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Online identification of stability region for large-scale wind farms, Part I: clustering based piecewise affine impedance modeling

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International Journal of Electrical Power & Energy Systems 2025年 170卷

作者： Jia Luo Peng Wang Haoran Zhao Chenxinwei Yuan Tiancheng Liu Vladimir Terzija School of Electrical Engineering Shandong University Jinan 250061 China School of Engineering Newcastle University Newcastle upon Tyne NE1 7RU UK

The online assessment of the small-signal stability of wind farms faces challenges in accurately identifying impedance due to the unknown structure and parameters of wind turbine generators. Besides, impedance recalculation is needed under varying operating points. This study proposes a piecewise affine method for impedance modeling and identification under diverse operating points. The piecewise affine impedance is derived through a process that combines offline impedance modeling and online identification. In the offline modeling, the affine impedance is performed in the parameter space of the operating point of the wind turbine generator. A clustering algorithm is employed to optimize the partitioning of the parameter space. In each partition, the impedance is expressed as a first-order explicit function of the complex variable and the operating state variables. Moving to online applications, impedance identification is readily achieved with knowledge of the real-time measured operating point. By locating the operating point in the partitions of the parameter space, the coefficients of the first-order affine models can be determined. Based on the affine first-order impedance of wind turbine generators, the nodal admittance matrix for large-scale wind farms is established with high accuracy. The sensitivity of the dominant eigenvalue is analyzed with respect to the operating point. In validation, the accuracy and efficiency of the piecewise affine impedance model are verified under varying operating points. The online stability and modal analysis based on PWA-identified impedance are validated. A physical experiment is performed to validate the proposed method, which involves impedance data acquisition from frequency scan, offline piecewise affine modeling, online impedance identification, and online stability assessment.

关键词： clustering algorithm Impedance model Piecewise affine Small-signal stability Space partitioning Wind turbine generator

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A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

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ACM TRANSACTIONS ON SPATIAL algorithmS AND SYSTEMS 2020年第3期6卷 1–24页

作者： Ferreira, Danielle L. Nunes, Bruno A. A. Campos, Carlos Alberto, V Obraczka, Katia Univ Calif Santa Cruz UCSC 1156 High St Santa Cruz CA 95064 USA Univ Calif San Francisco UCSF 1500 Owens St 1630 Third St San Francisco CA USA Fed Univ State Rio de Janeiro UNIRIO Av Pasteur 296 Rio De Janeiro RJ Brazil Univ Calif San Francisco UCSF Dept Cardiol San Francisco CA 94143 USA

Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of more sophisticated models to characterize human mobility in urban areas. In this work, our main goal is to extract spatio-temporal features that characterize user mobility and, based on the similarity of these features, identify user communities. To this end, we propose a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images. These features are then fed to a clustering algorithm that identifies the underlying community structures. We use a diverse urban mobility dataset to validate the proposed framework. Our results show that the proposed framework is able to significantly increase the similarity between intra-community nodes (by up to 107%) as well as dissimilarity between inter-community nodes (up to 54%) when compared against no pre-processing of the datasets, i.e without pre-processing the datasets through any feature fusion method. Moreover, it was also able to reach up to 100% improvement when compared against community identification using Principal Component Analysis (PCA). Our results also show that the proposed approach yields significant increase in contact time amongst users belonging to the same community, by up to 80% when compared to the average contact time when not considering community structures, and by up to 150% when compared to the baseline. To the best of our knowledge, our proposal is the first to consider deep convolutional autoencoding to perform automatic extraction of non-linear spatio-temporal mobility features characterizing individual users from raw mobility datasets with the goal of identifying user communities.

关键词： Deep learning autoencoder convolutional neural networks T-sne principal component analysis clustering algorithm human mobility geographical preference user community mobility feature extraction

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Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs

Array

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Array 2025年 27卷

作者： Azamuddin Ab Rahman Sakib Iqram Hamim Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

The Internet of Things (IoTs) increasingly depends on Wireless Sensor Networks (WSNs) for real time data collection and communication. However, due to the limited battery capacity of sensor nodes, energy efficiency remains a critical challenge, especially since data transmission consumes the most energy. This study introduces an enhanced energy aware clustering approach that combines an improved K-Means algorithm with an adaptive distance threshold to optimize relay node selection and cluster formation. The method considers node proximity, residual energy, and overall network conditions to achieve balanced energy distribution across the network. The proposed approach was evaluated against established protocols including Hybrid Energy-Efficient Distributed clustering (HEED), Threshold-Sensitive Energy-Efficient Sensor Network (TEEN), and previous versions of the Energy Efficient Cluster and Routing (EECR) protocol under three different deployment scenarios. Experimental results show that the enhanced EECR protocol reduces energy consumption by 5 % and significantly extends network lifetime, outperforming conventional techniques. The inclusion of adaptive distance thresholds proves effective in minimizing unnecessary energy drain and improving the reliability of data transmission. These results highlight the method's potential as a scalable and energy efficient solution for future IoT applications involving large scale sensor networks.

关键词： Energy Improved k-means Distance threshold clustering algorithm Wireless sensor networks Internet of things

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Semisupervised Fuzzy clustering With Partition Information of Subsets

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2019年第9期27卷 1726-1737页

作者： Mei, Jian-Ping Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

Pairwise constraint is a type of side information that is widely considered in existing semisupervised clustering approaches. In this paper, we explore a new form of supervision for clustering. We consider the partition results of a number of subsets as additional information to assist clustering. Compared to the pairwise constraint, which only involves the "must-link" or "cannot-link" relationship of two objects, the partition of a subset of objects provides information about the group structure of more objects and hence can possibly serve as a more effective form of supervision for clustering. In this paper, we instantiate the idea of clustering with subset partitions under the fuzzy clustering framework for document categorization. The proposed fuzzy clustering approach is formulated to learn from the partition of subsets and has the ability to handle high-dimensional document data. Specifically, the partition results of subsets are collectively transformed into pairwise relationships, based on which a penalty term is constructed and incorporated into a cosine-distance-based fuzzy c-means approach. The experimental results on benchmark data sets demonstrate the effectiveness of the proposed approach for a semisupervised document clustering.

关键词： clustering algorithm document categorization fuzzy clustering semisupervised learning

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Ship Detection in High-Resolution SAR Images by clustering Spatially Enhanced Pixel Descriptor

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第8期57卷 5407-5423页

作者： Lang, Haitao Xi, Yuyang Zhang, Xi Beijing Univ Chem Technol Sch Sci Dept Phys & Elect Beijing 100029 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Shandong Peoples R China

This paper proposes a new scheme for detecting ship targets in high-resolution (HR) single-channel synthetic aperture radar (SAR) images. By using the proposed spatially enhanced pixel descriptor (SEPD) and the modified density-based spatial clustering of application with noise (M-DBSCAN), this scheme can overcome typical challenges of ship detection in HR SAR images. Specifically, the proposed SEPD maps the representation for a given pixel in an SAR image into a high-dimensional feature space by embedding spatial and intensity information of its neighborhood synchronously. It enables the spatial structure information of ship targets and textural information of the sea surface to be preserved by the SEPD feature vector, leading to a significant improvement in the separability between ship targets and sea clutter. A statistical study shows that, in SEPD feature space, a large amount of pixels belonging to sea clutter gather densely in the low-value region and are surrounded by a tiny proportion of ship targets that are distributed sparsely in the high-value region. This distribution characteristic motivates us to apply a density-based clustering approach to distinguish ship targets from the sea clutter. To overcome the weakness of original DBSCAN clustering algorithm and make it suitable for the requirements of ship detection in SAR images, we propose the method of M-DBSCAN, which introduces three critical improvements, including a new dimensionality independent distance metric, a one-class clustering strategy, and an entirely deterministic approach to border points. A novel ship detector is proposed by applying M-DBSCAN to cluster pixels that are represented by SEPD descriptor. Comprehensive experiments demonstrate that the proposed method outperforms other intensity-based clustering methods (k-means and fuzzy c-means), and widely used intensity threshold-based method (constant false alarm rate detector) in most circumstances, and can effectively handle various chal

关键词： clustering algorithm constant false alarm rate (CFAR) modified density-based spatial clustering of applications with noise (M-DBSCAN) ship detection spatially enhanced pixel descriptor (SEPD) synthetic aperture radar (SAR)

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Sparse learning based on clustering by fast search and find of density peaks

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第23期78卷 33261-33277页

作者： Li, Pengqing Deng, Xuelian Zhang, Leyuan Gan, Jiangzhang Li, Jiaye Li, Yonggang Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China Guangxi Univ Chinese Med Coll Publ Hlth & Management Nanning Peoples R China

clustering by fast search and find of density peaks (CFSFDP) is a novel clustering algorithm proposed in recent years. The algorithm has the advantages of low computational complexity and high accuracy. However, the truncation distance d(c) needs to be determined according to user experience. Aiming to overcome these drawbacks, this paper proposes a new algorithm named Sparse learning based on clustering by fast search and find of density peaks (SL-CFSFDP). Compared to CFSFDP, the proposed algorithm can obtain d(c) automatically, and it uses sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. SL-CFSFDP combines the local density and the distance delta(i) to automatically determine cluster centers, after which the remaining data points are assigned to clusters according to the local density and distance delta(i). Extensive experimental results on both synthetic and benchmark datasets show that SL-CFSFDP is superior to DBSCAN and CFSFDP.

关键词： Truncation distance Sparse learning Local density Density peaks clustering algorithm

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Data clustering: Theory, algorithms, and Applications, Second Edition

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丛书名： Mathematics in Industry

2020年

作者： Guojun Gan Chaoqun Ma Jianhong Wu

ISBN: (数字)9781611976335

ISBN: (纸本)9781611976328

Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments.

Data clustering: Theory, algorithms, and Applications, Second Edition

covers the basics of data clustering,
includes a list of popular clustering algorithms, and
provides program code that helps users implement clustering algorithms.

This book will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.

关键词： Data clustering Cluster analysis clustering algorithm Unsupervised learning Data mining Machine learning Pattern recognition Visualization Statistical data analysis Bioinformatics Knowledge discovery

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Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 128卷 470-505页

作者： Rajendran, Suchithra Zack, Joshua Univ Missouri Dept Ind & Mfg Syst Engn Columbia MO 65211 USA Univ Missouri Dept Mkt Columbia MO 65211 USA

Expected to launch globally in the forthcoming years, air taxis are brand-new aviation ridesharing services that will be provided by international logistic pioneers. This study is one of the first to estimate the demand for such a service and provide recommendations on the potential location of facilities to support this network operations. We adopt a two-phase approach: phase-1 estimates the demand for air taxi services by taking a subset of the regular taxi customers who are likely to avail this service, and phase-2 proposes a constrained clustering approach, with multimodal transportation-based warm start technique, to identify potential sites for locating infrastructures based on the estimated demand. We test the feasibility of the proposed approach using millions of real-life New York City taxi records. Results indicate that large facilities with a capacity of nearly 150 landings/hour have to be established in JFK International Airport and South Central Park, while smaller stops are required in World Trade Center, Washington Square and Allerton Ballfields. In addition, we evaluate the impact of the commuter's "willingness to fly" rate, demand fulfillment rate and time-cost tradeoffs. Our analysis shows that the percentage of time savings and "willingness to fly" rate did not significantly impact location decisions and the number of sites, while it is necessary to conduct an intense market study to determine on-road travel limits. Insights provided in this study can act as a decision support tool for any logistics company that is interested in venturing into the air taxi market.

关键词： Air taxi Soaring service Operating hubs Location decisions clustering algorithm Decision support tool

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Study on Composition Analysis and Identification of Ancient Glass Products

Study on Composition Analysis and Identification of Ancient ...

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作者： Nian Yang Xi'an Jiaotong University

Glass is the precious material evidence of the early trade of the ancient Silk Road, but the ancient glass is easily weathered by the influence of burial, and its composition ratio changes, which affects the correct judgment of its category. The study of the composition analysis and identification of ancient glass products is of great help to understand the social culture and foreign trade civilization at that time. This paper mainly studies the composition analysis and identification of ancient glassware, to evaluate, predict and classify the ancient glassware, this paper establishes a comprehensive evaluation model, using the chi-square test, K-means clustering model, decision tree model, Lasso regression and grey correlation degree test. It helps archaeologists to analyze and predict the correlation between the weathering degree of cultural relics and their attributes and chemical composition content, and according to the existing classification standards of cultural relics.A labelled subclassification scheme is formulated to identify the types of unknown cultural relics. At the same time, the correlation between the chemical components of different types of cultural relics was analyzed.

关键词： Ancient glass relics correlation analysis clustering algorithm decision tree

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A Software-Defined clustering Mechanism for Underwater Acoustic Sensor Networks

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IEEE ACCESS 2019年 7卷 121742-121754页

作者： Wang, Jianping Gao, Guohong Qu, Peixin Chen, Wei Zhang, Shujing Zuo, Xiangang Yu, Zhou Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China

Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. clustering is a crucial strategy which determines the performance and the lifetime of UASNs significantly. As a technological innovation, software defined networking (SDN) separates the data plane from the control plane to build a programmable network infrastructure. This paper presents a clustering mechanism based on SDN. Firstly, we introduce the architecture of software-defined UASN named SD-UASN. Secondly, we investigate hardware models of the underwater node and the surface controller. Thirdly, we define the communication procedure of SD-UASN, and implement the clustering mechanism. Finally, we perform simulations to verify the effectiveness of the proposed mechanism. The results reveal that a tradeoff of multiple constraints is achieved, and the performance of the clustering mechanism can be enhanced greatly. This work provides essential theoretical and technical support for software-defined UASNs.

关键词： Underwater acoustic sensor networks (UASNs) clustering algorithm software defined networking (SDN) software-defined UASN (SD-UASN) OpenFlow

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