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Research on Grounding Grid Corrosion Diagnosis Based on Genetic k-means algorithm

ENERGY SCIENCE & ENGINEERING 2025年

作者： Huang, Longsheng Xiao, Xianghui Huang, Mingxian Zhang, Zhenshan Song, Yunhao Guan, Luchang Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Foshan Univ Guangdong Prov Key Lab Ind Intelligent Inspect Tec Foshan Peoples R China

Grounding grid corrosion is one of the main reasons that affect the stable operation of electrical equipment in substations and endanger personal safety. After many years of operation, the grounding conductors will be eroded by soil. It may even cause major accidents and economic losses. Therefore, it is of great significance to diagnose the corrosion faults of the grounding grid and find out the corroded conductors. In this paper, the genetic k-means algorithm (GkA) is proposed to solve the mathematical model and judge the corrosion of grounding conductors. This algorithm combines GA's global searching ability and k-means's local searching ability, which improves the diagnosis result. In the simulation experiment, compared with the single GA's diagnosis, the diagnosis results of GkA were improved, and the number of misdiagnosed branches decreased by 66.7%. The simulation results show that the proposed algorithm takes less time to run, can eliminate the misdiagnosed branches commendably, and improve the accuracy of diagnosis. The proposed method provides a new idea to evaluate the corrosion degree of the grounding grid. The clustering algorithm is used to classify branches with similar corrosion degrees to achieve the purpose of corrosion diagnosis.

关键词： corrosion diagnosis electrical network theory genetic algorithm grounding grid k-means clustering algorithm

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An efficient method for extracting and clustering rock mass discontinuities from 3D point clouds

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ACTA GEOTECHNICA 2023年第7期18卷 3485-3503页

作者： Yi, Xiaoyu Feng, Wenkai Wang, Di Yang, Ronghao Hu, Yunpeng Zhou, Yongjian Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Earth Sci Chengdu 610059 Peoples R China

A rock mass discontinuity is a fundamental element of a rock mass structure that regulates its mechanical and hydrogeological properties and has significant engineering implications. In this research, we offer a method for extracting discontinuities and performing efficient clustering analysis based on 3D point cloud data for rock outcrops. First, the k-d tree approach is utilized to organize the point cloud data so that the normal vector and curvature can be calculated quickly. Discontinuities are then extracted using a multirule region growing algorithm, and the dip directions and dip angles of the discontinuities are calculated. Then, the improved farthest point sampling algorithm and the elbow method are used to optimize the k-means algorithm and finally automatically determine the main discontinuity set and average direction for the rock mass. The approach is tested on two real cases and compared to the methods of international researchers, and it is discovered that the method proposed in this work shows good accuracy, with an average deviation of less than 5 degrees from the dip direction and dip angle. Comparative tests with many point cloud data sets show that this new method can be used to extract discontinuities from massive-scale rock outcrop point cloud data and perform cluster analysis with high efficiency. The proposed method gives geologists and geological engineers a new tool for quickly and efficiently understanding rock outcrop discontinuities.

关键词： 3D point cloud Region growing algorithm Rock mass discontinuity k-means clustering algorithm

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Discrete phase CV-QkD over a hybrid free space - fibre channel with k-means clustering

Discrete phase CV-QKD over a hybrid free space - fibre chann...

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Conference on Quantum Technologies

作者： Vilashini, Saravana M. Prabhakara, Anil Indian Inst Technol Madras Chennai 600036 India

ISBN: (纸本)9781510673052;9781510673045

Quadrature phase shift keying continuous variable quantum key distribution (QPSk CV-QkD) can be used to encode bits of information on weak coherent states, enhanced with quantum scissors. In our experiment, we use discretely modulated QPSk CV-QkD across a 150 m long free-space optical (FSO) link over the two buildings with a four symbol constellation. Other CV-QkD deployments in the literature have demonstrated a Frame Error Rate of 20% with a 100 MHz repetition rate and a loss of 6 dB in the channel. Our setup has a QBER of 25% for a 35 dB channel loss. Our system has been tested for a link loss of upto 50 dB. The QPSk symbols generated at 100 MHz from a Zynq Ultrascale+ Field Programmable Gated Array and fed to an I/Q transmitter, which in turn modulated a 1550 nm laser. The FSO transmitter, connected to our lab 500 m away, consists of a collimator and a beam expander. The transmitted states were captured on the other side by a FSO receiver unit consisting of a collimator. The received signal was transported back to the lab via a fiber of length 1 km, which is then passed through a 90 degree hybrid coherent receiver, with a 1 GHz bandwidth. The captured quadratures are post processed with a k-means cluster algorithm allowing us to recover the bit values. Our experiments focus on understanding the change in BER in the presence of varying levels of attenuation over the hybrid FSO+fibre channel.

关键词： Quadrature phase shift keying Free Space Optics Continuous Variable Quantum key Distribution k-means clustering algorithm

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Mapping global human thermal comfort: Spatiotemporal variability of UTCI regions under climate change

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Zheng, Jing Du, Yaxing Zhu, Xian Dong, Wenjie Sun Yat Sen Univ Sch Atmospher Sci Key Lab Trop Atmosphere Ocean Syst Southern Marine Sci & Engn Guangdong Lab ZhuhaiMi Zhuhai 519082 Peoples R China

Human Thermal Comfort (HTC) is directly related to human health, quality of life, and sustainable urban planning. However, the global evaluation of outdoor thermal comfort via Universal Thermal Climate Index (UTCI) is still inadequate. This study systematically analyzed global HTC, via UTCI between 1951 and 2100 based on the Coupled Model Intercomparison Project Phase 6, covering historical trends and four Shared Socioeconomic Pathways (SSPs) future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The following key findings were presented: (1) The global average UTCI exhibits a latitudinal gradient distribution and a clear, consistent increase over time. (2) Ten characteristic thermal regions were identified by combining Empirical Orthogonal Function (EOF) and kmeans clustering. (3) EOF analysis revealed significant global warming and complex regional contrasts. (4) Low-latitude thermal regions expand poleward in all scenarios, with SSP5-8.5 causing major thermal property shifts and region redistributions. These results underscore a significant impact of changes inhuman thermal comfort under climate change, offering recommendations for interventions and policy guidance to address the challenges of climate change.

关键词： Human thermal comfort (HTC) Universal Thermal Climate Index (UTCI) k-means clustering algorithm Empirical orthogonal function (EOF) analysis

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Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

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Computers, Materials & Continua 2024年第6期79卷 4659-4681页

作者： Xin Xiong Jing Zhang Sanli Yi Chunwu Wang Ruixiang Liu Jianfeng He Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China College of Physics and Electronic Engineering Hanshan Normal UniversityChaozhou521041China Department of Clinical Psychology Second People’s Hospital of YunnanKunming650021China

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical *** methods such as Atomic Agglomerative Hierarchical clustering(AAHC),k-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature *** these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy k-means clustering *** innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG *** this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,*** findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task *** imagined movement,microstate A was significantly *** with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of ***,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual *** utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a *** results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EE

关键词： EEG microstate motor imagery k-means clustering algorithm gaus sian kernel function shannon entropy Lempel-Ziv complexity

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Multipath/NLOS Detection Based on k-means clustering for GNSS/INS Tightly Coupled System in Urban Areas

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MICROMACHINES 2022年第7期13卷 1128-1128页

作者： Wang, Hao Pan, Shuguo Gao, Wang Xia, Yan Ma, Chun Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Peoples R China Nanjing Tech Univ Sch Geomat Sci & Technol Nanjing 211800 Peoples R China

Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the k-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the k-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

关键词： GNSS INS tightly coupled system multipath NLOS detection k-means clustering algorithm urban areas

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Convex Optimization and PV Inverter Control Strategy-Based Research on Active Distribution Networks

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ENERGIES 2025年第7期18卷 1793-1793页

作者： Shi, Jiachuan Hu, Sining Fu, Rao Zhang, Quan Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Smart Bldg & Energy Efficiency Jinan 250101 Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN "key" nodes, a "key" nodes selection approach that used improved k-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users' interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver.

关键词： active distribution networks (ADN) convex optimization k-means clustering algorithm PV inverter second-order cone power flow (SOCP)

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Trusted Power Load Management Communication Framework Using Topology clustering and Blockchain Authentication 5

Trusted Power Load Management Communication Framework Using ...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Chen, ke Liu, Yinghui Zhuang, Zhong Su, Huiling Duan, Meimei China Elect Power Res Inst Beijing Key Lab Demand Side Multienergy Carriers Beijing Peoples R China State Grid Jiangsu Mkt Serv Ctr Metrol Ctr Nanjing Peoples R China

ISBN: (纸本)9798350363272;9798350363265

This paper proposes a novel communication framework for a new type of power load management system, utilizing a geographic clustering approach based on the k-means algorithm to optimize network topology. Each cluster of communication nodes forms a subnet, with the cluster center serving as a routing node. Leveraging blockchain principles, each subnet is structured as a circular blockchain, starting with the routing node as the genesis block. This node initializes communication with a timestamp and computes the hash of the block, passing it to the next node. Subsequent nodes add their data and timestamps to the previous hash, computing new hashes and continuing the chain. The last node in the subnet links back to the routing node, forming a closed loop, which enables the routing node to validate the integrity of the entire subnet's data. The same validation mechanism is extended between the routing nodes and the main station of the power load management system, ensuring robust and trustworthy communication across the network. This dual verification mechanism enhances the reliability and security of data transmission within the power load management system.

关键词： power load management systems network topology k-means clustering algorithm communication framework trustworthy communication

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Toward ecological environmental risk for spoil ground group management in mega projects

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第9期31卷 3706-3726页

作者： Li, Yulong Yao, Ziwen Wu, Jing Zeng, Saixing Wu, Guobin Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai Peoples R China

PurposeThe numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental ***/methodology/approachBased on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and k-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed *** results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive ***/valueThis paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

关键词： Spoil grounds Ecological environment Risk assessment Categorization Projection pursuit clustering method k-means clustering algorithm

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kBMP: kubernetes-Orchestrated IoT Online Battery Monitoring Platform

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 25358-25370页

作者： Chen, Qingyang He, Yinghui Yu, Guanding Xu, Chong Liu, Mingyang Li, Zhenming Zhejiang Univ Coll Informat & Elect Engn Hangzhou 310027 Zhejiang Peoples R China China Elect Power Res Inst Co Ltd Energy Storage & Novel Technol Elect Engn Dept Beijing 100192 Peoples R China

The rise in renewable energy has driven the widespread use of large-scale energy storage batteries, which makes the risk of overheating more threatening. To ensure battery safety, it is essential to build a monitoring system with a comprehensive evaluation of large quantities of batteries. However, existing battery management systems exhibit significant limitations in terms of monitoring scope, analytical precision, and transmission efficiency. As an applicable solution, cloud-edge technology is an advanced integrated method that provides low-latency data access, accurate analysis capabilities, and adjustable monitoring ranges. In this work, the kubernetes-orchestrated battery monitoring platform (kBMP), which integrates kubernetes and cloud-edge technology, is proposed to provide comprehensive battery management. Specifically, kubernetes is used to ensure low latency in data transmission and analysis, while the k-means clustering algorithm is applied to provide accurate thermal runaway (TR) warnings. To validate the performance of kBMP, four sets of real battery TR data are fed to test its accuracy and latency. The experimental findings reveal that kBMP is capable of providing battery TR warnings in advance within 30 min. Additionally, the platform concurrently decreases data transmission latency by up to 20% and reduces replica scaling latency by 50% compared to the platform without integrating kubernetes.

关键词： Batteries Monitoring Cloud computing Data visualization Servers Data analysis Data communication Battery monitoring platform cloud-edge technology k-means clustering algorithm kubernetes thermal runaway (TR) prediction

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