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Enhancing Participation of Widespread Distributed Energy Storage Systems in Frequency Regulation Through Partitioning-Based Control

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PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 2025年第1期10卷 76-89页

作者： Lin, Yujun Luo, Haowen Chen, Yin Yang, Qiufan Zhou, Jianyu Chen, Xia Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China State Grid Jingmen Power Supply Co Jingmen 448000 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

In recent years, a significant number of distributed small-capacity energy storage (ES) systems have been integrated into power grids to support grid frequency regulation. However, the challenges associated with high-dimensional control and synergistic operation alongside conventional generators remain unsolved. In this paper, a partitioning-based control approach is developed for the participation of widespread distributed ES systems on frequency control in power systems. The approach comprises a network partitioning method and a two-layer frequency control scheme. The partitioning method utilizes a community detection algorithm in which the weights between the buses are calculated based on the electrical distances. After partitioning the buses into different groups, an optimization-based frequency control system with two layers is established to aggregate and dis-aggregate the inertia and droop coefficients so that frequency regulation and economical operation can be achieved. The effectiveness of the proposed method is demonstrated through numerical simulations on an IEEE 39-bus system. The results confirm the successful elimination of frequency deviations and low operating cost of the proposed approach.

关键词： Costs Power systems Topology Reliability detection algorithms Sustainable development Protection Frequency control Energy storage Resilience community detection algorithm distributed control energy storage frequency response

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Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling

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MULTIVARIATE BEHAVIORAL RESEARCH 2025年第2期60卷 184-210页

作者： Shi, Dingjing Christensen, Alexander P. Day, Eric Anthony Golino, Hudson F. Garrido, Luis Eduardo Univ Oklahoma Dept Psychol Norman OK 73109 USA Vanderbilt Univ Dept Psychol & Human Dev Nashville TN USA Univ Virginia Dept Psychol Charlottesville VA USA Pontificia Univ Catolica Madre & Maestra Santiago Los Caballeros Dominican Rep

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., ***) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (***) yielded higher accuracy and smaller errors than PA;lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then *** and ***. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the *** technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the *** method as a valuable alternate technique. The findings also indicated encouraging results for extending the regulariz

关键词： Network psychometrics dimensionality assessment estimation procedures community detection algorithm Bayesian estimation

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Efficient Vector Influence Clustering Coefficient Based Directed community detection Method

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IEEE ACCESS 2017年 5卷 17106-17116页

作者： Deng, Xiaolong Zhai, Jiayu Lv, Tiejun Yin, Luanyu Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Educ Minist Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Int Sch Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Normal Univ China Acad Social Management Beijing 100875 Peoples R China

community detection algorithms are important for determining the character statistics of complex networks. Compared with the conventional community detection algorithms, which always focus on undirected networks, our algorithm is concentrated on directed networks such as the WeChat moments relationship network and the Sina Micro-Blog follower relationship network. To address disadvantages such as lower execution efficiency and higher deviation of precision that current directed community detection algorithms always have, we propose a newapproach that is based on the triangle structure of community basis and modeled on the local information transfer process to precisely detect communities in directed networks. Based on the directed vector theory in probability graphs and the dynamic information transfer gain (ITG) of vertices in directed networks, we propose the novel ITG method and the corresponding target optimal function for evaluating the partition quality in a community detection algorithm. Then, we combine ITG and the target function to create the new community detection algorithm ITG-directed weighted community clustering for directed networks. With extensive experiments using artificial network data sets and large, real-world network data sets derived from online social media, our algorithm proved to be more accurate and faster in directed networks than several traditional, well-known community detection methods, such as FastGN, order statistics local optimization method, and Infomap.

关键词： community detection algorithm information transfer gain target function optimization community detection in directed network

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Scalable Spectral Clustering for Overlapping community detection in Large-Scale Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第4期32卷 754-767页

作者： Van Lierde, Hadrien Chow, Tommy W. S. Chen, Guanrong City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

While the majority of methods for community detection produce disjoint communities of nodes, most real-world networks naturally involve overlapping communities. In this paper, a scalable method for the detection of overlapping communities in large networks is proposed. The method is based on an extension of the notion of normalized cut to cope with overlapping communities. A spectral clustering algorithm is formulated to solve the related cut minimization problem. When available, the algorithm may take into account prior information about the likelihood for each node to belong to several communities. This information can either be extracted from the available metadata or from node centrality measures. We also introduce a hierarchical version of the algorithm to automatically detect the number of communities. In addition, a new benchmark model extending the stochastic blockmodel for graphs with overlapping communities is formulated. Our experiments show that the proposed spectral method outperforms the state-of-the-art algorithms in terms of computational complexity and accuracy on our benchmark graph model and on five real-world networks, including a lexical network and large-scale social networks. The scalability of the proposed algorithm is also demonstrated on large synthetic graphs with millions of nodes and edges.

关键词： Clustering algorithms Image edge detection Benchmark testing Partitioning algorithms Social network services detection algorithms Optimization community detection algorithm overlapping communities real-world network spectral clustering normalized cut

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Common strategy to improve community detection performance based on the nodes' property

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第2期2卷 68-72页

作者： Du, Wei He, Xiaochen Xi An Jiao Tong Univ Sch Publ Policy & Adm Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Ctr Adm Complex Sci Xian Shaanxi Peoples R China

Improving the community detection algorithm is of great importance. The authors propose a novel method based on the nodes' property in order to detect the community structure. Given a detected community structure, in which nodes have their community signals, the value of the modularity can be changed if a node's community sign change to other communities' signs. Accordingly, the new method readjusts the affiliation between a node and its community in order to raise the modularity value. Experimental results of the detection for a list of open-source networks show that the proposed algorithm can detect better community structure than classic methodologies based on modularity.

关键词： network theory (graphs) graph theory social networking (online) pattern clustering complex networks common strategy community detection performance nodes community detection algorithm detected community structure community signals community sign change communities modularity value

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community detection for directional neural networks inferred from EEG data

Community detection for directional neural networks inferred...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Liu, Ying Moser, Jason Aviyente, Selin Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Psychol E Lansing MI 48824 USA

ISBN: (纸本)9781424441228

One major challenge in neuroscience is to identify the functional modules from multichannel, multiple subjects recordings. Most research on community detection has focused on finding the association matrix based on functional connectivity, instead of effective connectivity, thus not capturing the causality in the network. In this paper, we propose a community detection algorithm suitable for weighted and asymmetric (directed) networks representing effective connectivity, and apply the algorithm to multichannel electroencephalogram (EEG) data. In addition, we extend the algorithm to find one common community structure from multiple subjects.

关键词： algorithm design and analysis Clustering algorithms Communities detection algorithms EEG data Electroencephalography Neuroscience Partitioning algorithms asymmetric networks community detection algorithm directional neural networks electroencephalography

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Modularity Based community detection in Hypergraphs 1

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18th International Workshop on algorithms and Models for the Web Graph (WAW)

作者： Kaminski, Bogumil Misiorek, Pawel Pralat, Pawel Theberge, Francois SGH Warsaw Sch Econ Decis Anal & Support Unit Warsaw Poland Poznan Univ Tech Inst Comp Sci Poznan Poland Toronto Metropolitan Univ Dept Math Toronto ON Canada Tutte Inst Math & Comp Ottawa ON Canada

ISBN: (数字)9783031322969

ISBN: (纸本)9783031322952;9783031322969

In this paper, we make a significant step toward designing a scalable community detection algorithm using hypergraph modularity function. The main obstacle with adjusting the initial stage of the classical Louvain algorithm is dealt via carefully adjusted linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. It remains to properly tune the algorithm and design a mechanism to adjust the weights in the modularity function (in an unsupervised way), depending on how often nodes in one community share hyperedges with nodes from other communities. It will be done in the journal version of this paper.

关键词： community detection algorithm Hypergraphs Modularity Function

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community detection on Large Graph Datasets for Recommender Systems 14

Community Detection on Large Graph Datasets for Recommender ...

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Parimi, Rohit Caragea, Doina Kansas State Univ CIS Dept Manhattan KS 66506 USA

ISBN: (纸本)9781479942749

The explosion of content on World Wide Web (WWW) means that consumers are presented with a wide variety of items to choose from ( items that concur with their taste and requirements). The generation of personalized consumer recommendations has become a crucial functionality for many web applications, yet a challenging task, given the scale and nature of the data. One popular solution to creating personalized item suggestions to users is recommender systems. In this work, we propose an approach that integrates community detection with neighborhood-based recommender systems, specifically, the Adsorption algorithm, for recommending items using implicit user preferences. Network communities represent a principled way of organizing real-world networks into densely connected clusters of nodes. We believe that these dense clusters identified by the community detection algorithm will be helpful to construct user neighborhoods for Adsorption algorithm for recommending collaborators and books to users. Through comprehensive experimental evaluations on the DBLP co-author dataset and BookCrossing dataset, the proposed approach of integrating community detection with the Adsorption algorithm is shown to deliver good performance.

关键词： Internet database management systems graph theory recommender systems Book Crossing dataset DBLP coauthor dataset WWW Web applications World Wide Web adsorption algorithm community detection algorithm dense clusters implicit user preferences items recommendation large graph datasets neighborhood-based recommender systems network communities personalized consumer recommendations personalized item suggestions real-world networks user neighborhoods Adsorption algorithm design and analysis Communities detection algorithms Image edge detection Recommender systems Training Adsorption algorithm Collaborative Filtering community detection Neighborhood-based Approaches Recommender systems World Wide Web detection algorithms algorithm design and analysis Edge detection web applications collaborative filtering database management systems Network communities Graph theory Adsorption Consumers Internets

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community detection in multi-relational directional networks

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 67卷

作者： Roozbahani, Zahra Rezaeenour, Jalal Katanforoush, Ali Univ Qom Fac Technol & Engn Dept Comp Engn & IT Qom Iran Univ Qom Fac Technol & Engn Dept Ind Engn Qom Iran Shahid Beheshti Univ Fac Math Sci Dept Data & Comp Sci Tehran Iran

community detection algorithms are essential tools that allow us to discover organizational principles in net-works. Today, in most cases, social networks are modeled as a multi-relational network. So far, despite the introduction of community detection algorithms specific to multi-relational networks, very few of these algo-rithms have been able to find overlapping communities in a multi-relational directional network. In this paper, we present OCMRN (Overlapping Communities in Multi-Relational Networks), an overlapping community detection method in multi-relational directional networks. A semi-supervised method is used in the training phase of this algorithm which allows determining the importance of each layer to shape communities. The proposed method is evaluated on nine real and eight synthetic datasets and is compared with different algo-rithms. Well-known evaluation criteria are used to compare overlapping and non-overlapping communities resulting from various algorithms. The high accuracy of the results obtained from the proposed model suggests this algorithm can be used with high confidence for community detection of multi-relational directional net-works to find overlapping communities.

关键词： Multi-Relational Network Overlapping Communities community detection algorithm Probabilistic Model

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Review of community detection over Social Media: Graph Prospective

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第2期10卷 591-602页

作者： Jain, Pranita Tomar, Deepak Singh Maulana Azad Natl Inst Technol Dept Comp Sci Bhopal 462001 India

community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

关键词： community detection social media social media mining homophily influence confounding social theory community detection algorithm

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