Pub Date : 2024-02-12DOI: 10.1109/OJIES.2024.3361167
Tomas Salvadores;Javier Pereda;Félix Rojas
The modular multilevel converter (MMC) can integrate distributed energy systems (DES), such as a battery energy storage system, to expand its functionalities and carry out multiple simultaneous tasks. However, a DES induces power imbalances within the MMC, which affects the operating currents and voltages of the converter. This phenomenon has been partially covered in recent works, but an analytical analysis has not yet been carried out to see the behavior and implications in different MMC-DES applications. This article introduces a novel analytical analysis of the power imbalances between MMC clusters. It pioneers the development of general equations and imbalance capability metrics, enabling the assessment of maximum currents and voltages supported by the MMC clusters. The developed tools allow the evaluation of any MMC-DES application regarding the current and voltage rating requirements of MMC clusters. The analysis shows that the MMC operating mode can substantially restrain or enlarge its imbalance capacity, affecting its suitability for different DES applications. While it needs around 33% current overrating in the worst imbalances, under some operating modes it can reach most imbalances without requiring current overrating. The ac compensation mode is much more capable of achieving imbalances than the dc compensation mode, reaching 88.37% and 16.74% of the imbalance points, respectively, without requiring any overrating.
{"title":"Power Imbalance Analysis of Modular Multilevel Converter With Distributed Energy Systems","authors":"Tomas Salvadores;Javier Pereda;Félix Rojas","doi":"10.1109/OJIES.2024.3361167","DOIUrl":"10.1109/OJIES.2024.3361167","url":null,"abstract":"The modular multilevel converter (MMC) can integrate distributed energy systems (DES), such as a battery energy storage system, to expand its functionalities and carry out multiple simultaneous tasks. However, a DES induces power imbalances within the MMC, which affects the operating currents and voltages of the converter. This phenomenon has been partially covered in recent works, but an analytical analysis has not yet been carried out to see the behavior and implications in different MMC-DES applications. This article introduces a novel analytical analysis of the power imbalances between MMC clusters. It pioneers the development of general equations and imbalance capability metrics, enabling the assessment of maximum currents and voltages supported by the MMC clusters. The developed tools allow the evaluation of any MMC-DES application regarding the current and voltage rating requirements of MMC clusters. The analysis shows that the MMC operating mode can substantially restrain or enlarge its imbalance capacity, affecting its suitability for different DES applications. While it needs around 33% current overrating in the worst imbalances, under some operating modes it can reach most imbalances without requiring current overrating. The ac compensation mode is much more capable of achieving imbalances than the dc compensation mode, reaching 88.37% and 16.74% of the imbalance points, respectively, without requiring any overrating.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"109-121"},"PeriodicalIF":8.5,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947272","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 : 2024-02-07DOI: 10.1109/OJIES.2024.3363500
Hoa Thi Nguyen;Roland Olsson;Øystein Haugen
Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.
{"title":"Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators","authors":"Hoa Thi Nguyen;Roland Olsson;Øystein Haugen","doi":"10.1109/OJIES.2024.3363500","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3363500","url":null,"abstract":"Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"91-108"},"PeriodicalIF":8.5,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10423805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937098","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 : 2024-02-06DOI: 10.1109/OJIES.2024.3363093
Aswani Radhakrishnan;Jushnah Palliyalil;Sreeja Babu;Anuar Dorzhigulov;Alex James
The hardware implementation of neuromorphic system requires energy and area-efficient hardware. Memristor-based hardware architectures is a promising approach that naturally mimics the switching behavior of the neuron models. However, to build complex neural systems, it is a tedious process to select the right memristor models and architectures that are suitable to be used in a range of realistic conditions. To simplify the design and development of neuromemristive architectures, we present a web-based graphical user interface (GUI) called “PyMem” that uses Keras Python to implement multiple memristor models on multiple neural architectures that can be used to analyze their working under a wide range of hardware variability. Without the need for programming, the GUI provides options for adding variability to the memristors and observing the neural network behavior under realistic conditions. The tool has options to characterize the ideal (software) and nonideal (hardware) for performance analysis including accuracy, precision, recall, and relative current error values.
神经形态系统的硬件实现需要高能效和高面积效率的硬件。基于忆阻器的硬件架构是一种很有前途的方法，它能自然地模拟神经元模型的开关行为。然而，要构建复杂的神经系统，选择适合在各种现实条件下使用的正确忆阻器模型和架构是一个繁琐的过程。为了简化神经忆阻器架构的设计和开发，我们提出了一个名为 "PyMem "的基于网络的图形用户界面（GUI），它使用 Keras Python 在多个神经架构上实现多个忆阻器模型，可用于分析它们在各种硬件变化条件下的工作情况。无需编程，图形用户界面就能为忆阻器提供添加可变性的选项，并观察神经网络在现实条件下的行为。该工具还提供了一些选项，用于描述理想（软件）和非理想（硬件）的性能分析，包括准确度、精确度、召回率和相对电流误差值。
{"title":"PyMem: A Graphical User Interface Tool for Neuromemristive Hardware–Software Co-Design","authors":"Aswani Radhakrishnan;Jushnah Palliyalil;Sreeja Babu;Anuar Dorzhigulov;Alex James","doi":"10.1109/OJIES.2024.3363093","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3363093","url":null,"abstract":"The hardware implementation of neuromorphic system requires energy and area-efficient hardware. Memristor-based hardware architectures is a promising approach that naturally mimics the switching behavior of the neuron models. However, to build complex neural systems, it is a tedious process to select the right memristor models and architectures that are suitable to be used in a range of realistic conditions. To simplify the design and development of neuromemristive architectures, we present a web-based graphical user interface (GUI) called “PyMem” that uses Keras Python to implement multiple memristor models on multiple neural architectures that can be used to analyze their working under a wide range of hardware variability. Without the need for programming, the GUI provides options for adding variability to the memristors and observing the neural network behavior under realistic conditions. The tool has options to characterize the ideal (software) and nonideal (hardware) for performance analysis including accuracy, precision, recall, and relative current error values.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"81-90"},"PeriodicalIF":8.5,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10423246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139916626","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 : 2024-01-31DOI: 10.1109/OJIES.2024.3360509
Awungabeh Flavis Akawung;Besong John Ebot;Yasutaka Fujimoto
High power-density electric machines present the benefits of high torque and speed. However, this generally comes with heating problems characterized by high temperatures that affect performance. Conventional approaches to address overheating are to include cooling fans or jackets within the stator core of the machine. This approach is challenging to implement in small-size high power-density machines. In this article, a cooling mechanism integrated in the rotor of a high power-density permanent magnet motor is proposed. It comprises a set of six holes, shrouded within a hollow shaft. The mechanism is based on conditioning air due to a centrifugal force that is produced by the rotational speed of the rotor from the inlet. A theoretical model based on flow resistance network is proposed to analyze the airflow rate. An analytical thermal model based on lumped parameter thermal network is developed to analyze the effect of the flow rate on the temperature distribution in the motor. Also, a simulation analysis model was conducted using computational fluid dynamics to analyze the effect of air flowing in the motor. An experimental prototype is developed to verify, validate, and evaluate the proposed cooling model. The cooling system is effective in reducing temperatures from speeds above 6000 min −1 .
{"title":"Airflow Cooling Mechanism for High Power-Density Permanent Magnet Motor","authors":"Awungabeh Flavis Akawung;Besong John Ebot;Yasutaka Fujimoto","doi":"10.1109/OJIES.2024.3360509","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3360509","url":null,"abstract":"High power-density electric machines present the benefits of high torque and speed. However, this generally comes with heating problems characterized by high temperatures that affect performance. Conventional approaches to address overheating are to include cooling fans or jackets within the stator core of the machine. This approach is challenging to implement in small-size high power-density machines. In this article, a cooling mechanism integrated in the rotor of a high power-density permanent magnet motor is proposed. It comprises a set of six holes, shrouded within a hollow shaft. The mechanism is based on conditioning air due to a centrifugal force that is produced by the rotational speed of the rotor from the inlet. A theoretical model based on flow resistance network is proposed to analyze the airflow rate. An analytical thermal model based on lumped parameter thermal network is developed to analyze the effect of the flow rate on the temperature distribution in the motor. Also, a simulation analysis model was conducted using computational fluid dynamics to analyze the effect of air flowing in the motor. An experimental prototype is developed to verify, validate, and evaluate the proposed cooling model. The cooling system is effective in reducing temperatures from speeds above 6000 min\u0000<sup>−1</sup>\u0000.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"67-80"},"PeriodicalIF":8.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10417077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139908592","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 : 2024-01-29DOI: 10.1109/OJIES.2024.3359951
Zhitai Liu;Xinghu Yu;Weiyang Lin;Juan J. Rodríguez-Andina
Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system.
{"title":"Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems","authors":"Zhitai Liu;Xinghu Yu;Weiyang Lin;Juan J. Rodríguez-Andina","doi":"10.1109/OJIES.2024.3359951","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3359951","url":null,"abstract":"Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"54-66"},"PeriodicalIF":8.5,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715144","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 : 2024-01-22DOI: 10.1109/OJIES.2024.3356721
Niklas Monzen;Florian Stroebl;Herbert Palm;Christoph M. Hackl
Multiobjective hyperparameter optimization is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives, such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.
{"title":"Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines","authors":"Niklas Monzen;Florian Stroebl;Herbert Palm;Christoph M. Hackl","doi":"10.1109/OJIES.2024.3356721","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3356721","url":null,"abstract":"Multiobjective hyperparameter optimization is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives, such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"41-53"},"PeriodicalIF":8.5,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139704470","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 : 2024-01-17DOI: 10.1109/OJIES.2024.3355192
Liheng Chen;Qingsong Xu
This article proposes a new reinforcement learning (RL)-based adaptive control design for precision motion control of a two-degree-of-freedom piezoelectric XY nanopositioning system. In this design, an actor-critic structure is developed to eliminate the effects of uncertain nonlinearities and cross-coupling motion between the two working axes. Then, an adaptive parameter adjustment mechanism is designed to optimize the control performance without a priori knowledge of the unknown perturbations. The effectiveness and superiority of the proposed method are verified by performing simulation and experimental studies. The results show that the proposed RL-based adaptive control method provides a better robust performance and smaller tracking error for the nanopositioning system.
{"title":"Reinforcement Learning-Based Adaptive Control of a Piezo-Driven Nanopositioning System","authors":"Liheng Chen;Qingsong Xu","doi":"10.1109/OJIES.2024.3355192","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3355192","url":null,"abstract":"This article proposes a new reinforcement learning (RL)-based adaptive control design for precision motion control of a two-degree-of-freedom piezoelectric XY nanopositioning system. In this design, an actor-critic structure is developed to eliminate the effects of uncertain nonlinearities and cross-coupling motion between the two working axes. Then, an adaptive parameter adjustment mechanism is designed to optimize the control performance without a priori knowledge of the unknown perturbations. The effectiveness and superiority of the proposed method are verified by performing simulation and experimental studies. The results show that the proposed RL-based adaptive control method provides a better robust performance and smaller tracking error for the nanopositioning system.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"28-40"},"PeriodicalIF":8.5,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10402007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676215","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}
Neutral conductor loss on the upstream network of an electrical installation, as well as unintentional islanding operation, are two critical issues directly related to the safety of both human life and equipment of the electrical installation. Furthermore, another critical issue is meter tampering, which affects the operation and development of national power sectors. The state-of-the-art methods deal with those issues separately, without providing a holistic solution; moreover, they are facing several challenges in the prospect of mass storage installations at buildings. This article introduces a new scheme for the detection of neutral loss at the upstream network, the unintentional islanding conditions, and meter tampering simultaneously. In this context, a suitable technical solution is given by using one low-power H-bridge inverter and a current transformer, along with voltage and current measuring instruments. The detection scheme is based on the injection of a (zero-sequence) 12th harmonic voltage component in series with the utility voltage and the monitoring of the corresponding harmonic current component. The theoretical analysis of the proposed technique is discussed, whereas its effectiveness is validated through simulation and experimental results.
电力设施上游网络的中性线损耗和意外孤岛运行是直接关系到人的生命和电力设施设备安全的两个关键问题。此外，另一个关键问题是影响国家电力部门运行和发展的电表篡改问题。最先进的方法分别处理这些问题，但没有提供整体解决方案；此外，这些方法在楼宇大容量存储装置的前景方面面临着一些挑战。本文介绍了一种同时检测上游网络中性点损耗、无意孤岛情况和电表篡改的新方案。在此背景下，通过使用一个小功率 H 桥逆变器和一个电流互感器以及电压和电流测量仪器，给出了一个合适的技术解决方案。检测方案的基础是注入与市电电压串联的（零序）12 次谐波电压分量，并监测相应的谐波电流分量。本文讨论了拟议技术的理论分析，并通过模拟和实验结果验证了其有效性。
{"title":"On the Detection of Neutral Loss, Islanding, and Meter Tampering in Electrical Installations","authors":"Syllas Frantzeskakis;Nick Rigogiannis;Christos Christodoulou;Nick Papanikolaou","doi":"10.1109/OJIES.2024.3354799","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3354799","url":null,"abstract":"Neutral conductor loss on the upstream network of an electrical installation, as well as unintentional islanding operation, are two critical issues directly related to the safety of both human life and equipment of the electrical installation. Furthermore, another critical issue is meter tampering, which affects the operation and development of national power sectors. The state-of-the-art methods deal with those issues separately, without providing a holistic solution; moreover, they are facing several challenges in the prospect of mass storage installations at buildings. This article introduces a new scheme for the detection of neutral loss at the upstream network, the unintentional islanding conditions, and meter tampering simultaneously. In this context, a suitable technical solution is given by using one low-power H-bridge inverter and a current transformer, along with voltage and current measuring instruments. The detection scheme is based on the injection of a (zero-sequence) 12th harmonic voltage component in series with the utility voltage and the monitoring of the corresponding harmonic current component. The theoretical analysis of the proposed technique is discussed, whereas its effectiveness is validated through simulation and experimental results.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"1-12"},"PeriodicalIF":8.5,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654493","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 : 2024-01-10DOI: 10.1109/OJIES.2024.3352265
Yunhe Yu;Lode De Herdt;Aditya Shekhar;Gautham Ram Chandra Mouli;Pavol Bauer
The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.
{"title":"EV Smart Charging in Distribution Grids–Experimental Evaluation Using Hardware in the Loop Setup","authors":"Yunhe Yu;Lode De Herdt;Aditya Shekhar;Gautham Ram Chandra Mouli;Pavol Bauer","doi":"10.1109/OJIES.2024.3352265","DOIUrl":"https://doi.org/10.1109/OJIES.2024.3352265","url":null,"abstract":"The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"13-27"},"PeriodicalIF":8.5,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10387706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676258","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 : 2023-12-28DOI: 10.1109/OJIES.2023.3347101
Rodrigo H. Cuzmar;Andrés Mora;Javier Pereda;Ricardo P. Aguilera;Pablo Poblete;Sebastián Neira
Modular multilevel matrix converters stand out for their performance in ac–ac high-power conversion. However, they require multiple control loops to govern the currents from both ac ports, the internal circulating currents, and the capacitor voltages. This article proposes a computationally efficient model predictive control (MPC) strategy based on a new converter modeling to exploit the phase-shifted pulsewidth modulation working principle fully, achieving four improvements: 1) control unification of both ac-ports currents, circulating currents, and capacitor voltages; 2) constrained optimization to safeguard the converter limits; 3) computational burden reduction and high scalability compared to standard MPC strategies; and 4) wide frequency operation with fast closed-loop transient responses, low harmonic distortion, and fixed switching frequency.
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