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Stator Single-Phase-to-Ground Fault Protection for Bus-connected Powerformers Based on hierarchical clustering algorithm

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2013年第4期28卷 991-998页

作者： Wang, Yuan Yuan Zeng, Xiang Jun Dong, Zhao Yang Xu, Yao Yuan, Jun Huang, Yue Changsha Univ Sci & Technol Sch Elect & Informat Engn Hunan Prov Key Lab Smart Grids Operat & Control Changsha 410076 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

The traditional stator single-phase-to-ground fault protective schemes are hard to completely satisfy the requirement of selectivity for bus-connected Powerformers. In order to overcome the aforementioned problem, a new stator single-phase-to-ground fault protective scheme based on the hierarchical clustering algorithm is proposed in this paper. The direction and magnitude of zero-sequence current and leakage current are discussed and selected as feature vectors. Then, the data set are divided into two groups by hierarchical clustering algorithm, and cluster center of each group is calculated. Finally, the space relative distance among detected pattern and two cluster centers is calculated to discriminate the faulty Powerformer. The fundamental component and the third harmonic component of the data set are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 k Omega.

关键词： hierarchical clustering algorithm Powerformer protection stator single-phase-to-ground fault

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CNSD-Par: container network isolation scheme based on behavioral similarity

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JOURNAL OF SUPERCOMPUTING 2025年第8期81卷 1-25页

作者： He, Xinfeng Miao, Ruifeng Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Univ Hebei Key Lab High Trusted Informat Syst Baoding 071000 Peoples R China

With the rapid development of containerization technology, the workload of container safety isolation management gradually increased, but the efficiency was not high. At the same time, safety problems caused by insufficient container isolation capabilities became increasingly apparent. We proposed a container network isolation scheme based on behavior similarity in this context. First, the system call sequence was used as the benchmark for measuring the behavior similarity of containers, and an optimized hierarchical clustering algorithm (N-HCluster) was employed to divide containers with similar behavior into the same container network security domain. We then designed a container network isolation architecture based on security domains to enhance the container network's isolation and security. Finally, we evaluated this protocol's feasibility, accuracy, and performance through experiments. The experimental results showed that this scheme was effective and feasible and demonstrated good container network isolation capabilities.

关键词： hierarchical clustering algorithm System call sequence Container behavior similarity Container network isolation Container security

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Real-time Dynamic Division Based Online Estimation of Region Inertia in Power Systems

Real-time Dynamic Division Based Online Estimation of Region...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Wang, Yalun Shi, Lin Jiang, Yefeng Wang, Qi Southeast Univ Sch Elect Engn Nanjing Peoples R China State Grid Jiangsu Elect Power Co LTD Nanjing Peoples R China

ISBN: (纸本)9798350381849;9798350381832

The large-scale integration of inverter-based resources (IBRs), such as wind and solar power, into power systems has increased the complexity of system dynamic characteristics. The trend of decreasing overall system inertia and enhancing spatial distribution characteristics has become more significant. The demand for online estimation of inertia using phasor measurement units (PMUs) has become urgent. This paper proposes an online estimation method of region inertia based on real-time dynamic division. First, considering the frequency spatial distribution characteristics and the real-time requirements of the division operation, the system is divided into regions using the hierarchical clustering algorithm based on the dynamic response curve of the entire system bus frequency. Then, considering the coupling characteristics of the power-frequency relationship under the inertia angle, the frequency measurement points are selected based on region power data. Finally, to improve the accuracy and stability of the results, continuous inertia estimation is achieved through difference calculation and data processing. Simulation experiments in the IEEE 39-bus system have verified the effectiveness of the proposed method.

关键词： online estimation of inertia spatial distribution characteristics of frequency dynamic region division hierarchical clustering algorithm

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Fast Power Grid Partition for Voltage Control With Balanced-Depth-Based Community Detection algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第2期37卷 1612-1622页

作者： Yang, Yang Sun, Yichao Wang, Qi Liu, Fusuo Zhu, Ling Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China NARI Grp Corp Dept Elect Network Anal Nanjing 210097 Peoples R China

Network partition in complex power networks is essential for the var-voltage control. Traditional partition methods such as Ward method are applied in practical power networks, but they are unable to evaluate the quality of partition results. Moreover, they lack efficiency when dealing with large-scale networks. Since complex operation characteristics and topology have emerged in recent power systems, the power grid partition requires higher efficiency and quality. Therefore, this paper proposes a fast network partition method with a balanced-depth-based community detection algorithm. Its aim is to significantly improve the efficiency of partition while maintaining high quality of partition, with which the inter-zone coupling is minimized while the intra-zone coupling is maximized. In the meantime, a surrogate-optimization-based selection algorithm is proposed to select the zonal pilot bus, based on which the secondary voltage control method is used to evaluate the quality of partition. Results from four case studies conducted in various power networks with different sizes, as compared to other partition methods, validate the high efficiency and high quality of the proposed power grid partition approach.

关键词： Detection algorithms Power grids Partitioning algorithms Voltage measurement Power measurement Sensitivity Sun Community detection algorithm electrical distance hierarchical clustering algorithm voltage control power network partition modularity pilot bus

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clustering-Assisted 3D Beamforming for Throughput Maximization in mmWave Networks

Clustering-Assisted 3D Beamforming for Throughput Maximizati...

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IEEE International Conference on Communications (ICC)

作者： Tripathi, Shraddha Redhu, Surender Hegde, Rajesh M. Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Agder Kristiansand Norway

ISBN: (纸本)9781728194417

Beamforming schemes have been widely used to improve network throughput in SG mmWave networks. However, 3D beamforming schemes have hereto not been investigated in this context. In this work, a cluster-assisted 3D beamforming scheme is proposed to optimize the downtilt angle for network coverage and throughput maximization. User Equipment (UEs) are clustered based on inter-user and the inter-cluster distances. The interference is accounted from the adjacent clusters and thus frequency resources can be assigned to the non-adjacent clusters. Optimal downtilt angles are obtained for every cluster to maximize the throughput while considering the interference from adjacent clusters. 3D beam patterns are generated for every cluster based on optimal downtilt angles. Also, the Maximal Ratio Combining approach is utilized to compute the inter-cluster interference. Extensive simulations are performed to evaluate the performance of the proposed cluster-assisted 3D beamforming. It is observed that the overall throughput and SINR are significantly improved. The performance of the proposed beamforming scheme with optimal downtilt angles is compared with the conventional beamforming schemes with fixed downtilt angles. Moreover, the proposed scheme performs better in terms of Throughput and Maximal Coupling Loss compared to alternative schemes. Analytical validations motivate the utilisation of the proposed scheme for next-generation mmWave networks.

关键词： 3D beamforming hierarchical clustering algorithm SG mmWave Communication

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Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7763页

作者： Alsulami, Majid H. Shaqra Univ Appl Coll Shaqra 11961 Saudi Arabia

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model's performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively.

关键词： intrusion detection systems synthetic minority oversampling technique hierarchical clustering algorithm fire hawks

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Simulation Framework for Substation Siting Integrating Load, Land Use, Neighborhood, and Cost Analysis

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INTERNATIONAL JOURNAL OF GAMING AND COMPUTER-MEDIATED SIMULATIONS 2024年第1期16卷 1页

作者： Xiong, Jing Yang, Jinming Deng, Yueling Chen, Zixuan Yang, Jihaoyu Xu, Chen Qi, Yong Shaanxi Univ Sci & Technol Xian Peoples R China

This study presents a comprehensive framework for substation siting to address power demand and urbanization challenges. It integrates economic, social, and environmental dimensions to ensure reliable power supply systems. Using simulation techniques, the paper evaluates substation placement by analyzing load distribution, land use, neighborhood satisfaction, and construction costs. The Analytical Hierarchy Process (AHP) assesses load distribution, while a hierarchical clustering algorithm maximizes land use efficiency. Neighborhood satisfaction is measured using hierarchical analysis and fuzzy logic. Construction costs are optimized via a genetic algorithm. These simulations formulate a multi-criteria decision-making tool, proposing an optimized siting strategy balancing technical, socio-economic, and environmental considerations. The framework enhances the accuracy of substation siting studies and provides practical implementation guidance, offering new insights and refined methodologies for modern power system planning and development.

关键词： Substation Siting Simulation hierarchical Analysis hierarchical clustering algorithm Fuzzy Comprehensive Evaluation Genetic algorithm

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A novel grey object matrix incidence clustering model for panel data and its application

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SCIENTIA IRANICA 2021年第1期28卷 371-385页

作者： Liu, Y. Du, J. L. Li, J. Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China

In order to fully excavate the information contained in multi-index and small sample panel data, one takes decision objects as the research object. The development state matrix and the development speed matrix of the decision objects are defined by considering the cross-section information and time information of the decision objects, and then the distances among the objects over the indices are given. Based on grey incidence analysis, the absolute difference and relative difference between the measured value matrices are used to characterize and measure the close degree of the development state matrix and the development level matrix of the decision objects, so that the grey object matrix absolute incidence analysis model is established. Subsequently, according to the grey incidence degree between the objects, the objects can be clustered based on the hierarchical clustering algorithm. Finally, a clustering problem of regional patent research and development (R&D) efficiency is used to verify the validity and rationality of the proposed model. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Panel data Section information Time information Grey incidence analysis hierarchical clustering algorithm

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Determination of miscible CO2 flooding analogue projects with machine learning

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期209卷 109826-109826页

作者： Olukoga, Tolu A. Feng, Yin Univ Louisiana Lafayette 104 E Univ Ave Lafayette LA 70504 USA

Using analogue reservoirs for comparison and benchmarking is a comprehensive method for ensuring accurate measurement and gaining a better understanding of potential oil recovery enhancement prospects. The approach of leveraging information from analogue reservoirs is extended to CO2 EOR project management in this study. We present a novel application of machine learning clustering algorithms to the rapid identification of analogues for new projects without having to sift through massive amounts of data. We use machine learning clustering methods to group successfully executed miscible CO2 flooding projects into clusters of projects with similar fluid/reservoir characteristics and to identify analogues for new target projects. Porosity, permeability, oil gravity and viscosity, reservoir pressure and temperature, minimum miscibility pressure (MMP), and depth were all input parameters. Data from nearly 200 miscible CO2 EOR projects around the world were clustered using the Agglomerative hierarchical clustering algorithm (HCA), K-Means, and K-Median techniques. To reduce information redundancy in high-dimensional data, Principal Component Analysis was used as a pre-processing step. Three evaluation indices (the Davies Bouldin, Calinski Harabasz, and Silhouette Coefficient Scores) were used to compare the efficiency of the clustering algorithms and choose the best one for this dataset. A Principal Component - weighted Euclidean distance similarity metric was computed using three existing miscible CO2 flooding projects (Weyburn, Hansford Marmaton, and Paradis) as test cases to confirm the clustering results. The clustering analysis identified five different classes of miscible CO2 projects, each with its reservoir and fluid characteristics. Type 1 projects, in general, are those that are carried out primarily in shallow carbonate reservoirs at the lowest temperatures and pressures of any database project with typical porosity and permeability. More than 60% of Type 2 pro

关键词： Machine learning Miscible CO2 hierarchical clustering algorithm K-means Enhanced oil recovery: reservoir management

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Application of Agglomerative hierarchical clustering for clustering of Time Series Data 10

Application of Agglomerative Hierarchical Clustering for Clu...

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10th IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - Smart Grids - Key Enablers of a Green Power System

作者： Radovanovic, Ana Li, Junshi Milanovic, Jovica, V Milosavljevic, Nina Storchi, Riccardo Univ Manchester Dept Elect & Elect Engn Manchester Lancs England Univ Manchester Div Neurosci & Expt Psychol Manchester Lancs England

ISBN: (纸本)9781728171005

The advancements in technology have made it possible to automatically record and store large amount of data, which has resulted in a need for development and application of efficient data analysis techniques. Unsupervised data clustering methods have proven to be capable of extracting useful information from various types and sizes of datasets. This paper investigates the performance of the standard agglomerative hierarchical clustering algorithm using two time series datasets from electric power system and neuroscience area. The main steps in clustering procedure are presented in detail. Results show that the effectiveness of the clustering algorithm is affected to a large extent by the main characteristics of the clustering data and algorithm's parameters.

关键词： clustering electric power system hierarchical clustering algorithm neuroscience

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