The increasing adoption of heat pumps presents new challenges for power grids, including the potential overloading of transformers and cables. To address this issue, in this work, a model predictive control for a low-temperature district heating network is proposed to prevent the overloading of transformers and cables. A comprehensive control strategy that considers various factors influencing the flexibility of heat pumps is introduced. The considered factors include integrating distributed energy resources (DER) such as a photovoltaic system, a battery energy storage system, and flexible indoor temperatures. The control mechanism is validated through a hardware-in-the-loop cosimulation setup, ensuring practical applicability and operational feasibility. The results indicate that with the proposed control, the power consumption of the heat pumps is reduced to alleviate overloading issues. To meet the power consumption constraints imposed on the heat pumps the gas usage by the heating grid would increase up to 506% of the level in the case without power constraints. However, by integrating DERs, along with leveraging the flexibility in indoor temperature, this additional gas usage is limited to 135%.
越来越多地采用热泵给电网带来了新的挑战，包括变压器和电缆可能过载。为解决这一问题，本研究提出了一种低温区域供热网络的模型预测控制方法，以防止变压器和电缆过载。文中介绍了一种综合控制策略，该策略考虑了影响热泵灵活性的各种因素。考虑的因素包括整合分布式能源资源（DER），如光伏系统、电池储能系统和灵活的室内温度。通过硬件在环协同仿真设置对控制机制进行了验证，以确保实际适用性和操作可行性。结果表明，采用所提出的控制方法，热泵的功耗得以降低，从而缓解了过载问题。为了满足对热泵施加的功率消耗限制，供热电网的天然气用量将增加到无功率限制情况下的 506%。然而，通过整合 DER 以及利用室内温度的灵活性，额外的天然气用量被限制在 135%。
{"title":"Short-Term Control of Heat Pumps to Support Power Grid Operation","authors":"Diran Liu;Daniele Carta;André Xhonneux;Dirk Müller;Andrea Benigni","doi":"10.1109/OJIES.2024.3486560","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3486560","url":null,"abstract":"The increasing adoption of heat pumps presents new challenges for power grids, including the potential overloading of transformers and cables. To address this issue, in this work, a model predictive control for a low-temperature district heating network is proposed to prevent the overloading of transformers and cables. A comprehensive control strategy that considers various factors influencing the flexibility of heat pumps is introduced. The considered factors include integrating distributed energy resources (DER) such as a photovoltaic system, a battery energy storage system, and flexible indoor temperatures. The control mechanism is validated through a hardware-in-the-loop cosimulation setup, ensuring practical applicability and operational feasibility. The results indicate that with the proposed control, the power consumption of the heat pumps is reduced to alleviate overloading issues. To meet the power consumption constraints imposed on the heat pumps the gas usage by the heating grid would increase up to 506% of the level in the case without power constraints. However, by integrating DERs, along with leveraging the flexibility in indoor temperature, this additional gas usage is limited to 135%.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1221-1238"},"PeriodicalIF":5.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/OJIES.2024.3486353
Alvaro Carreno;Mariusz Malinowski;Marcelo A. Perez;Jingyu Ding
The hybrid distribution transformer (HDT) has been proposed as a solution to cope with the low short-circuit capability of solid-state transformers. Among the available HDT configurations, the one that connects a series/parallel converter on the primary/secondary side can be highlighted. This configuration improves the voltage and current waveforms on the transformer and regulates the voltage supplied to the ac microgrid. Nonetheless, this HDT suffers from a circulating active power flow (CAPF), affecting its efficiency. Moreover, during the unbalanced operation of the HDT, an additional CAPF component exists. Depending on the grid and load conditions and whether the parallel converter compensates for the load unbalances, the CAPF can either increase or decrease. Although the CAPF can be eliminated by employing the dc port of the HDT, it is not always possible to extract energy from it. This work contributes with the analysis of the operation of an HDT under an unbalanced grid voltage and load, along with an extended CAPF model that considers the losses of the HDT. The effect of the unbalanced components on the CAPF is analyzed, and the conditions in which the CAPF is minimized are obtained. Nonetheless, in most scenarios, a minimum CAPF does not coincide with the maximum efficiency of the HDT. Therefore, the conditions for achieving maximum efficiency are also determined. A simpler suboptimal condition is obtained due to the complexity of requiring precise parameters and operating conditions of the HDT. Moreover, the suboptimal condition allows for improving the power quality of the HDT. Therefore, a certain amount of CAPF is desired to operate the HDT properly.
{"title":"Effects of Grid Voltage and Load Unbalances on the Efficiency of a Hybrid Distribution Transformer","authors":"Alvaro Carreno;Mariusz Malinowski;Marcelo A. Perez;Jingyu Ding","doi":"10.1109/OJIES.2024.3486353","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3486353","url":null,"abstract":"The hybrid distribution transformer (HDT) has been proposed as a solution to cope with the low short-circuit capability of solid-state transformers. Among the available HDT configurations, the one that connects a series/parallel converter on the primary/secondary side can be highlighted. This configuration improves the voltage and current waveforms on the transformer and regulates the voltage supplied to the ac microgrid. Nonetheless, this HDT suffers from a circulating active power flow (CAPF), affecting its efficiency. Moreover, during the unbalanced operation of the HDT, an additional CAPF component exists. Depending on the grid and load conditions and whether the parallel converter compensates for the load unbalances, the CAPF can either increase or decrease. Although the CAPF can be eliminated by employing the dc port of the HDT, it is not always possible to extract energy from it. This work contributes with the analysis of the operation of an HDT under an unbalanced grid voltage and load, along with an extended CAPF model that considers the losses of the HDT. The effect of the unbalanced components on the CAPF is analyzed, and the conditions in which the CAPF is minimized are obtained. Nonetheless, in most scenarios, a minimum CAPF does not coincide with the maximum efficiency of the HDT. Therefore, the conditions for achieving maximum efficiency are also determined. A simpler suboptimal condition is obtained due to the complexity of requiring precise parameters and operating conditions of the HDT. Moreover, the suboptimal condition allows for improving the power quality of the HDT. Therefore, a certain amount of CAPF is desired to operate the HDT properly.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1206-1220"},"PeriodicalIF":5.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1109/OJIES.2024.3483293
Irati Ibanez-Hidalgo;Rodrigo H. Cuzmar;Alain Sanchez-Ruiz;Angel Perez-Basante;Asier Zubizarreta;Salvador Ceballos;Ricardo P. Aguilera
Low-switching frequency modulation techniques, such as selective harmonic control-pulsewidth modulation (SHC-PWM), have been recently proposed for high-power medium-voltage active power filter (APF) application. Compared to high-switching frequency modulation techniques, these methods reduce the switching losses and avoid derrating the current. This results in enhanced power density and efficiency, and facilitates a reduction in costs. However, the low-switching frequency tends to worsen the closed-loop dynamic response and system stability if countermeasures are not taken during the design process of the closed-loop controllers. Moreover, the digital filter used to obtain the harmonic components of the measured signals introduces a delay that can affect the stability and performance of the closed-loop control. This work presents different methods to improve the dynamic response of traditional proportional-integral based closed-loop controllers, which are applied along with SHC-PWM for high-power medium-voltage APFs. A current predictor that substitutes the traditional cross-coupling terms and a Smith predictor are proposed to compensate the delay introduced by the digital filters. In addition, different digital filter implementations are analyzed and compared in terms of dynamic and stationary response with the aim of improving the harmonic estimation from the measured signals. Experimental results for a 3-level NPC converter are provided to verify the effectiveness of the control.
{"title":"Enhanced PI Control Based SHC-PWM Strategy for Active Power Filters","authors":"Irati Ibanez-Hidalgo;Rodrigo H. Cuzmar;Alain Sanchez-Ruiz;Angel Perez-Basante;Asier Zubizarreta;Salvador Ceballos;Ricardo P. Aguilera","doi":"10.1109/OJIES.2024.3483293","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3483293","url":null,"abstract":"Low-switching frequency modulation techniques, such as selective harmonic control-pulsewidth modulation (SHC-PWM), have been recently proposed for high-power medium-voltage active power filter (APF) application. Compared to high-switching frequency modulation techniques, these methods reduce the switching losses and avoid derrating the current. This results in enhanced power density and efficiency, and facilitates a reduction in costs. However, the low-switching frequency tends to worsen the closed-loop dynamic response and system stability if countermeasures are not taken during the design process of the closed-loop controllers. Moreover, the digital filter used to obtain the harmonic components of the measured signals introduces a delay that can affect the stability and performance of the closed-loop control. This work presents different methods to improve the dynamic response of traditional proportional-integral based closed-loop controllers, which are applied along with SHC-PWM for high-power medium-voltage APFs. A current predictor that substitutes the traditional cross-coupling terms and a Smith predictor are proposed to compensate the delay introduced by the digital filters. In addition, different digital filter implementations are analyzed and compared in terms of dynamic and stationary response with the aim of improving the harmonic estimation from the measured signals. Experimental results for a 3-level NPC converter are provided to verify the effectiveness of the control.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1174-1189"},"PeriodicalIF":5.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.
{"title":"A Detailed Study on Algorithms for Predictive Maintenance in Smart Manufacturing: Chip Form Classification Using Edge Machine Learning","authors":"Alessia Lazzaro;Doriana Marilena D'Addona;Massimo Merenda","doi":"10.1109/OJIES.2024.3484006","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3484006","url":null,"abstract":"Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1190-1205"},"PeriodicalIF":5.2,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1109/OJIES.2024.3483552
Ander González-Docasal;Jon Alonso;Jon Olaizola;Mikel Mendicute;María Patricia Franco;Arantza del Pozo;Daniel Aguinaga;Aitor Álvarez;Eduardo Lleida
This work introduces the design and assessment of a voice-controlled elevator system aimed at facilitating touchless interaction between users and hardware, thereby minimizing contact and improving accessibility for individuals with disabilities. The research distinguishes three distinct deployment scenarios—on cloud, on edge, and embedded—with the ultimate goal of integrating the entire system into a low-resource environment on a custom carrier board. An objective evaluation measured acoustic conditions rigorously using a dataset of 2900 audio files recorded inside a laboratory elevator cabin featuring two internal coatings, five audio input devices, and under four distinct noise conditions. The study evaluated the performance of two Automatic Speech Recognition systems: Google's Speech-to-Text API and a Kaldi model adapted for this task, deployed using Vosk. In addition, latency times for these transcribers and two communication protocols were measured to enhance efficiency. Finally, two subjective evaluations on clean and noisy conditions were conducted simulating a real world scenario. The results, yielding 84.7 and 77.2 points, respectively, in a System Usability Scale questionnaire, affirm the reliability of the presented prototype for industrial deployment.
{"title":"Design and Evaluation of a Voice-Controlled Elevator System to Improve the Safety and Accessibility","authors":"Ander González-Docasal;Jon Alonso;Jon Olaizola;Mikel Mendicute;María Patricia Franco;Arantza del Pozo;Daniel Aguinaga;Aitor Álvarez;Eduardo Lleida","doi":"10.1109/OJIES.2024.3483552","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3483552","url":null,"abstract":"This work introduces the design and assessment of a voice-controlled elevator system aimed at facilitating touchless interaction between users and hardware, thereby minimizing contact and improving accessibility for individuals with disabilities. The research distinguishes three distinct deployment scenarios—on cloud, on edge, and embedded—with the ultimate goal of integrating the entire system into a low-resource environment on a custom carrier board. An objective evaluation measured acoustic conditions rigorously using a dataset of 2900 audio files recorded inside a laboratory elevator cabin featuring two internal coatings, five audio input devices, and under four distinct noise conditions. The study evaluated the performance of two Automatic Speech Recognition systems: Google's Speech-to-Text API and a Kaldi model adapted for this task, deployed using Vosk. In addition, latency times for these transcribers and two communication protocols were measured to enhance efficiency. Finally, two subjective evaluations on clean and noisy conditions were conducted simulating a real world scenario. The results, yielding 84.7 and 77.2 points, respectively, in a System Usability Scale questionnaire, affirm the reliability of the presented prototype for industrial deployment.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1239-1250"},"PeriodicalIF":5.2,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10721366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In robotic assembly of flexible flat cables (FFCs), a unique challenge is the inherent difficulty in manipulating such flexible objects compared to their rigid counterparts and the precise estimation of the cable pose. This work proposes a framework that combines object pose estimation using computer-aided design (CAD) models and multiview fusion to perform precise FFC assembly. Our key insight is that a multiview fusion combined with pretrained 6-D pose estimation models offers a more flexible and precise object pose estimation. In a series of experiments involving FFC insertion tasks requiring assembly tolerances down to 0.1 mm, our approach achieves an insertion success rate of 399 out of 400 total attempts. Furthermore, the assembly tasks include the releasing and securing of FFCs from cable connectors, where the system is successful in 200 out of 200 trials. We have also demonstrated the generalization capability of the methodology by successfully completing insertion tasks for common electronic cables like DisplayPort and USB-A, achieving 199 successes in 200 trials. The results not only validate the feasibility of the proposed approach, but also demonstrate its robustness for real-world industrial applications.
{"title":"On Robust Assembly of Flexible Flat Cables Combining CAD and Image Based Multiview Pose Estimation and a Multimodal Robotic Gripper","authors":"Junbang Liang;Joao Buzzatto;Bryan Busby;Haodan Jiang;Saori Matsunaga;Rintaro Haraguchi;Toshisada Mariyama;Bruce A. MacDonald;Minas Liarokapis","doi":"10.1109/OJIES.2024.3467171","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3467171","url":null,"abstract":"In robotic assembly of flexible flat cables (FFCs), a unique challenge is the inherent difficulty in manipulating such flexible objects compared to their rigid counterparts and the precise estimation of the cable pose. This work proposes a framework that combines object pose estimation using computer-aided design (CAD) models and multiview fusion to perform precise FFC assembly. Our key insight is that a multiview fusion combined with pretrained 6-D pose estimation models offers a more flexible and precise object pose estimation. In a series of experiments involving FFC insertion tasks requiring assembly tolerances down to 0.1 mm, our approach achieves an insertion success rate of 399 out of 400 total attempts. Furthermore, the assembly tasks include the releasing and securing of FFCs from cable connectors, where the system is successful in 200 out of 200 trials. We have also demonstrated the generalization capability of the methodology by successfully completing insertion tasks for common electronic cables like DisplayPort and USB-A, achieving 199 successes in 200 trials. The results not only validate the feasibility of the proposed approach, but also demonstrate its robustness for real-world industrial applications.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1104-1114"},"PeriodicalIF":5.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1109/OJIES.2024.3461949
Praneet Amitabh;Dimitar Bozalakov;Frederik De Belie
In this article, a novel hybrid model of an induction machine is proposed that can emulate the response of a machine with a faulty bearing. The idea behind developing such a topology is to have the response quite close to that from a real asset while keeping it computationally efficient. The aim is to develop an accurate and efficient model, akin to digital twins, which have the potential for real-time operation. Therefore, the model is divided into two parts. One is a physics-based model that takes fundamental equations and motor construction parameters to yield an intermediate response. All the major parameters are taken into account such that the fundamental component comes quite close to that of the real asset and the bearing fault signature comes in the same order. These signatures are quite small and some small parasitic effects or the assumptions taken while simplifying the model might not impact the fundamental component that much but they alter the signature's amplitude quite significantly. One way is to model all the parasitic effects, which might increase the computation effort significantly. Another way is to take all the parasitic effects altogether and bridge the difference using a statistical approach which is developed using experimental data. Therefore, the current measurements were taken for several bearings with different fault severity. These measurements are processed and quantified such that the net outcome can express the evolution of the signature with increasing fault severity. The same is done for the data generated using the physics-based model. Finally, the difference in the responses is reduced using the neural network such that it can mimic real-world machine behavior closely. The analytical model followed by statistical adjustment overall is considered a hybrid model. The ultimate goal of this methodology is to generate extensive datasets encompassing diverse operating conditions that can be used further to estimate the health of the bearing and possibly be used for training predictive algorithms to estimate bearing RUL in motors. The proposed methodology is developed for the machine operating at 1000 and 1500 RPM and is validated for three different operating speeds.
{"title":"Hybrid Modeling of an Induction Machine to Support Bearing Diagnostics","authors":"Praneet Amitabh;Dimitar Bozalakov;Frederik De Belie","doi":"10.1109/OJIES.2024.3461949","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3461949","url":null,"abstract":"In this article, a novel hybrid model of an induction machine is proposed that can emulate the response of a machine with a faulty bearing. The idea behind developing such a topology is to have the response quite close to that from a real asset while keeping it computationally efficient. The aim is to develop an accurate and efficient model, akin to digital twins, which have the potential for real-time operation. Therefore, the model is divided into two parts. One is a physics-based model that takes fundamental equations and motor construction parameters to yield an intermediate response. All the major parameters are taken into account such that the fundamental component comes quite close to that of the real asset and the bearing fault signature comes in the same order. These signatures are quite small and some small parasitic effects or the assumptions taken while simplifying the model might not impact the fundamental component that much but they alter the signature's amplitude quite significantly. One way is to model all the parasitic effects, which might increase the computation effort significantly. Another way is to take all the parasitic effects altogether and bridge the difference using a statistical approach which is developed using experimental data. Therefore, the current measurements were taken for several bearings with different fault severity. These measurements are processed and quantified such that the net outcome can express the evolution of the signature with increasing fault severity. The same is done for the data generated using the physics-based model. Finally, the difference in the responses is reduced using the neural network such that it can mimic real-world machine behavior closely. The analytical model followed by statistical adjustment overall is considered a hybrid model. The ultimate goal of this methodology is to generate extensive datasets encompassing diverse operating conditions that can be used further to estimate the health of the bearing and possibly be used for training predictive algorithms to estimate bearing RUL in motors. The proposed methodology is developed for the machine operating at 1000 and 1500 RPM and is validated for three different operating speeds.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1140-1157"},"PeriodicalIF":5.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reducing the common mode voltage (CMV) fluctuations is crucial in transformer-less (T-less) converters. The modulation modification-based methods inherently increase the steady-state error of the compared currents due to the reduced number of voltage vectors. This error can significantly raise the total harmonic distortion (THD) output current of the inverter. This research presents a strategy of odd virtual vectors based on model-free predictive control using the extended state observer (ESO) to fix the CMV fluctuations and a significant decrease in the THD of the output current. This means the number of CMV stabilizing vectors increases with the linear combination of odd voltage vectors. The proposed method has two advantages over CMV fluctuation reduction schemes that are modulation modification-based: simultaneous control of CMV stabilization and THD reduction in T-less converters, and independence of the controller from system variables and parameters, making it a robust predictive control method. The practical results show that the proposed method, in addition to the complete CMV stabilization and the reduction of the current THD, is completely robust to the changes in the parameters of the ultralocal model and ESO compared to the model-based solutions.
{"title":"Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set","authors":"Majid Akbari;S. Alireza Davari;Reza Ghandehari;Freddy Flores-Bahamonde;Jose Rodriguez","doi":"10.1109/OJIES.2024.3457835","DOIUrl":"10.1109/OJIES.2024.3457835","url":null,"abstract":"Reducing the common mode voltage (CMV) fluctuations is crucial in transformer-less (T-less) converters. The modulation modification-based methods inherently increase the steady-state error of the compared currents due to the reduced number of voltage vectors. This error can significantly raise the total harmonic distortion (THD) output current of the inverter. This research presents a strategy of odd virtual vectors based on model-free predictive control using the extended state observer (ESO) to fix the CMV fluctuations and a significant decrease in the THD of the output current. This means the number of CMV stabilizing vectors increases with the linear combination of odd voltage vectors. The proposed method has two advantages over CMV fluctuation reduction schemes that are modulation modification-based: simultaneous control of CMV stabilization and THD reduction in T-less converters, and independence of the controller from system variables and parameters, making it a robust predictive control method. The practical results show that the proposed method, in addition to the complete CMV stabilization and the reduction of the current THD, is completely robust to the changes in the parameters of the ultralocal model and ESO compared to the model-based solutions.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1042-1057"},"PeriodicalIF":5.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/OJIES.2024.3455264
Maryam Assafo;Peter Langendoerfer
Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings, where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.
{"title":"Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition","authors":"Maryam Assafo;Peter Langendoerfer","doi":"10.1109/OJIES.2024.3455264","DOIUrl":"10.1109/OJIES.2024.3455264","url":null,"abstract":"Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings, where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"993-1010"},"PeriodicalIF":5.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10668405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While it is a widespread understanding that the sustainability of the global economy requires a transition to a circular economy paradigm where a growing share of the raw materials resources used for the manufacturing of the products are recycled when products reach their end-of-life, still this much-needed transition faces organizational and technical challenges. The key technical and economic bottlenecks are in the automation of disassembly. In this article, we propose a viable functional framework for the systematic analysis, design, and implementation of disassembly cells. This framework consists of two main parts: a systematic categorization of disassembly tasks and a modular and flexible hardware (HW)/software (SW) architecture of a disassembly cell able to implement the disassembly tasks. We analyze and categorize human manipulation when disassembling a common object of daily working activities as a new companion concept to the more common concept of daily life activities. We tested and validated our methodology on the disassembly of a car suspension.
{"title":"A Functional and Practical Taxonomy for the Industrial Implementation of Highly Automated Reverse Manufacturing Cells","authors":"Annagiulia Morachioli;Vladimir Sivtsov;Nicolas Rojas;Fabio Bonsignorio","doi":"10.1109/OJIES.2024.3453900","DOIUrl":"10.1109/OJIES.2024.3453900","url":null,"abstract":"While it is a widespread understanding that the sustainability of the global economy requires a transition to a circular economy paradigm where a growing share of the raw materials resources used for the manufacturing of the products are recycled when products reach their end-of-life, still this much-needed transition faces organizational and technical challenges. The key technical and economic bottlenecks are in the automation of disassembly. In this article, we propose a viable functional framework for the systematic analysis, design, and implementation of disassembly cells. This framework consists of two main parts: a systematic categorization of disassembly tasks and a modular and flexible hardware (HW)/software (SW) architecture of a disassembly cell able to implement the disassembly tasks. We analyze and categorize human manipulation when disassembling a common object of daily working activities as a new companion concept to the more common concept of daily life activities. We tested and validated our methodology on the disassembly of a car suspension.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1115-1139"},"PeriodicalIF":5.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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