangdao 066004 People's Republic of China Department of Electrical Electronics and Computer Systems University of Sargodha People's Republic of China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 People's Republic of China

ISBN: (数字)9781837243150

Decentralized excitation control based on local asynchronous clock feedback measurement leads to the problem of decentralized incompatibility of excitation controllers, which reduces the ability of excitation control design to improve the stability of power systems. Therefore, the problem of decentralized compatible excitation control is proposed, and the effect of excitation control on the dynamic characteristics of the system under compatible and incompatible modes is compared and analyzed, and the shortcomings of decentralized incompatible excitation control and the advantages of decentralized compatible excitation control are given.

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

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arXiv 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

Faster Convergence of Local SGD for Over-Parameterized Models

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arXiv 2022年

作者： Qin, Tiancheng Rasoul Etesami, S. Uribe, Cesár A. Department of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Rice University United States

Modern machine learning architectures are often highly expressive. They are usually overparameterized and can interpolate the data by driving the empirical loss close to zero. We analyze the convergence of Local SGD (or FedAvg) for such over-parameterized models in the heterogeneous data setting and improve upon the existing literature by establishing the following convergence rates. For general convex loss functions, we establish an error bound of O(1/T) under a mild data similarity assumption and an error bound of O(K/T) otherwise, where K is the number of local steps and T is the total number of iterations. For non-convex loss functions we prove an _error bound of O(K/T). These bounds improve upon the best previous bound of O(1/√nT) in both cases, where n is the number of nodes, when no assumption on the model being over-parameterized is made. We complete our results by providing problem instances in which our established convergence rates are tight to a constant factor with a reasonably small stepsize. Finally, we validate our theoretical results by performing large-scale numerical experiments that reveal the convergence behavior of Local SGD for practical over-parameterized deep learning models, in which the O(1/T) convergence rate of Local SGD is clearly shown. Copyright © 2022, The Authors. All rights reserved.

关键词： Parameterization

Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

Intelligent data-driven methods for enhancing distributed excitation control in electrical grid

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Intelligent data-driven methods for enhancing distributed ex...

International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Hafiz Ghulam Murtza Qamar Muhammad Imran Fareed Ahmad Ayesha Riaz Adil Ashraf Muhammad Zain Yousaf Imran Khan School of Electrical Engineering Yanshan University Qinhuangdao 066004 People's Republic of China Department of Electrical Electronics and Computer Systems University of Sargodha People's Republic of China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 People's Republic of China

ISBN: (数字)9781837243150

This paper proposes a decentralized excitation control method based on synchronized estimation using asynchronous measurements from voltage/current transformers (VT/CT), addressing the challenges of decentralized excitation control caused by asynchronous updates. Due to the high cost and limited use of Phasor Measurement Units (PMUs), synchronized generator output measurements are often not feasible. The proposed method establishes a framework for decentralized excitation control that aligns with synchronized estimation in multi-machine power systems. It develops an estimation model for synchronized acquisition of excitation control feedback inputs, even under asynchronous sampling conditions. Simulation results using IEEE standard test systems demonstrate that synchronized estimated values offer a more accurate representation of the system’s status compared to asynchronous measurements, with better compatibility with the network structure. The findings show that decentralized excitation control based on synchronized estimation significantly enhances power system stability compared to traditional control methods that rely on asynchronous feedback.

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The Age of Generative AI and AI-Generated Everything

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

Implementational Aspects for the Characterization of Low-Inertia in Power systems

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Implementational Aspects for the Characterization of Low-Ine...

Annual North-American Power Symposium

作者： M. D. Baquedano-Aguilar Nader Aljohani Sean P. Meyn Arturo Bretas Department of Electrical & Computer Engineering University of Florida Gainesville Florida Pacific Northwest National Laboratory Distributed Systems Group Richland WA

ISBN: (纸本)9781665499224

The replacement of conventional generating units with non-conventional renewable energy resources impacts the reliability of power systems. With high integration of renewable-based generation resources, there is a reduction in total system inertia, which could lead to systems with less time to react for frequency restoration in case of unexpected unit failures. Though several power system testbeds are available for studying and analysing different stability phenomena, none are suitable for characterizing, or analyzing low-inertia conditions. In this paper, a power system simulation environment is introduced for investigating control architectures for power grids, with special attention to the impact of high penetration of inverter-based generation resources (IBRs). The testbed is applied to the WSCC 9- bus test system, modified so that a range of IBR penetration levels can be analyzed (from zero to 80%). The testbed is suitable for studying both fast and slow electromechanical phenomena. A conclusion from numerical experiments is that high levels of IBRs do not necessarily result in low-inertia. This is due in part to the layers of control in the grid already in place to avoid frequency excursions.

关键词： Renewable energy sources Time-frequency analysis System dynamics Simulation Power system stability Power grids Stability analysis

A Novel Long Short-Term Memory (LSTM)-Based Degradation Detection in Photovoltaic systems using Performance Ratio

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A Novel Long Short-Term Memory (LSTM)-Based Degradation Dete...

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Amirhossein Dadashpour Aref Eskandari Amir Nedaei Sherko Salehpour Parviz Parvin Andreas Livera George Makrides Mohammadreza Aghaei Department of Electrical Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology (IUST) Tehran Iran Department of Physics and Energy Engineering Amirkabir University of Technology (AUT) Tehran Iran PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus (UCY) PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Ocean Operations and Civil Engineering Norwegian University of Science and Technology (NTNU) Alesund Norway Professor Department of Sustainable Systems Engineering (INATECH) University of Freiburg Freiburg Germany

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic (PV) systems deteriorate over time as a result of prolonged exposure to UV light and harsh weather conditions. This degradation can have a major impact on the performance of the PV systems and increase their levelized cost of energy (LCOE). To deal with this problem, in this paper, a method has been proposed based on predicting The PV systems' performance ratio, which is a measure of the actual output compared to the expected output. To estimate the performance ratio, a dataset including a PV array of normal and degraded states under different environmental conditions has been created. The main model uses an architecture with long short-term memory (LSTM). The model results show high accuracy in predicting the performance ratio of systems. The model achieved the outstanding $\mathbf{R}^{2}$ scores of $\mathbf{9 9. 4 7 \%}$ and $\mathbf{9 9. 4 6 \%}$ and the root mean squared error (RMSE) values of $\mathbf{0. 0 1 6 3}$ and 0.0169 for the training and testing processes, respectively.

关键词： Degradation Photovoltaic systems Training Accuracy System performance Predictive models Long short term memory Testing Meteorology

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph