2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)最新文献_第5页

Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution 基于贪婪自适应差分进化的电力系统谐波滤波器优化设计

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733571

M. Ortiz, Yigen Zenlander, N. Xiong, F. Herrera

{"title":"Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution","authors":"M. Ortiz, Yigen Zenlander, N. Xiong, F. Herrera","doi":"10.1109/ETFA.2016.7733571","DOIUrl":"https://doi.org/10.1109/ETFA.2016.7733571","url":null,"abstract":"Harmonic filtering has been widely applied to reduce harmonic distortion in power distribution systems. This paper investigates a new method of exploiting Differential Evolution (DE) to support the optimal design of harmonic filters. DE is a class of stochastic and population-based optimization algorithms that are expected to have stronger global ability than trajectory-based optimization techniques in locating the best component sizes for filters. However, the performance of DE is largely affected by its two control parameters: scaling factor and crossover rate, which are problem dependent. How to decide appropriate setting for these two parameters presents a practical difficulty in real applications. Greedy Adaptive Differential Evolution (GADE) algorithm is suggested in the paper as a more convenient and effective means to automatically optimize filter designs. GADE is attractive in that it does not require proper setting of the scaling factor and crossover rate prior to the running of the program. Instead it enables dynamic adjustment of the DE parameters during the course of search for performance improvement. The results of tests on several problem examples have demonstrated that the use of GADE leads to the discovery of better filter circuits facilitating less harmonic distortion than the basic DE method.","PeriodicalId":6483,"journal":{"name":"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79085119","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}

引用次数: 3

New types of faults detection and diagnosis using a mixed soft & hard clustering framework 基于软硬混合聚类框架的新型故障检测与诊断

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733738

Heng-Chao Yan, Junhong Zhou, C. Pang

{"title":"New types of faults detection and diagnosis using a mixed soft & hard clustering framework","authors":"Heng-Chao Yan, Junhong Zhou, C. Pang","doi":"10.1109/ETFA.2016.7733738","DOIUrl":"https://doi.org/10.1109/ETFA.2016.7733738","url":null,"abstract":"In general, one limitation in current diagnosis approaches is that they could only detect the existing types of faults, while not be able to detect new types of faults. It is difficult to know in advance all fault types and new types of faults may occur in industry. As such, effective detection and diagnosis on new types of faults are important. In this paper, a novel mixed soft&hard assignment clustering framework will be proposed to detect and diagnose new types of faults based on the feature signals. As a popular soft assignment strategy, Gaussian mixture model targets to diagnose existing types from training and detect new category. Next, the hard assignment strategy based on the Euclidean distance of K-means is used to further classify the fault details if the new category is detected. Effectiveness of the proposed framework is testified on a partial discharge measurement dataset of different high voltage electronic and power equipment in industry. It is able to achieve as good performance as benchmark approaches for conventional diagnosis without new fault category, while it also effectively detects and classifies new types of faults with average accuracy of 75.0%.","PeriodicalId":6483,"journal":{"name":"2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84588315","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}

引用次数: 9

Generation of safe operation sequences using iterative refinements and abstractions of timed automata 使用迭代改进和时间自动机抽象的安全操作序列的生成

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733728

T. Cochard, David Gouyon, J. Pétin

引用次数: 1

Moving IEC 61131-3 applications to a computing framework based on CLR Virtual Machine 将IEC 61131-3应用迁移到基于CLR虚拟机的计算框架

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733632

S. Cavalieri, G. Puglisi, Marco Stefano Scroppo, L. Galvagno

引用次数: 3

Requirements engineering and modelling for building automation systems 建筑自动化系统的需求工程和建模

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733675

Michelle Günther, André Scholz, P. Schmidt, A. Fay, Patrick Diekhake, Damian Eduardo Diaz Fuentes, U. Becker

引用次数: 6

From AutomationML to ROS: A model-driven approach for software engineering of industrial robotics using ontological reasoning 从AutomationML到ROS:使用本体推理的工业机器人软件工程的模型驱动方法

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733579

Yingbing Hua, Stefan Zander, Mirko Bordignon, B. Hein

引用次数: 33

Task planning for human robot interactive processes 人机交互过程的任务规划

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733523

Nils Wantia, Menno Esen, Andre Hengstebeck, F. Heinze, J. Roßmann, J. Deuse, B. Kuhlenkötter

引用次数: 6

Material comparison and design of low cost modular tactile surface sensors for industrial manipulators 工业机械臂低成本模块化触觉表面传感器的材料比较与设计

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733553

Jan Niklas Haus, Arne Muxfeldt, Daniel Kubus

引用次数: 6

Translating timing requirements of Embedded Software systems modeled in Simulink to a timing analysis model 将用Simulink建模的嵌入式软件系统的时序需求转化为时序分析模型

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733662

Padma Iyenghar, Arne Noyer, Joachim Engelhardt, E. Pulvermüller

引用次数: 4

Aggregating OPC UA servers for monitoring manufacturing systems and mobile work machines 聚合用于监控制造系统和移动工作机器的OPC UA服务器

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2016-09-01 DOI: 10.1109/ETFA.2016.7733739

I. Seilonen, Tomi Tuovinen, J. Elovaara, Ian Tuomi, T. Oksanen

引用次数: 26