{"title":"32nd IEEE International Symposium on Industrial Electronics, ISIE 2023, Helsinki, Finland, June 19-21, 2023","authors":"","doi":"10.1109/ISIE51358.2023","DOIUrl":"https://doi.org/10.1109/ISIE51358.2023","url":null,"abstract":"","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74011516","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 : 2022-01-01DOI: 10.1109/ISIE51582.2022.9831770
Julie Aubry, N. Steiner, S. Morando, N. Zerhouni, Fabian Van der Linden, D. Hissel
{"title":"Fuel Cell prognosis using particle filter: application to the automotive sector","authors":"Julie Aubry, N. Steiner, S. Morando, N. Zerhouni, Fabian Van der Linden, D. Hissel","doi":"10.1109/ISIE51582.2022.9831770","DOIUrl":"https://doi.org/10.1109/ISIE51582.2022.9831770","url":null,"abstract":"","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"1 1","pages":"360-365"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75041695","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 : 2020-01-01DOI: 10.1109/ISIE45063.2020.9152508
A. Ashoornezhad, H. Falaghi, M. Yousefi, A. Hajizadeh
{"title":"Bi-Level Distribution Network Planning Integrated with Energy Storage to PV-Connected Network","authors":"A. Ashoornezhad, H. Falaghi, M. Yousefi, A. Hajizadeh","doi":"10.1109/ISIE45063.2020.9152508","DOIUrl":"https://doi.org/10.1109/ISIE45063.2020.9152508","url":null,"abstract":"","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"29 1","pages":"1325-1329"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79163734","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 : 2019-01-01DOI: 10.1109/ISIE.2019.8781141
Yuezu Lü, Junjie Fu, Jialing Zhou, G. Wen, Xinghuo Yu
{"title":"Distributed adaptive anti-windup consensus tracking of networked systems with switching topologies","authors":"Yuezu Lü, Junjie Fu, Jialing Zhou, G. Wen, Xinghuo Yu","doi":"10.1109/ISIE.2019.8781141","DOIUrl":"https://doi.org/10.1109/ISIE.2019.8781141","url":null,"abstract":"","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"71 1","pages":"1793-1798"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80413618","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 : 2018-06-01DOI: 10.1109/ISIE.2018.8433778
Duy-Tang Hoang, Hee-Jun Kang
Bearing takes an important part in rotary machines. In industrial manufacturing systems, bearing fault diagnosis is a critical task which helps to reduce the cost for maintaining. This paper proposes a novel bearing fault diagnosis algorithm for rotary machine in which multiple sensors are installed using Deep Belief Network and Dempster-Shafer evidence theory. First, for signals from each sensor, a Deep Belief Network is used to extracted features. Each feature set generated by the corresponding Deep Belief Network is classifier by one softmax classifier. Finally, prediction results of all softmax classifiers are fused by DS evidence theory to generate the final prediction of bearing fault. Experiments are carried out with bearing data from Case Western Reserve University Data Central.
{"title":"Deep Belief Network and Dempster-Shafer Evidence Theory for Bearing Fault Diagnosis","authors":"Duy-Tang Hoang, Hee-Jun Kang","doi":"10.1109/ISIE.2018.8433778","DOIUrl":"https://doi.org/10.1109/ISIE.2018.8433778","url":null,"abstract":"Bearing takes an important part in rotary machines. In industrial manufacturing systems, bearing fault diagnosis is a critical task which helps to reduce the cost for maintaining. This paper proposes a novel bearing fault diagnosis algorithm for rotary machine in which multiple sensors are installed using Deep Belief Network and Dempster-Shafer evidence theory. First, for signals from each sensor, a Deep Belief Network is used to extracted features. Each feature set generated by the corresponding Deep Belief Network is classifier by one softmax classifier. Finally, prediction results of all softmax classifiers are fused by DS evidence theory to generate the final prediction of bearing fault. Experiments are carried out with bearing data from Case Western Reserve University Data Central.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"63 1","pages":"841-846"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75317987","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 : 2017-08-08DOI: 10.1109/ISIE.2017.8001490
Lucas Woltmann, Rune Dalmo, Raymond Kristiansen
In modern data analysis it is imperative to use well maintained data sources with curated content. This publication gives an approach for research areas, where there is no such central facility. The specific area used here is sea ice classification from images. The publication is split into two parts. The first part describes the integration of discontinuous sources for different aspects of data needed. The second part describes a simple approach for getting the sea ice concentration from images taken by an ice breaker. The classification is based on the above mentioned multi-source data. We can illustrate that it is possible to combine data from various sources to a central repository and use this repository to obtain the sea ice concentration from images without using any other inputs.
{"title":"Multi-source data collection for state-of-the-art data analysis from ground-proximate images in sea ice classification","authors":"Lucas Woltmann, Rune Dalmo, Raymond Kristiansen","doi":"10.1109/ISIE.2017.8001490","DOIUrl":"https://doi.org/10.1109/ISIE.2017.8001490","url":null,"abstract":"In modern data analysis it is imperative to use well maintained data sources with curated content. This publication gives an approach for research areas, where there is no such central facility. The specific area used here is sea ice classification from images. The publication is split into two parts. The first part describes the integration of discontinuous sources for different aspects of data needed. The second part describes a simple approach for getting the sea ice concentration from images taken by an ice breaker. The classification is based on the above mentioned multi-source data. We can illustrate that it is possible to combine data from various sources to a central repository and use this repository to obtain the sea ice concentration from images without using any other inputs.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"10 1","pages":"1626-1630"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81027937","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 : 2017-08-08DOI: 10.1109/ISIE.2017.8001390
N. Muller, H. Renaudineau, F. Flores-Bahamonde, S. Kouro, P. Wheeler
In most large-scale grid-tied photovoltaic (PV) plants, central inverter configurations are used, mainly due to higher converter efficiency and lower cost per kW. However, compared to other configurations, its Maximum Power Point Tracking (MPPT) efficiency is the lowest since it is the less distributed configuration. Under non-uniform conditions, as mismatch caused by aging and/or partial shading, several local maxima may arise in the PV curve, hence requiring additional actions to maximize the output power of the PV plant. Moreover, tighter grid codes have appeared, requiring for PV systems to limit power fluctuations. This paper presents an alternative to perform Global MPPT (GMPPT) while complying with stiffer grid code limitations. The proposed alternative adds an Energy Storage System (ESS) at inverter level, consisting of an ultracapacitor (UC) bank connected to the DC-link of a PV central inverter through interleaved DC-DC power converters. The proposed configuration is preliminary validated through simulations and tested under extreme conditions. The performance of the system is analyzed and compared to other existing solutions.
{"title":"Ultracapacitor storage enabled global MPPT for photovoltaic central inverters","authors":"N. Muller, H. Renaudineau, F. Flores-Bahamonde, S. Kouro, P. Wheeler","doi":"10.1109/ISIE.2017.8001390","DOIUrl":"https://doi.org/10.1109/ISIE.2017.8001390","url":null,"abstract":"In most large-scale grid-tied photovoltaic (PV) plants, central inverter configurations are used, mainly due to higher converter efficiency and lower cost per kW. However, compared to other configurations, its Maximum Power Point Tracking (MPPT) efficiency is the lowest since it is the less distributed configuration. Under non-uniform conditions, as mismatch caused by aging and/or partial shading, several local maxima may arise in the PV curve, hence requiring additional actions to maximize the output power of the PV plant. Moreover, tighter grid codes have appeared, requiring for PV systems to limit power fluctuations. This paper presents an alternative to perform Global MPPT (GMPPT) while complying with stiffer grid code limitations. The proposed alternative adds an Energy Storage System (ESS) at inverter level, consisting of an ultracapacitor (UC) bank connected to the DC-link of a PV central inverter through interleaved DC-DC power converters. The proposed configuration is preliminary validated through simulations and tested under extreme conditions. The performance of the system is analyzed and compared to other existing solutions.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"17 1","pages":"1046-1051"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84422394","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 : 2017-08-08DOI: 10.1109/ISIE.2017.8001373
J. Kitson, S. Williamson, P. Harper, Chris M. McMahon, Ges Rosenberg, Michael Tierney, Karen Bell
This paper presents a detailed method for creating an embedded Matlab model in Simulink for any solar photovoltaic panel starting with its datasheet values. It links extrinsic functions to the Simulink embedded model to provide fast and simple iterative solving of non-linear equations. It also provides a method sufficiently flexible to produce a model output based on panel current or voltage such that it can be cascaded with different Simulink elements.
{"title":"A photovoltaic panel modelling method for flexible implementation in Matlab/Simulink using datasheet quantities","authors":"J. Kitson, S. Williamson, P. Harper, Chris M. McMahon, Ges Rosenberg, Michael Tierney, Karen Bell","doi":"10.1109/ISIE.2017.8001373","DOIUrl":"https://doi.org/10.1109/ISIE.2017.8001373","url":null,"abstract":"This paper presents a detailed method for creating an embedded Matlab model in Simulink for any solar photovoltaic panel starting with its datasheet values. It links extrinsic functions to the Simulink embedded model to provide fast and simple iterative solving of non-linear equations. It also provides a method sufficiently flexible to produce a model output based on panel current or voltage such that it can be cascaded with different Simulink elements.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"28 1","pages":"946-951"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74527456","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 : 2017-08-08DOI: 10.1109/ISIE.2017.8001570
J. Chin, V. Callaghan, I. Lam
This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work.
{"title":"Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data","authors":"J. Chin, V. Callaghan, I. Lam","doi":"10.1109/ISIE.2017.8001570","DOIUrl":"https://doi.org/10.1109/ISIE.2017.8001570","url":null,"abstract":"This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"11 1","pages":"2050-2055"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80804708","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 : 2017-08-08DOI: 10.1109/ISIE.2017.8001375
Konstantina Panagiotou, C. Klumpner, M. Sumner
Since 2011, in the context of sustainable development, UK government has been encouraging individuals to work as groups, and now, more than 5,000 community led projects are sprouted across the country, since more than 50% of the UK citizens had expressed their interest to get involved with energy communities if they can potentially reduce their electricity cost. The aim of this study is to quantify the financial benefits for end-users and energy management authority when an energy community is settled up. By simulating possible operating scenarios and by observing and assuming a cost effective power flow/exchange between the individuals, the communal energy storage and the power grid, the finances of each scenario were quantified. Consequently, the electricity cost for the end-users and the incomes for the management authority were monitored and the most financially suitable community energy storage along with the PV penetration were identified.
{"title":"Being a member of an energy community: Assessing the financial benefits for end-users and management authority","authors":"Konstantina Panagiotou, C. Klumpner, M. Sumner","doi":"10.1109/ISIE.2017.8001375","DOIUrl":"https://doi.org/10.1109/ISIE.2017.8001375","url":null,"abstract":"Since 2011, in the context of sustainable development, UK government has been encouraging individuals to work as groups, and now, more than 5,000 community led projects are sprouted across the country, since more than 50% of the UK citizens had expressed their interest to get involved with energy communities if they can potentially reduce their electricity cost. The aim of this study is to quantify the financial benefits for end-users and energy management authority when an energy community is settled up. By simulating possible operating scenarios and by observing and assuming a cost effective power flow/exchange between the individuals, the communal energy storage and the power grid, the finances of each scenario were quantified. Consequently, the electricity cost for the end-users and the incomes for the management authority were monitored and the most financially suitable community energy storage along with the PV penetration were identified.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"127 1","pages":"957-963"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77391677","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：