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Analysis, Detection, and Location of Open-Switch Submodule Failures in a Modular Multilevel Converter

IEEE TRANSACTIONS ON POWER DELIVERY 2016年第1期31卷 155-164页

作者： Yang, Qichen Qin, Jiangchao Saeedifard, Maryam Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

The modular multilevel converter (MMC) has become one of the most promising converter topologies for medium/high-power applications. Since the MMC is structured based upon stacking up a number of series-connected identical submodules (SMs), to improve its fault tolerance and reliability, SM failure detection and location is of significant importance. In this paper, the impact of open-circuit switch failures of the SMs on the operation of the MMC is analyzed. Based on the analysis under SM failure conditions, two SM failure detection and location methods are proposed, that is, a clustering algorithm (CA)-based method and a calculated capacitance (CC)-based method. In the proposed CA-based method, a pattern-recognition-based fault diagnosis approach, which employs the clustering algorithm to detect and locate the faulty SMs with open-switch failures through identifying the pattern of 2-D trajectories of the SM characteristic variables, is developed. The proposed CC-based method is based on the calculation and comparison of a physical component parameter, that is, the nominal SM capacitance, and is capable of failure detection, location, and classification within one stage. The performance of the proposed failure detection methods for an MMC system is evaluated based on time-domain simulation studies in the PSCAD/EMTDC software environment. The reported study results demonstrate the capabilities of the two proposed methods in detecting and locating any SM failure under various conditions accurately and efficiently.

关键词： Calculated capacitance clustering algorithm failure detection and location modular multilevel converter (MMC)

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Cluster-Based Quality-Aware Adaptive Data Compression for Streaming Data

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ACM JOURNAL OF DATA AND INFORMATION QUALITY 2017年第1期9卷 2-2页

作者： Basheer, Aseel Sha, Kewei Univ Baghdad Coll Sci Women Dept Comp Sci Baghdad Iraq Univ Houston Dept Comp Sci Houston TX 77058 USA Univ Houston Cyber Secur Inst Houston TX 77058 USA

Wireless sensor networks (WSNs) are widely applied in data collection applications. Energy efficiency is one of the most important design goals of WSNs. In this article, we examine the tradeoffs between the energy efficiency and the data quality. First, four attributes used to evaluate data quality are formally defined. Then, we propose a novel data compression algorithm, Quality-Aware Adaptive data Compression (QAAC), to reduce the amount of data communication to save energy. QAAC utilizes an adaptive clustering algorithm to build clusters from dataset;then a code for each cluster is generated and stored in a Huffman encoding tree. The encoding algorithm encodes the original dataset based on the Haffman encoding tree. An improvement algorithm is also designed to reduce the information loss when data are compressed. After the encoded data, the Huffman encoding tree and parameters used in the improvement algorithm have been received at the sink, a decompression algorithm is used to retrieve the approximation of the original dataset. The performance evaluation shows that QAAC is efficient and achieves a much higher compression ratio than lossy and lossless compression algorithms, while it has much smaller information loss than lossy compression algorithms.

关键词： Wireless sensor networks data quality adaptive clustering algorithm energy efficiency data compression

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Analysis of Power User Behavior Based on Big Data Technology: A Review

Analysis of Power User Behavior Based on Big Data Technology...

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IEEE Conference on Energy Internet and Energy System Integration

作者： Xingchen Gu Boyue Xiong State Grid Shanghai Shinan Electric Power Supply Company Shanghai China

Power industry is one of the important fields in the application of big data technology. Power big data is generated in every link of power production and contains rich commercial and social values. It is necessary to implement the analysis of electric power user behavior based on big data technology. This paper presents a comprehensive study on the analysis of power user behavior based on big data. The characteristics and application challenges of electric power big data are first introduced, followed by the extraction of power user side big data processing mode. Finally, this paper focuses on the main methods of data mining and analysis and discusses the clustering analysis algorithms to make better analysis of electric power user behavior.

关键词： Power big data Power user behavior analysis Big data mining and analysis clustering algorithm

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Degree of Link Dependence-Based LTE-V2V clustering and Alarm Information Forwarding

Degree of Link Dependence-Based LTE-V2V Clustering and Alarm...

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IEEE/CIC International Conference on Communication in China (ICCC)

作者： Chen, Qian Wu, Fan Leng, Supeng Qi, Sibo Zhang, Ke Univ Elect Sci & Technol China Chengdu Sch Commun & Informat Engn Chengdu Shi Sichuan Sheng Peoples R China

ISBN: (纸本)9781509021437

The LTE-based V2V communications is considered to be the key technology to improve the performance of the intelligent transportation system. In order to allocate LTE resources efficiently on the scenarios of large-scale LTE-V2V networks, a novel clustering algorithm based on the similarity of mobility is proposed in this paper. We present a new definition-degree of link dependence to measure the similarity of mobility and a novel maintenance scheme which can reduce the rebuilt cost. In the proposed algorithm, vehicles are grouped in one cluster only if they are with similarity mobility. The nodes selected as heads will not be reselected every cycle but keep their roles on the straight road, and only under some certain circumstance the heads list will be updated;but the ordinary nodes always continuously execute attaching-head process by themselves. In addition, under the topological structure of cluster, we devise an alarm information forwarding scheme which can distribute the alarm packets efficiently and reliably under half-duplex communication mode. The simulation results represent the performance of the algorithm in terms of the cluster head lifetime and the average reattaching-head times of ordinary nodes.

关键词： LTE-V2V clustering algorithm clustering maintenance alarm information forwarding

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Equivalent modelling of wind farm for small-signal stability analysis in weak power system

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The Journal of Engineering 2017年第13期2017卷 1388-1393页

作者： Xiuqiang He Hua Geng Geng Yang Xin Zou Yanan Li Department of Automation Tsinghua University Beijing People's Republic of China State Power Economic Research Institute Beijing People's Republic of China

This study proposes a novel equivalent modelling method of wind farm (WF), which can be used for small-signal stability analysis and researches on damping control of low-frequency oscillation. First, a complete WF model described with a set of differential algebraic equations is established, then its linearised model is obtained at one of the steady-state operating points. Next, the eigenanalysis and the modal participation factor (MPF) analysis methods are adopted to evaluate the low-frequency oscillation modes and the corresponding modal participation in each wind turbine generator (WTG). A feature vector for each WTG, describing its oscillation characteristics, is extracted based on the MPFs. Afterwards, all WTGs are clustered into some groups via a clustering algorithm, then the WTGs in the same groups are aggregated into a single WTG, using the weighted summation method. Furthermore, the criterion for the validity of the equivalent model is studied. The proposed modelling and validation methods are verified by simulations.

关键词： wind power plants power system stability damping frequency control differential algebraic equations linearisation techniques eigenvalues and eigenfunctions modal analysis wind turbines turbogenerators pattern clustering power generation control damping control equivalent modelling method weak power system small-signal stability analysis wind farm weighted summation method clustering algorithm WTG wind turbine generator low-frequency oscillation mode MPF analysis method modal participation factor analysis method eigenanalysis steady-state operating point linearised model differential algebraic equation WF model

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A Novel Method for Multi-Targets ISAR Imaging Based on Particle Swarm Optimization and Modified CLEAN Technique

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IEEE SENSORS JOURNAL 2016年第1期16卷 97-108页

作者： Liu, Lei Zhou, Feng Tao, Mingliang Zhang, Zijing Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

In multi-targets inverse synthetic aperture radar (ISAR) imaging, range profiles of different target are coupled together, resulting in the failure of traditional mono-target imaging method. A novel multi-targets ISAR imaging method based on particle swarm optimization (PSO) and modified CLEAN technique is proposed in this paper. First, multi-targets are modeled as several separated group-targets in which translational motion of each target is analogous. And then, translational motion of each group-target is modeled as a polynomial, and the polynomial coefficient vector is estimated via the PSO-based iteration. Furthermore, a well-focused image of the group-target can be obtained and extracted via the proposed modified CLEAN technique. Meanwhile, each target can be segmented and extracted based on clustering number estimation and K-means clustering algorithm. Finally, better focused image of each target would be obtained through further traditional mono-target imaging processing. Experimental results verify the validity of the proposed method.

关键词： Inverse synthetic aperture radar (ISAR) multi-targets particle swarm optimization (PSO) modified CLEAN clustering algorithm

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Improving Data Credibility for Mobile Crowdsensing with clustering and Logical Reasoning

Improving Data Credibility for Mobile Crowdsensing with Clus...

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2nd International Conference on Cloud Computing and Security (ICCCS)

作者： Zhou, Tongqing Cai, Zhiping Chen, Yueyue Xu, Ming Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783319486741;9783319486734

Mobile crowdsensing is a new paradigm that tries to collect a vast amount of data with the rich set of sensors on pervasive mobile devices. However, the unpredictable intention and various capabilities of device owners expose the application to potential dishonest and malicious contributions, bringing forth the important issues of data credibility assurance. Existed works generally attempt to increase data confidence level with the guide of reputation, which is very likely to be unavailable in reality. In this work, we propose CLOR, a general scheme to ensure data credibility for typical mobile crowdsensing application without requiring reputation knowledge. By integrating data clustering with logical reasoning, CLOR is able to formally separate false and normal data, make credibility assessment, and filter out the false ingredient. Simulation results show that improved data credibility can be achieved effectively with our scheme.

关键词： Mobile crowdsensing Data credibility clustering algorithm Logical reasoning

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A new methodology to cluster derivative product modules: an application

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第23期54卷 7091-7099页

作者： Aydin, Merve Ulutas, Berna Haktanirlar Eskisehir Osmangazi Univ Dept Ind Engn Eskisehir Turkey Eskisehir Osmangazi Univ Eskisehir Turkey

Companies are trying several ways to offer competitive and highly differentiated products. The goal for the product platform is to share elements for common functions and to differentiate each product in the family by satisfying different requirements as much as possible. This study focuses on the product variety and short product life cycles that result from the increase and diversification in consumer needs and expectations. Proposed methodology aims to maximise the use of common product modules by considering platform-based derivative products and modular product design approaches to minimise the planning complexity in supply chain, manufacturing and service for derivative products. Functional and technical features of the products are determined in the first step. Then, design structure matrix is formed. After defining product components, similarity matrix for derivative products is formed. A clustering algorithm based on Clonal Selection is used to generate critical product modules. Data from a home appliance manufacturer are used to assess three versions of a product by also considering the production process. The grouping enabled to shorten the release time of a new derivative product to the market.

关键词： clonal selection algorithm clustering algorithm commonality index design structure matrix modular design

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Effect of Heterogeneous Nodes Location on the Performance of clustering algorithms for Wireless Sensor Networks 3

Effect of Heterogeneous Nodes Location on the Performance of...

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3rd International Conference on Recent Trends in Computing (ICRTC)

作者： Pal, Vipin Yogita Singh, Girdhari Yadav, R. P. Vivekananda Global Univ Jaipur 302012 Rajasthan India Panjab Univ UIET Chandigarh 110061 India Malaviya Natl Inst Technol Jaipur 302017 Rajasthan India

Improved network lifetime without much increase in the cost contributes popularity to heterogeneous wireless sensor networks. clustering algorithms designed to utilize advantage of heterogeneity of nodes allow these nodes to be cluster head more times than normal nodes to have load balanced network. Cluster head selection is pivotal for the performance of clustering algorithms as cluster quality in terms of communication distance depends upon the location of selected head in the cluster. Work of this paper analyzes the effect of location of heterogeneous nodes on the performance of clustering algorithms. Worst case, average ( random) case and best case for location of heterogeneous nodes are considered for analyzing the effect on the performance of clustering algorithms. (C) 2015 The Authrs. Published by Elsevier B.V.

关键词： clustering algorithm Heterogeneous Network Lifetime Wireless Sensor Network

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An image denoising algorithm based on clustering and median filtering 6

An image denoising algorithm based on clustering and median ...

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6th International Conference on Graphic and Image Processing (ICGIP)

作者： Wang YuLing Ming, Li Li, Li Nanjing Univ Aeronaut & Astronaut Dept Automat Engn Nanjing 210016 Jiangsu Peoples R China Nanchang HangKong Univ Minist Educ Key Lab Nondestruct Test Nanchang 330063 Peoples R China E China Inst Technol Nanchang 330013 Peoples R China

ISBN: (纸本)9781628415582

It is proposed of an improved median de-noising method, namely an image de-noising algorithm based on clustering and median filtering. The algorithm is a kind of image fast de-noising method based on the clustering idea, the singular point points are isolated from the image and then clustering. It is advantage to better protect the details of an image and to substantially reduce calculation. Compared with traditional median filter, mean filter and wiener filter, our approach is more adaptive and receives better results. While for images that have complex details such as texture images, the results of experiment show that the proposed algorithm works less well in the de-noising effect comparatively.

关键词： image de-noising clustering algorithm median filter PSNR

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