2011 IEEE International Symposium on Industrial Electronics最新文献_第3页

Design of Inverting Buck-Boost DC-DC converter with Input-to-Output by-pass capacitor 带输入输出旁路电容的反相Buck-Boost DC-DC变换器设计

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984161

N. Femia, G. Di Capua, A. De Nardo

引用次数: 0

Multidimensional intelligent diagnosis system based on Support Vector Machine Classifier 基于支持向量机分类器的多维智能诊断系统

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984489

M. Delgado, A. García, J. Ortega, J. J. Cárdenas, L. Romeral

{"title":"Multidimensional intelligent diagnosis system based on Support Vector Machine Classifier","authors":"M. Delgado, A. García, J. Ortega, J. J. Cárdenas, L. Romeral","doi":"10.1109/ISIE.2011.5984489","DOIUrl":"https://doi.org/10.1109/ISIE.2011.5984489","url":null,"abstract":"Heeding the diagnostic requirements of electromechanical systems applied in automotive and aeronautical sectors, a multidimensional diagnostic system based on Support Vector Machine classifier is presented in this paper. In this context, different stationary and non-stationary speed and torque conditions are taken into account over an experimental actuator, in the same way, different single and combined failures scenarios are analyzed. In order to achieve a proper reliability in the diagnosis process, a multidimensional strategy is proposed: currents and vibrations from an electro-mechanical actuator are acquired. A great deal of features is calculated using statistical parameters from the acquired signals in time and frequency domain. Additionally, advanced time-frequency domain analysis techniques, such as Wavelet Packet Transform and Empirical Mode Decomposition, are used to achieve features which provide information in non-stationary conditions. The feature space dimensionality is analyzed by a feature reduction stage based on Partial Least Squares, which optimizes and reduces the feature set to be used for diagnosis proposes. The classification core is based on Support Vector Machine. Moreover, this work provides a performance comparison between the proposed classification algorithm and others such as Neural Network, k- Nearest Neighbor and Classification Trees. Experimental results are presented to demonstrate the feasibility and diagnostic capability of the proposed system.","PeriodicalId":162453,"journal":{"name":"2011 IEEE International Symposium on Industrial Electronics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536756","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: 10

Research of harmonic spectrum of currents in traction drive with PMSM 永磁同步电动机牵引传动电流谐波谱研究

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984244

R. Dolecek, J. Novak, O. Černý, Z. Nemec

引用次数: 3

Improvements in motion learning system using Force Reverse Presentation Control with variable force and time 利用可变力和时间的力反向呈现控制改进运动学习系统

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984497

Keishi Okuda, Yusuke Suzuki, K. Ohnishi

引用次数: 4

Sensorless control of PMSM high dynamic drive at low speed range 永磁同步电机低速高动态驱动的无传感器控制

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984247

K. Urbanski

引用次数: 14

Short-term load forecasting in air-conditioned non-residential Buildings 空调非住宅建筑短期负荷预测

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984356

Yoseba K. Penya, C. E. Borges, Denis Agote, I. Fernández

{"title":"Short-term load forecasting in air-conditioned non-residential Buildings","authors":"Yoseba K. Penya, C. E. Borges, Denis Agote, I. Fernández","doi":"10.1109/ISIE.2011.5984356","DOIUrl":"https://doi.org/10.1109/ISIE.2011.5984356","url":null,"abstract":"Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been classically object of vast research since energy load prediction is known to be non-linear. In a previous work, we focused on non-residential building STLF, an special case of STLF where weather has negligible influence on the load. Now we tackle more modern buildings in which the temperature does alter its energy consumption. This is, we address here fully-HVAC (Heating, Ventilating, and Air Conditioning) ones. Still, in this problem domain, the forecasting method selected must be simple, without tedious trial-and-error configuring or parametrising procedures, work with scarce (or any) training data and be able to predict an evolving demand curve. Following our preceding research, we have avoided the inherent non-linearity by using the work day schedule as day-type classifier. We have evaluated the most popular STLF systems in the literature, namely ARIMA (autoregressive integrated moving average) time series and Neural networks (NN), together with an Autoregressive Model (AR) time series and a Bayesian network (BN), concluding that the autoregressive time series outperforms its counterparts and suffices to fulfil the addressed requirements, even in a 6 day-ahead horizon.","PeriodicalId":162453,"journal":{"name":"2011 IEEE International Symposium on Industrial Electronics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127973855","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: 40

Equipments characterization methods for stability analysis of DC networks 直流电网稳定性分析的设备表征方法

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984168

M. Charrada, S. Girinon, H. Piquet, N. Roux

引用次数: 2

Network-control cosimulation framework for NCS NCS网络控制协同仿真框架

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984471

M.D.-C. Medina, E. Delgado, Antonio Barreiro Blas

引用次数: 0

FPGA implementation of a synchronous motor real-time emulator based on delta operator 基于增量算子的同步电机实时仿真器的FPGA实现

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984396

M. Dagbagi, L. Idkhajine, E. Monmasson, L. Charaabi, I. Slama-Belkhodja

引用次数: 10

Online fault detection of induction motors using frequency domain independent components analysis 基于频域独立分量分析的异步电动机在线故障检测

2011 IEEE International Symposium on Industrial Electronics Pub Date : 2011-06-27 DOI: 10.1109/ISIE.2011.5984490

Z. Wang, C. Chang

引用次数: 16