Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776964
Saikat Majumder, M. Giri, G. Adarsh
The availability of inexpensive software defined radios (SDR) has enabled the deployment of cognitive radio (CR) features in large-scale networks such as internet-of-things (IoT). However, such radio receivers are limited by their non-ideal characteristics like coloured noise, IQ imbalance, phase noise etc. Performance of existing spectrum sensing algorithm degrade in coloured noise due to swelling effect of received signal covariance matrix. To overcome this limitation, we propose a novel spectrum sensing technique based on extreme learning machine (ELM) which uses eigenvalue and log determinant (LogDet) of covariance matrix features. Experimental results show the effectiveness of the proposed technique over existing algorithms in literature.
{"title":"Extreme Learning Machine based Spectrum Sensing in Coloured Noise with RTL-SDR","authors":"Saikat Majumder, M. Giri, G. Adarsh","doi":"10.1109/ICPC2T53885.2022.9776964","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776964","url":null,"abstract":"The availability of inexpensive software defined radios (SDR) has enabled the deployment of cognitive radio (CR) features in large-scale networks such as internet-of-things (IoT). However, such radio receivers are limited by their non-ideal characteristics like coloured noise, IQ imbalance, phase noise etc. Performance of existing spectrum sensing algorithm degrade in coloured noise due to swelling effect of received signal covariance matrix. To overcome this limitation, we propose a novel spectrum sensing technique based on extreme learning machine (ELM) which uses eigenvalue and log determinant (LogDet) of covariance matrix features. Experimental results show the effectiveness of the proposed technique over existing algorithms in literature.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115876036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776741
N. Manna, A. K. Sil
Energy that we consume are mostly served from fossil fuel although it largely pollutes environment and has limited available reserve. Domestic use of electricity is considerable one among all major sectors of electricity consumption. This large amount of fossil fuel usage is being replaced by renewable energy resources (e.g. photovoltaic, wind etc.) which can be placed locally at the consumer premises. Thus penetration of renewables can gradually reduce the dominance of using energy from fossil fuel. Research is being carried out on the effect of penetration of renewable energy resources with energy storages. Aim of all these research is to check the potential of these penetrations along with achieving minimum losses and maximum benefits to the system. A similar kind of analysis has been performed here on European Low Voltage Test Feeder. This is on the basis of achievable reduction in peak demand and three phase load balancing to the utility at the secondary side of substation transformer. In this context, an approach of tracking and addressing power injection to the peak load concentration hourly by dynamic cluster formations throughout the network have been used. Optimization of multiple relevant objectives has been performed to validate the compatibility of active and reactive power injection at different hour of the day by domestic consumers into the system. Also, based on feasible power injection at consumer ends, photovoltaic energy penetration and aggregate capacity of energy storages in the whole network have been determined.
{"title":"An Optimization Based Approach for Peak Shaving and Phase Balancing of Unbalanced Radial Distribution System by Peak Load Clustering","authors":"N. Manna, A. K. Sil","doi":"10.1109/ICPC2T53885.2022.9776741","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776741","url":null,"abstract":"Energy that we consume are mostly served from fossil fuel although it largely pollutes environment and has limited available reserve. Domestic use of electricity is considerable one among all major sectors of electricity consumption. This large amount of fossil fuel usage is being replaced by renewable energy resources (e.g. photovoltaic, wind etc.) which can be placed locally at the consumer premises. Thus penetration of renewables can gradually reduce the dominance of using energy from fossil fuel. Research is being carried out on the effect of penetration of renewable energy resources with energy storages. Aim of all these research is to check the potential of these penetrations along with achieving minimum losses and maximum benefits to the system. A similar kind of analysis has been performed here on European Low Voltage Test Feeder. This is on the basis of achievable reduction in peak demand and three phase load balancing to the utility at the secondary side of substation transformer. In this context, an approach of tracking and addressing power injection to the peak load concentration hourly by dynamic cluster formations throughout the network have been used. Optimization of multiple relevant objectives has been performed to validate the compatibility of active and reactive power injection at different hour of the day by domestic consumers into the system. Also, based on feasible power injection at consumer ends, photovoltaic energy penetration and aggregate capacity of energy storages in the whole network have been determined.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777028
Piyush Kant, Bhim Singh
This paper presents a new configuration of medium voltage drive (MVD), in which a 60-pulse AC-DC converter is adopted on grid side and a unique cascaded 7-level inverter on the drive end. This 60-pulse converter utilizes a new multi-phase transformer, which converts 3-φinput into 5-φ output AC-supply. The cascaded 7-level inverter is achieved by cascading six numbers of three-phase VSI in unique manner. This type of cascading is taken because the voltage and power level of the level can be scaled as per the application demand. Moreover, this configuration of 7-level inverter needs less numbers of power semiconductor switches than conventional 7-level cascaded H-bridge (CHB) inverter. A vector control scheme is used to control an induction motor (IM) and in this vector control rotor flux reference frame is taken to avoid coupling between the d and q axes currents. A nearest level modulation technique (NLMT) is adopted to control presented 7-level inverter. Due to this NLMT, a 7-level inverter is switched at fundamental frequency switching (FFS) and offers low switching losses than conventional CHB. Performance of MVIMD is verified through both simulated and test results while operating it at different operating conditions and it shows promising performance for the presented drive during all required operating region.
{"title":"Multipulse Converter Fed New 7-Level Cascaded Multilevel Inverter Based Induction Motor Drive","authors":"Piyush Kant, Bhim Singh","doi":"10.1109/ICPC2T53885.2022.9777028","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777028","url":null,"abstract":"This paper presents a new configuration of medium voltage drive (MVD), in which a 60-pulse AC-DC converter is adopted on grid side and a unique cascaded 7-level inverter on the drive end. This 60-pulse converter utilizes a new multi-phase transformer, which converts 3-φinput into 5-φ output AC-supply. The cascaded 7-level inverter is achieved by cascading six numbers of three-phase VSI in unique manner. This type of cascading is taken because the voltage and power level of the level can be scaled as per the application demand. Moreover, this configuration of 7-level inverter needs less numbers of power semiconductor switches than conventional 7-level cascaded H-bridge (CHB) inverter. A vector control scheme is used to control an induction motor (IM) and in this vector control rotor flux reference frame is taken to avoid coupling between the d and q axes currents. A nearest level modulation technique (NLMT) is adopted to control presented 7-level inverter. Due to this NLMT, a 7-level inverter is switched at fundamental frequency switching (FFS) and offers low switching losses than conventional CHB. Performance of MVIMD is verified through both simulated and test results while operating it at different operating conditions and it shows promising performance for the presented drive during all required operating region.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116956529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777077
A. S., L. S.
Last few years have seen a remarkable growth in the number of Electric Vehicle (EV) users. Autonomous driving and parking of EV s are the future of vehicle industry and this calls for customer friendly and innovative charging infrastructure de-velopment. A fully autonomous charging system highly aid them. This paper proposes the design of an automatic charging system for EVs. In this work YOLO (You Only Look Once) algorithm, a deep neural network based object detection algorithm is used to automatically recognize and locate the charging port of an EV. Thus accurate positioning of charging port in a complex environment can be achieved. A rigid-flexible manipulator on a movable platform is then designed for conductive charging of an EV automatically. Irrespective of vehicle models and charging ports the proposed design can be used for the automatic charging of EVs. The designed robotic manipulator successfully follows the path traced by the charging port detection system and perform plug-in process. The simulation results show the efficacy of the proposed design.
过去几年，电动汽车(EV)用户的数量显著增长。电动汽车的自动驾驶和停车是汽车行业的未来，这需要客户友好和创新的充电基础设施发展。一个完全自主的充电系统给了他们很大的帮助。提出了一种电动汽车自动充电系统的设计方案。YOLO (You Only Look Once)算法是一种基于深度神经网络的目标检测算法，用于自动识别和定位电动汽车的充电口。从而实现在复杂环境下对充电口的精确定位。在此基础上，设计了可移动平台上的刚柔机械手，实现电动汽车自动导电充电。无论车辆型号和充电端口如何，所提出的设计都可以用于电动汽车的自动充电。所设计的机械手成功地沿着充电口检测系统跟踪的路径进行插拔。仿真结果表明了该设计的有效性。
{"title":"Design of Automatic Charging System for Electric Vehicles using Rigid-Flexible Manipulator","authors":"A. S., L. S.","doi":"10.1109/ICPC2T53885.2022.9777077","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777077","url":null,"abstract":"Last few years have seen a remarkable growth in the number of Electric Vehicle (EV) users. Autonomous driving and parking of EV s are the future of vehicle industry and this calls for customer friendly and innovative charging infrastructure de-velopment. A fully autonomous charging system highly aid them. This paper proposes the design of an automatic charging system for EVs. In this work YOLO (You Only Look Once) algorithm, a deep neural network based object detection algorithm is used to automatically recognize and locate the charging port of an EV. Thus accurate positioning of charging port in a complex environment can be achieved. A rigid-flexible manipulator on a movable platform is then designed for conductive charging of an EV automatically. Irrespective of vehicle models and charging ports the proposed design can be used for the automatic charging of EVs. The designed robotic manipulator successfully follows the path traced by the charging port detection system and perform plug-in process. The simulation results show the efficacy of the proposed design.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777070
Wesam Shishah
In the medical field, early prediction of disease is a big challenge. This paper focuses on predicting heart disease at an early stage. Heart disease is a fatal human disease that rapidly increases at a global level. This disease affects both developed as well as undeveloped countries which subsequently causes death. In heart disease, the heart doesn't supply the required volume of blood to other body parts. It is essential to diagnose this disease at the early stage for preventing patients from higher damage. In medical diagnostic systems, errors can cause improper medical treatments which can result in the death of the patient. Artificial Intelligence (AI) can be applied in several healthcare processes to minimize the time and resources required in examining and diagnosing patients. In AI, machine learning has upsurged as an important technique in diagnosing heart disease. This paper showcases the current state-of-the-art techniques utilized in heart disease prediction. This paper proposes an architecture for heart disease prediction by using machine learning techniques along with Principal Component Analysis (PCA) for dimensionality reduction. It utilizes a standard UCI dataset of Kaggle having a rich set of attributes. Several standard machine learning techniques are utilized in the proposed architecture. The paper showcases the comparison of different machine learning algorithms for the detection of heart disease using standard parameters such as classification accuracy, precision, recall, an area under curve (AUC), F1 measure and ROC curve. It depicts that the Naive Bayes classifier outperforms for training without feature reduction and with feature reduction. However, Adaboost outperforms in testing in the proposed architecture.
{"title":"An Efficient Early Stage Heart Disease Risk Detection Using Machine Learning Techniques","authors":"Wesam Shishah","doi":"10.1109/ICPC2T53885.2022.9777070","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777070","url":null,"abstract":"In the medical field, early prediction of disease is a big challenge. This paper focuses on predicting heart disease at an early stage. Heart disease is a fatal human disease that rapidly increases at a global level. This disease affects both developed as well as undeveloped countries which subsequently causes death. In heart disease, the heart doesn't supply the required volume of blood to other body parts. It is essential to diagnose this disease at the early stage for preventing patients from higher damage. In medical diagnostic systems, errors can cause improper medical treatments which can result in the death of the patient. Artificial Intelligence (AI) can be applied in several healthcare processes to minimize the time and resources required in examining and diagnosing patients. In AI, machine learning has upsurged as an important technique in diagnosing heart disease. This paper showcases the current state-of-the-art techniques utilized in heart disease prediction. This paper proposes an architecture for heart disease prediction by using machine learning techniques along with Principal Component Analysis (PCA) for dimensionality reduction. It utilizes a standard UCI dataset of Kaggle having a rich set of attributes. Several standard machine learning techniques are utilized in the proposed architecture. The paper showcases the comparison of different machine learning algorithms for the detection of heart disease using standard parameters such as classification accuracy, precision, recall, an area under curve (AUC), F1 measure and ROC curve. It depicts that the Naive Bayes classifier outperforms for training without feature reduction and with feature reduction. However, Adaboost outperforms in testing in the proposed architecture.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776656
Samiksha Soni, Hardik N. Thakkar, B. Singh
Sickle cell disease is one of the most prevalent inherited blood disorders. The majority of the population suffering from this disorder are the active carrier of the disease (sickle cell trait) and are unaware of their health status. To have effective prevention of the spread of disease proper demarcation between disease and trait is required. The existing pathological methods for disease diagnosis are costly and time-consuming while most of the machine learning-based method focuses on normal versus abnormal cell classification. In this study transfer learning of pre-trained AlexNet model is proposed for classification of disease versus trait cases, a very first approach towards the sickle cell diseases subtype classification with the aid of machine learning and image processing tools. Also, the performance of the model is evaluated under various data division protocols, hold-out, 5-fold, 10-fold respectively. The study is conducted on a newly prepared database of 67 traits and 23 disease cases. The proposed system shows the highest classification accuracy of 95.5% with 10-fold data division protocol. Other performance parameters used for evaluation are precision, sensitivity, specificity, neg predicted value and ROC curve. In addition, the study examines a practical feature of the system by assessing it with fewer training samples. Also, the findings of the study suggest that transfer learning appears to be a helpful strategy when the availability of a medical dataset is restricted.
{"title":"Transfer Learning for Sickle Cell Anemia and Trait Classification","authors":"Samiksha Soni, Hardik N. Thakkar, B. Singh","doi":"10.1109/ICPC2T53885.2022.9776656","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776656","url":null,"abstract":"Sickle cell disease is one of the most prevalent inherited blood disorders. The majority of the population suffering from this disorder are the active carrier of the disease (sickle cell trait) and are unaware of their health status. To have effective prevention of the spread of disease proper demarcation between disease and trait is required. The existing pathological methods for disease diagnosis are costly and time-consuming while most of the machine learning-based method focuses on normal versus abnormal cell classification. In this study transfer learning of pre-trained AlexNet model is proposed for classification of disease versus trait cases, a very first approach towards the sickle cell diseases subtype classification with the aid of machine learning and image processing tools. Also, the performance of the model is evaluated under various data division protocols, hold-out, 5-fold, 10-fold respectively. The study is conducted on a newly prepared database of 67 traits and 23 disease cases. The proposed system shows the highest classification accuracy of 95.5% with 10-fold data division protocol. Other performance parameters used for evaluation are precision, sensitivity, specificity, neg predicted value and ROC curve. In addition, the study examines a practical feature of the system by assessing it with fewer training samples. Also, the findings of the study suggest that transfer learning appears to be a helpful strategy when the availability of a medical dataset is restricted.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121620955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776849
Nikita Chopde, M. Ekbote, Sampada Deshpande, Vijaya Kamble
Climate change is one of the biggest problems facing mankind. Increased flooding is one of the effects of climate change. Because floods are so severe, they can cause additional problems that can take only 24 hours to be seen in the affected areas. The paper deals with the extensive use of Deep Learning to identify flooded areas. Instead of using machine learning algorithms such as Decision Tree and Random Forest, a U-net architecture is used that will be able to locate and demarcate by doing classification on every pixel. The dataset consisted of VV and VH synthetic aperture radar (SAR) images which were converted to single RGB images. The dataset was augmented and an UNet model was created using the PyTorch library. The dataset was passed through six models which differed in number of epochs, learning rate and optimizer. Finally, the models were analyzed using cross entropy loss and MIOU.
{"title":"Flood Surveillance Using Deep Learning","authors":"Nikita Chopde, M. Ekbote, Sampada Deshpande, Vijaya Kamble","doi":"10.1109/ICPC2T53885.2022.9776849","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776849","url":null,"abstract":"Climate change is one of the biggest problems facing mankind. Increased flooding is one of the effects of climate change. Because floods are so severe, they can cause additional problems that can take only 24 hours to be seen in the affected areas. The paper deals with the extensive use of Deep Learning to identify flooded areas. Instead of using machine learning algorithms such as Decision Tree and Random Forest, a U-net architecture is used that will be able to locate and demarcate by doing classification on every pixel. The dataset consisted of VV and VH synthetic aperture radar (SAR) images which were converted to single RGB images. The dataset was augmented and an UNet model was created using the PyTorch library. The dataset was passed through six models which differed in number of epochs, learning rate and optimizer. Finally, the models were analyzed using cross entropy loss and MIOU.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114954230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776887
Kapu V Sri Ram Prasad, Varsha Singh
The polyphase induction motor (PIM) is widely used for domestic/industrial purposes, well known for its robust construction and performance. Due to continuous operation, wear, and tear these machines suffer from different faults. Early fault detection of these machines is important to avoid a breakdown that can increase the production of the entire sector. Nearly 10% of faults are related to broken rotor bars (BRB) of PIM. This paper proposes the flux distribution of healthy, one, and two BRB. The Finite Element Method (FEM) is used to design the stator (24 slots) and rotor bars (20), which are meshed in ALTAIR Flux software to predict the unbalanced forces due to the BRB. The machine is designed by building the geometry which is meshed with 54,177 nodes, 6032-line elements, and 27,054 surface elements. From the analysis, the torque and current of one and two BRB are progressively decreased with the increase in resistance. The inter bar current of BRB flows in the adjacent rotor bars increasing the current in healthy bars. These currents flow in the healthy bar for a long duration can deteriorate the healthy bar. The FEM provides an efficient method for the detection of flux orientation. The simulation results with high currents related to one and two BRB might enhance the fault isolation of adjacent rotor bars. A test machine is considered and MCSA is executed on PIM. The experimental results identify the BRB.
{"title":"Finite Element Analysis for Fault Diagnosis in Broken Rotor Bar of a Polyphase Induction Motor","authors":"Kapu V Sri Ram Prasad, Varsha Singh","doi":"10.1109/ICPC2T53885.2022.9776887","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776887","url":null,"abstract":"The polyphase induction motor (PIM) is widely used for domestic/industrial purposes, well known for its robust construction and performance. Due to continuous operation, wear, and tear these machines suffer from different faults. Early fault detection of these machines is important to avoid a breakdown that can increase the production of the entire sector. Nearly 10% of faults are related to broken rotor bars (BRB) of PIM. This paper proposes the flux distribution of healthy, one, and two BRB. The Finite Element Method (FEM) is used to design the stator (24 slots) and rotor bars (20), which are meshed in ALTAIR Flux software to predict the unbalanced forces due to the BRB. The machine is designed by building the geometry which is meshed with 54,177 nodes, 6032-line elements, and 27,054 surface elements. From the analysis, the torque and current of one and two BRB are progressively decreased with the increase in resistance. The inter bar current of BRB flows in the adjacent rotor bars increasing the current in healthy bars. These currents flow in the healthy bar for a long duration can deteriorate the healthy bar. The FEM provides an efficient method for the detection of flux orientation. The simulation results with high currents related to one and two BRB might enhance the fault isolation of adjacent rotor bars. A test machine is considered and MCSA is executed on PIM. The experimental results identify the BRB.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123207779","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}
An integrated method for harmonic reduction and reactive power correction is proposed based on an integrated thyristor-controlled reactor (TCR) and a shunt active power filter (SAPF). In order to control the TCR, a PI controller with a triggering alpha derived from linear firing was used. To regulate voltage and monitor current, a nonlinear APF control was developed. This paper focuses on a decoupled method of controlling, where the controlled system is divided into two loops, one fast and one slow. A linearization control was applied to the inner loop, while a nonlinear feedback control law was applied to the outer loop. Integrated compensators were introduced to both the current and voltage loops in order to eliminate steady-state errors caused by oscillation of system parameters. The total harmonic distortion of the source current is below the permissible limits imposed by several regulatory authorities, and the power factor of the supply is very close to unity. Simulated and experimental results indicate considerable reductions in harmonic distortions and compensation for reactive power.
{"title":"Harmonic Reduction and Reactive Power Improvement using Shunt Active Power Filter And Thyristor-Controlled Reactor","authors":"Vikram Singh, Shubhrata Gupta, Anamika Yadav, Jangili Rajashekar","doi":"10.1109/ICPC2T53885.2022.9777083","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777083","url":null,"abstract":"An integrated method for harmonic reduction and reactive power correction is proposed based on an integrated thyristor-controlled reactor (TCR) and a shunt active power filter (SAPF). In order to control the TCR, a PI controller with a triggering alpha derived from linear firing was used. To regulate voltage and monitor current, a nonlinear APF control was developed. This paper focuses on a decoupled method of controlling, where the controlled system is divided into two loops, one fast and one slow. A linearization control was applied to the inner loop, while a nonlinear feedback control law was applied to the outer loop. Integrated compensators were introduced to both the current and voltage loops in order to eliminate steady-state errors caused by oscillation of system parameters. The total harmonic distortion of the source current is below the permissible limits imposed by several regulatory authorities, and the power factor of the supply is very close to unity. Simulated and experimental results indicate considerable reductions in harmonic distortions and compensation for reactive power.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122854335","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}
In India, there is a severe shortage of trained healthcare professionals. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Cloud Computing, and 5G (together referred to as disruptive technologies) offer much potential for the Global Healthcare scenario. These disruptive technologies can improve doctors' efficiency and help alleviate the shortage of doctors. While these technologies brought many benefits to developed nations, their deployment in developing countries, especially India, may have challenges. Challenges go multiple folds because these technologies are still evolving and a significant segment of deployment of disruptive technologies is in early stages even in developed nations. This paper reviews the relevant literature and tries to explain the relevance of disruptive technologies for India. It assumes that learnings from developed and developing nations can be applied to India with some modifications. Through surveying existing literature, this paper looks at what Indian Healthcare is and presents factors affecting the deployment of disruptive technologies in Indian Healthcare.
{"title":"Factors Influencing Adoption of Disruptive Technologies in Healthcare in India: A Review","authors":"Sushim Shrivastava, Shalini Chandra, Muniza Askari","doi":"10.1109/ICPC2T53885.2022.9776945","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776945","url":null,"abstract":"In India, there is a severe shortage of trained healthcare professionals. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Cloud Computing, and 5G (together referred to as disruptive technologies) offer much potential for the Global Healthcare scenario. These disruptive technologies can improve doctors' efficiency and help alleviate the shortage of doctors. While these technologies brought many benefits to developed nations, their deployment in developing countries, especially India, may have challenges. Challenges go multiple folds because these technologies are still evolving and a significant segment of deployment of disruptive technologies is in early stages even in developed nations. This paper reviews the relevant literature and tries to explain the relevance of disruptive technologies for India. It assumes that learnings from developed and developing nations can be applied to India with some modifications. Through surveying existing literature, this paper looks at what Indian Healthcare is and presents factors affecting the deployment of disruptive technologies in Indian Healthcare.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123299300","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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