This article proposes a two-stage reliability-based model for the economic dispatch (ED) of thermal units (TUs) and wind turbines (WTs) in the presence of a demand-side response program (DSRP). In the first stage, the well-being analysis (WBS) is performed to determine the power generation and spinning reserve (SR) of the TUs regarding the timely power generation of WTs. In the second stage, the adoption of the responsive load consumption with various conditions of the generation system in the power pool market is established using the cost of expected energy not served criterion. This optimization problem is solved at two stages using the genetic algorithm. To validate the proposed model, numerical studies have been applied to the generation part of 24-Bus IEEE standard test power system including 11 TUs, one wind farm, and 1000 EVs. It is found from simulation results that an 8%–10% shift and increase in the energy consumption with responsive loads (RLs) participation especially EVs during low-load and off-peak hours can lead to more than 53.83% saving in total reliability cost of power system. In addition, the daily smooth load profile causes to savings in total load ED on TUs in the presence of WTs due to removing the unnecessary startup and shot-down costs during a day.
{"title":"Reliability-Based Thermal and Wind Units Economic Dispatch in the Presence of DSRP","authors":"Farzad Arefi;Hassan Meyar-Naimi;Ahmad Ghaderi Shamim","doi":"10.1109/ICJECE.2023.3320217","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3320217","url":null,"abstract":"This article proposes a two-stage reliability-based model for the economic dispatch (ED) of thermal units (TUs) and wind turbines (WTs) in the presence of a demand-side response program (DSRP). In the first stage, the well-being analysis (WBS) is performed to determine the power generation and spinning reserve (SR) of the TUs regarding the timely power generation of WTs. In the second stage, the adoption of the responsive load consumption with various conditions of the generation system in the power pool market is established using the cost of expected energy not served criterion. This optimization problem is solved at two stages using the genetic algorithm. To validate the proposed model, numerical studies have been applied to the generation part of 24-Bus IEEE standard test power system including 11 TUs, one wind farm, and 1000 EVs. It is found from simulation results that an 8%–10% shift and increase in the energy consumption with responsive loads (RLs) participation especially EVs during low-load and off-peak hours can lead to more than 53.83% saving in total reliability cost of power system. In addition, the daily smooth load profile causes to savings in total load ED on TUs in the presence of WTs due to removing the unnecessary startup and shot-down costs during a day.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 2","pages":"48-59"},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328908","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 : 2024-03-12DOI: 10.1109/ICJECE.2023.3313566
Wenju Sang;Wenyong Guo;Yang Cai;Wenming Guo;Chenyu Tian;Suhang Yu;Shaotao Dai
The performance of the power converter bus bar is not only determined by its normal operational design, but also related to its fault ride-through ability consideration. Conventional busbar design only takes the normal operational performance into account. This article proposes an optimal busbar design method for the modular multilevel converter (MMC) submodule, which takes both the normal and fault ride-through performance into account. The normal operational design is to realize low stray inductance and balanced inductance distribution between parallel capacitor branches. The basic structural design guideline for the MMC submodule is presented. Taking both the stray inductance and manufacturing cost into account, the optimal layout of the busbar is proposed. To balance the capacitor branch currents, the mathematical model of the busbar stray inductance is built. The influence of different busbar structures on the stray inductance is analyzed. The analysis is verified by simulation results. To improve the fault ride-through capability, special consideration is taken into account to reduce the thermal and mechanical stress at the weakest point. Simulation and experimental results verify the efficacy of the proposed approaches.
{"title":"Optimal Busbar Design for the Press-Packed IGBT-Based Modular Multilevel Converter Submodule Considering Both Normal and Fault Ride-Through Conditions","authors":"Wenju Sang;Wenyong Guo;Yang Cai;Wenming Guo;Chenyu Tian;Suhang Yu;Shaotao Dai","doi":"10.1109/ICJECE.2023.3313566","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3313566","url":null,"abstract":"The performance of the power converter bus bar is not only determined by its normal operational design, but also related to its fault ride-through ability consideration. Conventional busbar design only takes the normal operational performance into account. This article proposes an optimal busbar design method for the modular multilevel converter (MMC) submodule, which takes both the normal and fault ride-through performance into account. The normal operational design is to realize low stray inductance and balanced inductance distribution between parallel capacitor branches. The basic structural design guideline for the MMC submodule is presented. Taking both the stray inductance and manufacturing cost into account, the optimal layout of the busbar is proposed. To balance the capacitor branch currents, the mathematical model of the busbar stray inductance is built. The influence of different busbar structures on the stray inductance is analyzed. The analysis is verified by simulation results. To improve the fault ride-through capability, special consideration is taken into account to reduce the thermal and mechanical stress at the weakest point. Simulation and experimental results verify the efficacy of the proposed approaches.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 2","pages":"36-47"},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328936","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}
The proportional, derivative, and integral (PID) controllers are commonly used in load frequency control (LFC) problems in micro-grid (MG) systems with renewable energy resources. However, fine-tuning these controllers is crucial for achieving a satisfactory closed-loop response. In this study, we employed a deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm to adaptively adjust the PID controller parameters, taking into account the uncertain characteristics of the MG system. The DDPG agent was trained until it achieved the maximum possible reward and to learn an optimal policy. Subsequently, the trained agent was utilized in an online manner to adaptively adjust the PID controller gains for managing the fuel-cell (FC) unit, wind turbine generator (WTG), and plug-in electric vehicle (PEV) battery to meet the load demand. We have conducted various simulation scenarios to compare the performance of the proposed adaptive RL-tuned PID controller with the fuzzy gain scheduling PID (FGSPID) controller. While both methods employ intelligent mechanisms to adjust the gains of the PID controllers, our proposed RL-based adaptive PID controller outperformed the FGSPID controller.
{"title":"Deep Deterministic Policy Gradient Reinforcement Learning Based Adaptive PID Load Frequency Control of an AC Micro-Grid","authors":"Kamran Sabahi;Mohsin Jamil;Yaser Shokri-Kalandaragh;Mehdi Tavan;Yogendra Arya","doi":"10.1109/ICJECE.2024.3353670","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3353670","url":null,"abstract":"The proportional, derivative, and integral (PID) controllers are commonly used in load frequency control (LFC) problems in micro-grid (MG) systems with renewable energy resources. However, fine-tuning these controllers is crucial for achieving a satisfactory closed-loop response. In this study, we employed a deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithm to adaptively adjust the PID controller parameters, taking into account the uncertain characteristics of the MG system. The DDPG agent was trained until it achieved the maximum possible reward and to learn an optimal policy. Subsequently, the trained agent was utilized in an online manner to adaptively adjust the PID controller gains for managing the fuel-cell (FC) unit, wind turbine generator (WTG), and plug-in electric vehicle (PEV) battery to meet the load demand. We have conducted various simulation scenarios to compare the performance of the proposed adaptive RL-tuned PID controller with the fuzzy gain scheduling PID (FGSPID) controller. While both methods employ intelligent mechanisms to adjust the gains of the PID controllers, our proposed RL-based adaptive PID controller outperformed the FGSPID controller.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 1","pages":"15-21"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140063617","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}
The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous <xref>(2)</xref> . These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous <xref>(2)</xref> excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively); Faster-R-CNN-Inception-ResNetV2-Atrous <xref>(4)</xref> outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%); and Faster-R-CNN-Inception-ResNetV2-Atrous <xref>(4)</xref> is the best-performing method overall. <italic>Résumé</i> —L’approche conventionnelle de l’inspection du cuir chevelure dans l’industrie de la coiffure repose sur l’interprétation manuelle des images des symptômes du cuir chevelure. Les coiffeurs fournissent des traitements sur la base d’une évaluation visuelle, ce qui entraîne des inexactitudes et des erreurs d’appréciation potentielles. Pour remédier à ces lacunes, cet article propose un nouveau système multimodal d’inspection et de diagnostic du cuir chevelure basée sur l’apprentissage profond. Le système proposé utilise divers modules de reconnaissance d’objets par intelligence artificielle (IA), tels que le détecteur multi-boîtes (SSD)-MobileNetV2, SSD-InceptionV2, le réseau neuronal convolutif régional plus rapide (R-CNN)-InceptionV2, et le R-CNN-Inception-ResNetV2-Atrous <xref>(2)</xref> plus rapide. Ces modules forment un module diversifié de reconnaissance des symptômes du cuir chevelure intégrée dans un serveur de reconnaissance IA. Cette étude a porté sur neuf symptômes du cuir chevelure, englobant quatre affections primaires (pellicules, perte de cheveux, cheveux gris et cheveux huilés), ainsi que cinq affections spéciales (folliculite, résidus chimiques, moisissures, champignons, mycoses et psoriasis). L’efficacité du système proposé est évaluée par des expériences
{"title":"An Efficient Scalp Inspection and Diagnosis System Using Multiple Deep Learning-Based Modules Un système efficace d’inspection et de diagnostic du cuir chevelure utilisant plusieurs modules basés sur l’apprentissage profond","authors":"Liang-Bi Chen;Wan-Jung Chang;Yi-Chan Chiu;Xiang-Rui Huang","doi":"10.1109/ICJECE.2024.3354291","DOIUrl":"https://doi.org/10.1109/ICJECE.2024.3354291","url":null,"abstract":"The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous \u0000<xref>(2)</xref>\u0000. These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous \u0000<xref>(2)</xref>\u0000 excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively); Faster-R-CNN-Inception-ResNetV2-Atrous \u0000<xref>(4)</xref>\u0000 outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%); and Faster-R-CNN-Inception-ResNetV2-Atrous \u0000<xref>(4)</xref>\u0000 is the best-performing method overall. \u0000<italic>Résumé</i>\u0000—L’approche conventionnelle de l’inspection du cuir chevelure dans l’industrie de la coiffure repose sur l’interprétation manuelle des images des symptômes du cuir chevelure. Les coiffeurs fournissent des traitements sur la base d’une évaluation visuelle, ce qui entraîne des inexactitudes et des erreurs d’appréciation potentielles. Pour remédier à ces lacunes, cet article propose un nouveau système multimodal d’inspection et de diagnostic du cuir chevelure basée sur l’apprentissage profond. Le système proposé utilise divers modules de reconnaissance d’objets par intelligence artificielle (IA), tels que le détecteur multi-boîtes (SSD)-MobileNetV2, SSD-InceptionV2, le réseau neuronal convolutif régional plus rapide (R-CNN)-InceptionV2, et le R-CNN-Inception-ResNetV2-Atrous \u0000<xref>(2)</xref>\u0000 plus rapide. Ces modules forment un module diversifié de reconnaissance des symptômes du cuir chevelure intégrée dans un serveur de reconnaissance IA. Cette étude a porté sur neuf symptômes du cuir chevelure, englobant quatre affections primaires (pellicules, perte de cheveux, cheveux gris et cheveux huilés), ainsi que cinq affections spéciales (folliculite, résidus chimiques, moisissures, champignons, mycoses et psoriasis). L’efficacité du système proposé est évaluée par des expériences","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 1","pages":"22-35"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140063602","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}
The performance of an internal permanent magnet MISC machine (IPMMM) is improved by its cylindrical rotor, which dampens torque ripple. Changing the stator and rotor core materials, comparing with torque values to identify efficient motor and keeping that in consent, then change the magnet material for better ripple torque. The change in the rotor materials to vary the torque by the rotor angle is analyzed. The finite element method is applied to a MISC motor operating at 290 V, 20 A, and 3000 r/min with the goals of increasing torque and decreasing torque ripple. In this machine, changing stator and rotor materials improves the torque. The results are calculated and analyzed numerically, with the results being virtualized graphically.
{"title":"Analysis of Various Core Materials and Permanent Magnets on MISC Type Motor for Electrified Transportation Systems","authors":"Prabhu Sundaramoorthy;Saravanan Sivasamy;T. Sivaprakasam;S. Vijay Shankar;Vijaykumar Arun;Mahadevan Balaji","doi":"10.1109/ICJECE.2023.3339627","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3339627","url":null,"abstract":"The performance of an internal permanent magnet MISC machine (IPMMM) is improved by its cylindrical rotor, which dampens torque ripple. Changing the stator and rotor core materials, comparing with torque values to identify efficient motor and keeping that in consent, then change the magnet material for better ripple torque. The change in the rotor materials to vary the torque by the rotor angle is analyzed. The finite element method is applied to a MISC motor operating at 290 V, 20 A, and 3000 r/min with the goals of increasing torque and decreasing torque ripple. In this machine, changing stator and rotor materials improves the torque. The results are calculated and analyzed numerically, with the results being virtualized graphically.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654697","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}
Voltage source inverters are currently gaining popularity in a variety of power system applications, including renewable energy, HVdc, and microgrid. Among all the renewable energy applications, multilevel inverters (MLIs) are the most popular converters for high- and medium-power industries. This article reviews and compares many of the recently developed topologies for renewable energy integration with energy storage systems (ESSs). In addition, a new design of a seven-level inverter is proposed. It utilizes only six power electronic switches in its design of which four have unidirectional voltage-blocking capability and two have bidirectional voltage-blocking capability. Various simulation results of the proposed topology along with the total harmonic distortion (THD) contents of voltage and current are presented in detail under different loading conditions. Afterward, a new factor of comparison is proposed in which component ratings are also considered. Finally, a hardware prototype is built to check the authenticity of the proposed design, and satisfactory results are presented.
{"title":"Implementation of a Novel Multilevel Inverter Topology With Minimal Components—An Experimental Study","authors":"Vijay Sirohi;Tejinder Singh Saggu;Jagdish Kumar;Bob Gill","doi":"10.1109/ICJECE.2023.3340326","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3340326","url":null,"abstract":"Voltage source inverters are currently gaining popularity in a variety of power system applications, including renewable energy, HVdc, and microgrid. Among all the renewable energy applications, multilevel inverters (MLIs) are the most popular converters for high- and medium-power industries. This article reviews and compares many of the recently developed topologies for renewable energy integration with energy storage systems (ESSs). In addition, a new design of a seven-level inverter is proposed. It utilizes only six power electronic switches in its design of which four have unidirectional voltage-blocking capability and two have bidirectional voltage-blocking capability. Various simulation results of the proposed topology along with the total harmonic distortion (THD) contents of voltage and current are presented in detail under different loading conditions. Afterward, a new factor of comparison is proposed in which component ratings are also considered. Finally, a hardware prototype is built to check the authenticity of the proposed design, and satisfactory results are presented.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 1","pages":"7-14"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654257","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 : 2023-12-27DOI: 10.1109/ICJECE.2023.3328442
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering Publication Information","authors":"","doi":"10.1109/ICJECE.2023.3328442","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3328442","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050592","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}
Kratom (Mitragyna speciosa Korth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments. However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score = 0.63). While this study is limited to ECG abnormalities, the results indicate that long-term usage of kratom for its health benefits may be considered a safe and natural practice.
{"title":"Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning","authors":"Kasikrit Damkliang;Jularat Chumnaul;Dania Cheaha;Somchai Sriwiriyajan;Ekkasit Kumarnsit","doi":"10.1109/ICJECE.2023.3320103","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3320103","url":null,"abstract":"Kratom (Mitragyna speciosa Korth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments. However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score = 0.63). While this study is limited to ECG abnormalities, the results indicate that long-term usage of kratom for its health benefits may be considered a safe and natural practice.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"380-390"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550240","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 : 2023-11-30DOI: 10.1109/ICJECE.2023.3313151
Abolfazl Babaei;Waldemar Ziomek;Aniruddha M. Gole
In this article, a new device to test the on- load tap changer (OLTC) is proposed. The presented device, which is called OLTC tap scan (OLTCTS), enables the user to find the location of the error without opening the transformer and removing OLTC. The proposed device applies the electrical parameters to the bushings of transformers and utilizes electrical parameters, such as voltage, current, current and voltage slopes, and resistance. All the mentioned electrical parameters are evaluated both statistically and dynamically in the presented device. Dynamic current ripple and dynamic voltage ripple are the main parameters that are evaluated for OLTC testing in this article. After designing and building this device, it was used for practical testing on three power transformers, and the results obtained from those tests are analyzed in this article.
{"title":"An Improved Device for the Dynamic Testing of OLTCs Un dispositif amélioré pour l’essai dynamique des OLTCs","authors":"Abolfazl Babaei;Waldemar Ziomek;Aniruddha M. Gole","doi":"10.1109/ICJECE.2023.3313151","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3313151","url":null,"abstract":"In this article, a new device to test the on- load tap changer (OLTC) is proposed. The presented device, which is called OLTC tap scan (OLTCTS), enables the user to find the location of the error without opening the transformer and removing OLTC. The proposed device applies the electrical parameters to the bushings of transformers and utilizes electrical parameters, such as voltage, current, current and voltage slopes, and resistance. All the mentioned electrical parameters are evaluated both statistically and dynamically in the presented device. Dynamic current ripple and dynamic voltage ripple are the main parameters that are evaluated for OLTC testing in this article. After designing and building this device, it was used for practical testing on three power transformers, and the results obtained from those tests are analyzed in this article.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"358-370"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550318","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}
The switched reluctance motor (SRM) has gained significant attention in the industry due to its advantageous features, such as a durable rotor, simple stator windings, and ease of manufacturing. The main focus of SRM development has been enhancing efficiency while reducing torque ripple and losses. Given that this study aims to apply the proposed SRM design in electric vehicles, it is crucial to achieve a motor that is free from torque ripple and exhibits high efficiency. This research proposes a novel type of SRM called the outer rotor permanent magnet SRM (ORPMSRM) specifically for lightweight electric vehicles. Structural modifications are introduced in the ORPMSRM design to improve the torque characteristics and minimize losses. The electromagnetic analysis is conducted to predict the performance of the ORPMSRM with these modified structures. This article offers a comprehensive investigation that considers various configurations of rotor poles and stator poles with permanent magnets (PMs) to enhance the performance of the ORPMSRM. The finite element analysis (FEA) results are compared with experimental results, providing valuable insights into the motor’s performance and validating the analytical predictions.
{"title":"A Comprehensive Investigation of Outer Rotor Permanent Magnet Switched Reluctance Motor for Enhanced Performance in Electric Vehicles","authors":"Saravanan Sivasamy;Prabhu Sundaramoorthy;Marsaline Beno","doi":"10.1109/ICJECE.2023.3316261","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3316261","url":null,"abstract":"The switched reluctance motor (SRM) has gained significant attention in the industry due to its advantageous features, such as a durable rotor, simple stator windings, and ease of manufacturing. The main focus of SRM development has been enhancing efficiency while reducing torque ripple and losses. Given that this study aims to apply the proposed SRM design in electric vehicles, it is crucial to achieve a motor that is free from torque ripple and exhibits high efficiency. This research proposes a novel type of SRM called the outer rotor permanent magnet SRM (ORPMSRM) specifically for lightweight electric vehicles. Structural modifications are introduced in the ORPMSRM design to improve the torque characteristics and minimize losses. The electromagnetic analysis is conducted to predict the performance of the ORPMSRM with these modified structures. This article offers a comprehensive investigation that considers various configurations of rotor poles and stator poles with permanent magnets (PMs) to enhance the performance of the ORPMSRM. The finite element analysis (FEA) results are compared with experimental results, providing valuable insights into the motor’s performance and validating the analytical predictions.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"342-347"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550316","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}
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