Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256210
Junjie Zhang, Yunpeng Wang, G. Lu, Wenmin Long
This study investigates the influence of the response time on the dynamics of a desired safety margin (DSM) car-following model, where the response time plays an important role in determining the qualitative dynamical of vehicles in car-following process. The stability criterion of the DSM car-following model is obtained by the linear system stability theory. Numerical simulations are in good agreement with the analytical results, which reveals that the response time would significantly influence the stability of traffic flow on a straight road. The numerical results also indicate that intelligent driving can help to reduce traffic congestion because the unstable flow can be stabilized by adopting the response time.
{"title":"Analysis string stability of a new car-following model considering response time","authors":"Junjie Zhang, Yunpeng Wang, G. Lu, Wenmin Long","doi":"10.1109/COASE.2017.8256210","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256210","url":null,"abstract":"This study investigates the influence of the response time on the dynamics of a desired safety margin (DSM) car-following model, where the response time plays an important role in determining the qualitative dynamical of vehicles in car-following process. The stability criterion of the DSM car-following model is obtained by the linear system stability theory. Numerical simulations are in good agreement with the analytical results, which reveals that the response time would significantly influence the stability of traffic flow on a straight road. The numerical results also indicate that intelligent driving can help to reduce traffic congestion because the unstable flow can be stabilized by adopting the response time.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256252
Damien Petit, I. Ramirez-Alpizar, K. Harada, N. Yamanobe, Weiwei Wan, K. Nagata
This paper presents a framework to extract the grasping, contact points and object parts motion from an assembly demonstration. With this framework the object parts are recognized and tracked using Augmented Reality (AR) markers. The data of the user's hand assembling the object are acquired with a motion capture device. The grasping and contact points are determined with the motion capture data, the models of the object parts and point cloud based algorithms. The functionality of the framework is demonstrated with an experiment where the user assembles two parts of a toy airplane. The grasping and contact points between the object parts are extracted and visualized. This framework aims at capturing the necessary data to reproduce the assembly motion on a dual-arm robot for future work.
{"title":"Extracting grasping, contact points and objects motion from assembly demonstration","authors":"Damien Petit, I. Ramirez-Alpizar, K. Harada, N. Yamanobe, Weiwei Wan, K. Nagata","doi":"10.1109/COASE.2017.8256252","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256252","url":null,"abstract":"This paper presents a framework to extract the grasping, contact points and object parts motion from an assembly demonstration. With this framework the object parts are recognized and tracked using Augmented Reality (AR) markers. The data of the user's hand assembling the object are acquired with a motion capture device. The grasping and contact points are determined with the motion capture data, the models of the object parts and point cloud based algorithms. The functionality of the framework is demonstrated with an experiment where the user assembles two parts of a toy airplane. The grasping and contact points between the object parts are extracted and visualized. This framework aims at capturing the necessary data to reproduce the assembly motion on a dual-arm robot for future work.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256206
Mingzhou Xie, L. Xia, Jun Xu
Storage system is the infrastructure of big data. Performance analysis of hard disk drive (HDD) plays a fundamental role to improve the efficiency of storage system. State-dependent M/G/1/K queue is used to model HDD, but it does not have a closed-form solution in the literature. In this paper, we use an M/G/1/K with state-dependent service time to formulate the dynamics of disk random access, where the service time depends on the queue length (batch size of requests determined by the queue length). A numerical computation approach is then proposed to compute the steady state distribution of this queuing model. By utilizing the block structure of transition probability matrix, we further develop an approach to speed up the computation, which can reduce the model complexity from O(K6) to O(K3). Finally, we apply this approach to a case study of hard disks of Western Digital Corp. It demonstrates the efficiency of our approach and gains useful insights for the optimization of storage system.
{"title":"State-dependent M/G/1/K queuing model for hard disk drives","authors":"Mingzhou Xie, L. Xia, Jun Xu","doi":"10.1109/COASE.2017.8256206","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256206","url":null,"abstract":"Storage system is the infrastructure of big data. Performance analysis of hard disk drive (HDD) plays a fundamental role to improve the efficiency of storage system. State-dependent M/G/1/K queue is used to model HDD, but it does not have a closed-form solution in the literature. In this paper, we use an M/G/1/K with state-dependent service time to formulate the dynamics of disk random access, where the service time depends on the queue length (batch size of requests determined by the queue length). A numerical computation approach is then proposed to compute the steady state distribution of this queuing model. By utilizing the block structure of transition probability matrix, we further develop an approach to speed up the computation, which can reduce the model complexity from O(K6) to O(K3). Finally, we apply this approach to a case study of hard disks of Western Digital Corp. It demonstrates the efficiency of our approach and gains useful insights for the optimization of storage system.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126468235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256079
Wei-Chian Tan, I. Chen, H. K. Tan
This paper presents an approach to analyse decision from existing framework on automated evaluation of piping layout or design for reason of non-compliance. On top of Histogram of Connectivity and linear Support Vector Machines based approach for prediction if a design is compliant or non-compliant, multiple binary classifiers are trained using linear Support Vector Machines to classify a non-compliant design further according to nature of non-compliance, in space of Histogram of Connectivity. Non-compliant designs in existing dataset of Regulation 12, Annex I, International Convention for the Prevention of Pollution from Ships are further divided into separate categories according to reason of non-compliance. For each sub-category of non-compliance, a binary classifier is trained using linear Support Vector Machines by taking all non-compliant designs belonging to current category as positive and all others as negative class. Existing dataset of 1318 non-compliant designs is divided into seven sub-categories. Developed method has demonstrated encouraging performance on existing dataset of International Convention for the Prevention of Pollution from Ships.
{"title":"Multiple binary classifiers to analyse decision of non-compliance: For automated evaluation of piping layout","authors":"Wei-Chian Tan, I. Chen, H. K. Tan","doi":"10.1109/COASE.2017.8256079","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256079","url":null,"abstract":"This paper presents an approach to analyse decision from existing framework on automated evaluation of piping layout or design for reason of non-compliance. On top of Histogram of Connectivity and linear Support Vector Machines based approach for prediction if a design is compliant or non-compliant, multiple binary classifiers are trained using linear Support Vector Machines to classify a non-compliant design further according to nature of non-compliance, in space of Histogram of Connectivity. Non-compliant designs in existing dataset of Regulation 12, Annex I, International Convention for the Prevention of Pollution from Ships are further divided into separate categories according to reason of non-compliance. For each sub-category of non-compliance, a binary classifier is trained using linear Support Vector Machines by taking all non-compliant designs belonging to current category as positive and all others as negative class. Existing dataset of 1318 non-compliant designs is divided into seven sub-categories. Developed method has demonstrated encouraging performance on existing dataset of International Convention for the Prevention of Pollution from Ships.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125650838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1080/00401706.2017.1317291
Xiaochen Xian, Andi Wang, Kaibo Liu
With the rapid development of sensor techniques, we often face the challenges of monitoring big data streams in modern quality control, which consist of massive series of real-time, continuously and sequentially ordered observations. For example, in manufacturing industries, hundreds or thousands of variables are observed during online production for quality insurance. Also, smart grid infrastructure needs to simultaneously monitor massive access points for intrusion and threat detection. As another example, an image sensing device continuously collects high-resolution images at high frequency for video surveillance and object movement tracking. Ideally, in those applications, it is preferable to detect assignable causes as early as possible, while maintaining a prespecified in-control Average Run Length (ARL).
{"title":"A nonparametric adaptive sampling strategy for online monitoring of big data streams","authors":"Xiaochen Xian, Andi Wang, Kaibo Liu","doi":"10.1080/00401706.2017.1317291","DOIUrl":"https://doi.org/10.1080/00401706.2017.1317291","url":null,"abstract":"With the rapid development of sensor techniques, we often face the challenges of monitoring big data streams in modern quality control, which consist of massive series of real-time, continuously and sequentially ordered observations. For example, in manufacturing industries, hundreds or thousands of variables are observed during online production for quality insurance. Also, smart grid infrastructure needs to simultaneously monitor massive access points for intrusion and threat detection. As another example, an image sensing device continuously collects high-resolution images at high frequency for video surveillance and object movement tracking. Ideally, in those applications, it is preferable to detect assignable causes as early as possible, while maintaining a prespecified in-control Average Run Length (ARL).","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127912194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256072
Jiajun Liu, P. Jiang, Jiewu Leng
Increasing production personalization demand requires manufacturing enterprises to gain higher flexibility and faster market response. Enterprises have to share their manufacturing resources and cooperate with each other to win out in the fierce market competition. Under the circumstances, Social Manufacturing (SocialM) mode is proposed. In this mode, Social Manufacturing Network (SMN) integrates distributed Socialized Manufacturing Resources (SMRs) to provide more precise and professional service for customers. As a decentralized network, SMN cannot ensure the cross-enterprise collaborations because there is no trusted third party as supervisor. In this paper, a blockchain-based Production Credit Mechanism (PCM) for manufacturing services is put forward to regulate the cross-enterprise collaborations among SMRs. The frame and concept of PCM are firstly given, followed by four key enabling technologies supporting the implementation of the mechanism. It is expected that the PCM proposed in this paper will provide a possible way to normalize and regulate cross-enterprise collaborations under social manufacturing mode.
{"title":"A framework of credit assurance mechanism for manufacturing services under social manufacturing context","authors":"Jiajun Liu, P. Jiang, Jiewu Leng","doi":"10.1109/COASE.2017.8256072","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256072","url":null,"abstract":"Increasing production personalization demand requires manufacturing enterprises to gain higher flexibility and faster market response. Enterprises have to share their manufacturing resources and cooperate with each other to win out in the fierce market competition. Under the circumstances, Social Manufacturing (SocialM) mode is proposed. In this mode, Social Manufacturing Network (SMN) integrates distributed Socialized Manufacturing Resources (SMRs) to provide more precise and professional service for customers. As a decentralized network, SMN cannot ensure the cross-enterprise collaborations because there is no trusted third party as supervisor. In this paper, a blockchain-based Production Credit Mechanism (PCM) for manufacturing services is put forward to regulate the cross-enterprise collaborations among SMRs. The frame and concept of PCM are firstly given, followed by four key enabling technologies supporting the implementation of the mechanism. It is expected that the PCM proposed in this paper will provide a possible way to normalize and regulate cross-enterprise collaborations under social manufacturing mode.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256263
Xingjian Lai, Xiaoning Jin, Xi Gu
The efficiency of energy consumption of an engineering system dynamically changes during the its operation when the operational and environmental conditions vary in time. Various methods have been developed to monitor the energy consumption rate and predict the consumption efficiency for a given operating condition. The main challenges to maintain the accuracy of modeling and prediction stem from the great diversity of operational and environmental inputs that affect the energy consumption rate dynamically, as well as the lack of a full understanding of the physical relationship between energy efficiency and operation parameters of the system. Operating condition is a key component in system modeling and state identification in many applications because not only the system parameters, but also the structure and complexity of a model might vary significantly during different operation modes. This paper investigates a novel method that integrates a physics-based hydrodynamic model and dynamic parameter learning and estimation, using energy consumption monitoring data and operating condition data, in purpose of improving the prediction accuracy of energy consumption. By leveraging the strengths of both the physics-based models and data-driven parameter learning methods, the proposed method is advantageous when the complex system physics is not perfectly known and the performance of system is affected by the environmental operating condition, while abundant monitoring data are available. We demonstrate the model on a ship propulsion system for fuel consumption prediction, which achieves higher prediction accuracy compared with models without operating condition adaption and tuning.
{"title":"An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions","authors":"Xingjian Lai, Xiaoning Jin, Xi Gu","doi":"10.1109/COASE.2017.8256263","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256263","url":null,"abstract":"The efficiency of energy consumption of an engineering system dynamically changes during the its operation when the operational and environmental conditions vary in time. Various methods have been developed to monitor the energy consumption rate and predict the consumption efficiency for a given operating condition. The main challenges to maintain the accuracy of modeling and prediction stem from the great diversity of operational and environmental inputs that affect the energy consumption rate dynamically, as well as the lack of a full understanding of the physical relationship between energy efficiency and operation parameters of the system. Operating condition is a key component in system modeling and state identification in many applications because not only the system parameters, but also the structure and complexity of a model might vary significantly during different operation modes. This paper investigates a novel method that integrates a physics-based hydrodynamic model and dynamic parameter learning and estimation, using energy consumption monitoring data and operating condition data, in purpose of improving the prediction accuracy of energy consumption. By leveraging the strengths of both the physics-based models and data-driven parameter learning methods, the proposed method is advantageous when the complex system physics is not perfectly known and the performance of system is affected by the environmental operating condition, while abundant monitoring data are available. We demonstrate the model on a ship propulsion system for fuel consumption prediction, which achieves higher prediction accuracy compared with models without operating condition adaption and tuning.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131796545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256188
Hao She, Qingyun Yu, Zhihong Min, Li Li
This paper proposes a collaborative scheduling strategy for twin fab in semiconductor manufacturing, which consists of the job scheduling strategy and the equipment scheduling strategy. The former considers the WIP balance of production lines, the load of continuous processing areas, equipment load, the difference of optimal equipment state value of each fab and the transportation time of job, etc. The latter considers the factors such as equipment load, the due date of jobs, the occupation time of a job on equipment and so on. The simulation results show that the proposed strategy can effectively improve the performance indexes of total throughput, average cycle time and on time delivery rate.
{"title":"A collaborative scheduling strategy for twin fab","authors":"Hao She, Qingyun Yu, Zhihong Min, Li Li","doi":"10.1109/COASE.2017.8256188","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256188","url":null,"abstract":"This paper proposes a collaborative scheduling strategy for twin fab in semiconductor manufacturing, which consists of the job scheduling strategy and the equipment scheduling strategy. The former considers the WIP balance of production lines, the load of continuous processing areas, equipment load, the difference of optimal equipment state value of each fab and the transportation time of job, etc. The latter considers the factors such as equipment load, the due date of jobs, the occupation time of a job on equipment and so on. The simulation results show that the proposed strategy can effectively improve the performance indexes of total throughput, average cycle time and on time delivery rate.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131800888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256270
Can Wang, Haipeng Xie, Z. Bie, Chao-Bo Yan, Yanling Lin
With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.
{"title":"Reliability evaluation of AC/DC hybrid power grid considering transient security constraints","authors":"Can Wang, Haipeng Xie, Z. Bie, Chao-Bo Yan, Yanling Lin","doi":"10.1109/COASE.2017.8256270","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256270","url":null,"abstract":"With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130842164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.1109/COASE.2017.8256140
Raffaele Carli, M. Dotoli
Efficient and effective monitoring represents the starting point for a reliable and secure smart grid. Given the increasing size and complexity of power networks and the pressing concerns on privacy and robustness, the development of intelligent and flexible distributed monitoring systems represents a crucial issue in both structuring and operating future grids. In this context, this paper presents a distributed optimization framework for use in smart grid monitoring. We propose a distributed algorithm based on ADMM (Alternating Direction Method of Multipliers) for use in large scale optimization problems in smart grid monitoring. The proposed solution is based upon a local-based optimization process, where a limited amount of information is exchanged only between neighboring nodes in a locally broadcast fashion. Applying the approach to two illustrating examples demonstrates it allows exploiting the scalability and efficiency of distributed ADMM for distributed smart grid monitoring.
{"title":"Using the distributed proximal alternating direction method of multipliers for smart grid monitoring","authors":"Raffaele Carli, M. Dotoli","doi":"10.1109/COASE.2017.8256140","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256140","url":null,"abstract":"Efficient and effective monitoring represents the starting point for a reliable and secure smart grid. Given the increasing size and complexity of power networks and the pressing concerns on privacy and robustness, the development of intelligent and flexible distributed monitoring systems represents a crucial issue in both structuring and operating future grids. In this context, this paper presents a distributed optimization framework for use in smart grid monitoring. We propose a distributed algorithm based on ADMM (Alternating Direction Method of Multipliers) for use in large scale optimization problems in smart grid monitoring. The proposed solution is based upon a local-based optimization process, where a limited amount of information is exchanged only between neighboring nodes in a locally broadcast fashion. Applying the approach to two illustrating examples demonstrates it allows exploiting the scalability and efficiency of distributed ADMM for distributed smart grid monitoring.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132916168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：