2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)最新文献_第3页

Source Location of Forced Oscillations based on Bus Frequency Measurements 基于母线频率测量的强迫振荡源定位

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576286

Álvaro Ortega, F. Milano

引用次数: 0

AMI Load Forecasting and Interval Forecasting Using a Hybrid Intelligent Method 基于混合智能方法的AMI负荷预测和区间预测

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576343

Chao-Ming Huang, Yann-Chang Huang, Shin-Ju Chen, Sung-Pei Yang, Kun-Yuan Huang

{"title":"AMI Load Forecasting and Interval Forecasting Using a Hybrid Intelligent Method","authors":"Chao-Ming Huang, Yann-Chang Huang, Shin-Ju Chen, Sung-Pei Yang, Kun-Yuan Huang","doi":"10.1109/ISIE45552.2021.9576343","DOIUrl":"https://doi.org/10.1109/ISIE45552.2021.9576343","url":null,"abstract":"The advanced metering infrastructure (AMI) loads are affected by changes in the temperature, humidity, and energy consumption of electrical equipment. Due to the high variability of AMI loads, the operating risk of a power grid caused by prediction error must be addressed. This paper utilizes discrete wavelet transform (DWT) to decompose load signals into low- and high-frequency components. Unnecessary high-frequency signals are eliminated by appropriately reconstructing signals that increase the accuracy of the forecasting model. Signal reconstruction is a combinatorial optimization process. This paper further integrates a grey wolf optimizer (GWO) and an autoregressive with multiple exogenous inputs (MIARX) model to find the optimal solution for signal reconstruction. When the day-ahead hourly forecasting of AMI loads is obtained, a quantile regression (QR) model is utilized to produce asymmetric prediction intervals. An index that considers both prediction interval coverage probability (PICP) and an evaluation resolution of criterion (ERC) is used to evaluate the performance of the obtained prediction intervals. To verify its feasibility, the proposed method is tested on smart AMI users in a green energy building located at Cheng Kung University in Taiwan.","PeriodicalId":365956,"journal":{"name":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","volume":"34 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127981034","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: 0

Mean of Maximum Method for Optical Scanning System 光学扫描系统最大值法的平均值

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576395

Wendy Garcia-Gonzalez, W. Flores-Fuentes, O. Sergiyenko, J. Rodríguez-Quiñonez, J. E. Miranda-Vega, A. Díaz-Ramírez, Alejandra Flores–Buruel

引用次数: 0

Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers 基于多元时间序列机器学习分类器的海上油井流动不稳定性检测

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576310

Bruno Guilherme Carvalho, Ricardo Emanuel Vaz Vargas, R. M. Salgado, C. J. Munaro, F. M. Varejão

引用次数: 6

Thermal Analysis of Air Cooling System for Electric Machines Using Lumped Parameter and Flow Resistance Network 基于集总参数和流阻网络的电机风冷系统热分析

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576489

A. F. Akawung, Y. Fujimoto

引用次数: 1

Performance Analysis of Machine Learning Classifiers for Pothole Road Anomaly Segmentation 坑洼道路异常分割的机器学习分类器性能分析

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576214

H. Bello-Salau, A. Onumanyi, R. F. Adebiyi, E. A. Adedokun, G. Hancke

{"title":"Performance Analysis of Machine Learning Classifiers for Pothole Road Anomaly Segmentation","authors":"H. Bello-Salau, A. Onumanyi, R. F. Adebiyi, E. A. Adedokun, G. Hancke","doi":"10.1109/ISIE45552.2021.9576214","DOIUrl":"https://doi.org/10.1109/ISIE45552.2021.9576214","url":null,"abstract":"Recently, machine learning (ML) classifiers are being widely deployed in many intelligent transportation systems towards improving the safety and comfort of passengers as well as to ease and enhance road navigation. However, the comparative performance analyses of different ML classifiers within the confines of road anomaly detection remain unexplored under some specific capture conditions such as bright light, dim light, and hazy image conditions. Consequently, this paper investigates the performance of six different state-of-the-art ML classification algorithms, viz: random forest, JRip, One-R,naive Bayesian, J48, and AdaBoost for segmenting pothole road anomalies under three different environmental conditions viz: bright, dim, and hazy light conditions. The results obtained suggest that either the J48 random forest or JRip classifiers are suitable for classifying pothole anomalies captured under broad day light (bright light) conditions with an average accuracy performance of 95%. On the other hand, the One-R classifier sufficed as more suitable for use under hazy image condition yielding an average accuracy of 73%, whereas the random forest algorithm yielded the best classification accuracy of 55%under dim light conditions. These results are helpful particularly towards determining the best ML classifiers for use towards developing robust artificial intelligence-based real-time algorithms for detecting and characterizing road anomalies effectively in autonomous vehicles.","PeriodicalId":365956,"journal":{"name":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134390880","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: 2

A Traffic Based Reference State of Charge Planning Method for Plug-in Hybrid Electric Vehicles 一种基于交通的插电式混合动力汽车充电参考状态规划方法

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576254

Jie Li, Xiaodong Wu, Sunan Hu

{"title":"A Traffic Based Reference State of Charge Planning Method for Plug-in Hybrid Electric Vehicles","authors":"Jie Li, Xiaodong Wu, Sunan Hu","doi":"10.1109/ISIE45552.2021.9576254","DOIUrl":"https://doi.org/10.1109/ISIE45552.2021.9576254","url":null,"abstract":"An appropriate state of charge (SOC) planning is crucial for improving economics of plug-in hybrid electric vehicles (PHEVs). This paper proposed a novel ensemble learning based reference SOC trajectory variation predictor. It can predict the SOC variation of different road segments based on rough traffic information. On this basis, a framework of multi-objective adaptive equivalent consumption minimum strategy (A-ECMS) is introduced. At the long-term global design layer, the proposed method plans the global reference SOC trajectory based on traffic information. In the real-time control layer, a closed-loop controller is used to update equivalent factor according to the error between current SOC and the reference SOC trajectory. Finally, the proposed method is analyzed and compared with the conventional linearly decreased reference SOC planning method. The simulation results prove that the proposed method improves the accuracy and stability of the planned reference SOC trajectory. Moreover, the total cost of the A-ECMS based on the proposed method is reduced by 2.1 % compared to the A-ECMS based on the linearly decreased planning method, which indicates that the reference SOC trajectory planned by the proposed method can effectively reduce the total cost of PHEV.","PeriodicalId":365956,"journal":{"name":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131026035","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: 1

Adapting Smart Home Voice Assistants to Users’ Privacy Needs using a Raspberry-Pi based and Self-Adapting System 使用基于树莓派的自适应系统使智能家庭语音助手适应用户的隐私需求

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576469

Stephan Dallmer-Zerbe, Jan Haase

{"title":"Adapting Smart Home Voice Assistants to Users’ Privacy Needs using a Raspberry-Pi based and Self-Adapting System","authors":"Stephan Dallmer-Zerbe, Jan Haase","doi":"10.1109/ISIE45552.2021.9576469","DOIUrl":"https://doi.org/10.1109/ISIE45552.2021.9576469","url":null,"abstract":"Through improvements of voice recognition smart voice assistants have become available in various forms. They offer users the possibility to control devices by natural voice interaction. To improve accuracy, user conversation data is automatically streamed to companies for machine learning. This is perceived as an act privacy invasion by many users. This is also an argument for potential buyers against acquiring a voice assistant device. Therefore, the central design element of smart home voice assistants should be local processing. For this purpose, the work was based on the Privacy by Design principles by Cavoukian. Based on the analysis of existing cloud and offline solutions a prototype was developed. Through complete offline modality features like self adapting commands and more natural forms of voice interaction were integrated. This was possible while protecting user privacy. Furthermore, fulfilment of the goals with focus on accuracy and quick response time was evaluated. This paper shows that it is possible to implement an offline voice assistant with similar response times and high accuracy. Self adaptation and expanded recognition show great potential for future improvements.","PeriodicalId":365956,"journal":{"name":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141862","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: 1

Sliding Mode Control of an Isolated Inverter Based on Active Clamped Flyback-Forward Converter 基于有源箝位反激正变换器的隔离型逆变器滑模控制

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576252

Farzaneh Bagheri, S. Bayhan, H. Komurcugil

引用次数: 0

Application of Hybrid Wavelet-SVM Algorithm to Detect Broken Rotor Bars in Induction Motors 小波-支持向量机混合算法在感应电机转子断条检测中的应用

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) Pub Date : 2021-06-20 DOI: 10.1109/ISIE45552.2021.9576330

Shermineh Ghasemi, Alireza Sadeghian

{"title":"Application of Hybrid Wavelet-SVM Algorithm to Detect Broken Rotor Bars in Induction Motors","authors":"Shermineh Ghasemi, Alireza Sadeghian","doi":"10.1109/ISIE45552.2021.9576330","DOIUrl":"https://doi.org/10.1109/ISIE45552.2021.9576330","url":null,"abstract":"Induction motors are essential components in the modern manufacturing settings and can function continuously for hours without minor issues due to their reliability and construction. However, any fault in these types of machinery may result in extended downtime, costly maintenance, and safety issues. Consequently, advanced diagnostic methods that prevent possible failure by recognizing the early signs of deficiency can increase motors' reliability. In the past few years, researchers conducted many experiments to identify Broken Rotor Bars by integrating Motor Current Signal Analysis and Artificial Intelligence solutions. However, the motor may be subjected to load fluctuation, then oscillation related signatures exhibit similar behavior that of broken bar which leads misleading signatures. This overlapping frequencies, induced by Broken Rotor Bars and the Low-frequency Torque Oscillations (LTOs), can generate False Positive alarms and frustrates the diagnostic system. In this work, we propose a diagnostic algorithm to distinguish and disentangle the Broken Rotor Bar's frequencies from misleading LTOs, and detects Broken Rotor Bars by applying a Hybrid Wavelet-Support Vector Machines algorithm. The results verify the efficiency and reliability of our proposed algorithm compared to the existing methods.","PeriodicalId":365956,"journal":{"name":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127822340","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: 0