Pub Date : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752357
S. K. Verma, Aman Gupta, Ankita Jyoti
Weather forecasting has been a difficult problem for researchers for many years and continues to be today. The development of new and fast algorithms aids researchers in the pursuit of better weather forecast approximations. This problem attracts researchers because of the changing behavior of the environment, the increase in earth's temperature, and the drastic changes in ecosystem. Almost everywhere in the world is currently experiencing a slew of natural disasters, including storms on land and sea that are destroying infrastructure and taking the lives of many people. Machine learning and deep learning algorithms gave researchers and the general public hope that they would be able to develop fast applications and predict weather alarms in real time. Because of the combination of deep learning and the large amount of weather data that is available, researchers are motivated to investigate the hidden patterns of weather in forecasting. In this paper, the proposed model will be used to analyze intermediate variables, as well as variables associated with weather forecasting. Long Short-Term Model (LSTM) accuracy is affected by the number of layers in the model, as well as the number of layers in the stacked layer LSTM and the number of layers in Bidirectional LSTM. Because of the inclusion of an intermediate signal in the memory block, the methods proposed in this paper are an extended version of the LSTM. The premise is that two extremely connected patterns in the input dataset can rectify the input patterns and make it easier for the model to search for and recognize the pattern from the trained dataset by building a stronger connection between the patterns. In every trial, it is necessary to comprehend a long-lasting model for learning and to recognize the weather pattern. It makes use of predicted information such as visibility, as well as intermediate information such as temperature, pressure, humidity, and saturation, among other things. In bidirectional LSTM, the highest accuracy of 0.9355 and the lowest root mean square error of 0.0628 were achieved.
{"title":"Stack layer & Bidirectional Layer Long Short - Term Memory (LSTM) Time Series Model with Intermediate Variable for weather Prediction","authors":"S. K. Verma, Aman Gupta, Ankita Jyoti","doi":"10.1109/ComPE53109.2021.9752357","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752357","url":null,"abstract":"Weather forecasting has been a difficult problem for researchers for many years and continues to be today. The development of new and fast algorithms aids researchers in the pursuit of better weather forecast approximations. This problem attracts researchers because of the changing behavior of the environment, the increase in earth's temperature, and the drastic changes in ecosystem. Almost everywhere in the world is currently experiencing a slew of natural disasters, including storms on land and sea that are destroying infrastructure and taking the lives of many people. Machine learning and deep learning algorithms gave researchers and the general public hope that they would be able to develop fast applications and predict weather alarms in real time. Because of the combination of deep learning and the large amount of weather data that is available, researchers are motivated to investigate the hidden patterns of weather in forecasting. In this paper, the proposed model will be used to analyze intermediate variables, as well as variables associated with weather forecasting. Long Short-Term Model (LSTM) accuracy is affected by the number of layers in the model, as well as the number of layers in the stacked layer LSTM and the number of layers in Bidirectional LSTM. Because of the inclusion of an intermediate signal in the memory block, the methods proposed in this paper are an extended version of the LSTM. The premise is that two extremely connected patterns in the input dataset can rectify the input patterns and make it easier for the model to search for and recognize the pattern from the trained dataset by building a stronger connection between the patterns. In every trial, it is necessary to comprehend a long-lasting model for learning and to recognize the weather pattern. It makes use of predicted information such as visibility, as well as intermediate information such as temperature, pressure, humidity, and saturation, among other things. In bidirectional LSTM, the highest accuracy of 0.9355 and the lowest root mean square error of 0.0628 were achieved.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803217","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752079
K. Sreehari, M. Adham, Tom D Cheriya, Reshma Sheik
COVID-19, a disease produced by the SARS-CoV-2 virus, has had and continues to have a major influence on humankind. This pandemic has wreaked havoc on the global economy, pushing governments to take drastic steps to control its spread. Forecasting the growth of COVID-19 can assist healthcare providers, policymakers, manufacturers, and merchants predict the pandemic’s recurrence and the general public to have faith in the decisions made by them. Various existing findings showed that time-series techniques could learn and scale to properly anticipate how many people would be harmed by Covid-19 in the future. In this research, we did a comparative analysis of univariate time series models and multivariate time series models for confirming a better model at the end. As a result, we aim to bring out a time series model that is more suitable for forecasting the progression of pandemics worldwide, thus being a more reliable model. The research results showed that multivariate time series forecasting produced much better results for long-range than univariate time series models, which showed better results when expecting shorter periods.
{"title":"A Comparative Study between Univariate and Multivariate Time Series Models for COVID-19 Forecasting","authors":"K. Sreehari, M. Adham, Tom D Cheriya, Reshma Sheik","doi":"10.1109/ComPE53109.2021.9752079","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752079","url":null,"abstract":"COVID-19, a disease produced by the SARS-CoV-2 virus, has had and continues to have a major influence on humankind. This pandemic has wreaked havoc on the global economy, pushing governments to take drastic steps to control its spread. Forecasting the growth of COVID-19 can assist healthcare providers, policymakers, manufacturers, and merchants predict the pandemic’s recurrence and the general public to have faith in the decisions made by them. Various existing findings showed that time-series techniques could learn and scale to properly anticipate how many people would be harmed by Covid-19 in the future. In this research, we did a comparative analysis of univariate time series models and multivariate time series models for confirming a better model at the end. As a result, we aim to bring out a time series model that is more suitable for forecasting the progression of pandemics worldwide, thus being a more reliable model. The research results showed that multivariate time series forecasting produced much better results for long-range than univariate time series models, which showed better results when expecting shorter periods.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"3 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132969399","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}
Communication through Internet is common now a days. Type of communication, where one need to transfer any file became vulnerable due to penetration of unknown person in network to steal the important information, it was always challenging for researchers to send or received exact information on the network and also maintain the speed of transferring file. Preventing attackers from manipulating data and integrity validity is main objectives of this research paper. In first experiments SHA-256 were used to implements for quick and secure transfer of information, later in experiments it was found that MD5 Cryptographic hash function is more secure and faster. The main objective of this paper is to measure the performance of both algorithm and fusion of algorithm give better result and accuracy level also satisfactory as compare to other algorithms. Performance of the algorithms measured on the running time and complexity of MD5 and SHA-256. The complexity of the Algorithms MD5 and SHA-256 is that the same, i.e., θ(N), however the speed is of MD5 is better than SHA-256.
{"title":"iSIMP with Integrity Validation using MD5 Hash","authors":"Shiv Kumar Verma, Naved Anjum, Aryansh Sharma, Anubhav Mishra","doi":"10.1109/ComPE53109.2021.9752433","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752433","url":null,"abstract":"Communication through Internet is common now a days. Type of communication, where one need to transfer any file became vulnerable due to penetration of unknown person in network to steal the important information, it was always challenging for researchers to send or received exact information on the network and also maintain the speed of transferring file. Preventing attackers from manipulating data and integrity validity is main objectives of this research paper. In first experiments SHA-256 were used to implements for quick and secure transfer of information, later in experiments it was found that MD5 Cryptographic hash function is more secure and faster. The main objective of this paper is to measure the performance of both algorithm and fusion of algorithm give better result and accuracy level also satisfactory as compare to other algorithms. Performance of the algorithms measured on the running time and complexity of MD5 and SHA-256. The complexity of the Algorithms MD5 and SHA-256 is that the same, i.e., θ(N), however the speed is of MD5 is better than SHA-256.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"60 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113962337","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752170
V. Swapna, M. Gayatri
In recent years, Distributed Generation (DG) has created a great impact on the development of small-scale generation and green energy production. To meet the up-surging energy demand, renewable energy sources (RES) has integrated with the grid which helps in increasing generation capacity. The integration with the grid merely may not solve all the problems at all times. However, it will be effective only when the losses are minimized, uninterrupted and a reliable supply of power is present. On the other hand, Power quality (PQ) issues like harmonic distortion, voltage fluctuations, voltage flickering, power transients, etc., rise during integration. Monitoring and mitigating these power quality problems while integrating RES with the grid is the scope of research. This paper presents a comprehensive review of various power quality issues associated with the integration of DG units and their mitigation methods using different algorithms, control techniques, and devices.
{"title":"Power Quality Issues of Grid Integration of Distributed Generation: A Review","authors":"V. Swapna, M. Gayatri","doi":"10.1109/ComPE53109.2021.9752170","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752170","url":null,"abstract":"In recent years, Distributed Generation (DG) has created a great impact on the development of small-scale generation and green energy production. To meet the up-surging energy demand, renewable energy sources (RES) has integrated with the grid which helps in increasing generation capacity. The integration with the grid merely may not solve all the problems at all times. However, it will be effective only when the losses are minimized, uninterrupted and a reliable supply of power is present. On the other hand, Power quality (PQ) issues like harmonic distortion, voltage fluctuations, voltage flickering, power transients, etc., rise during integration. Monitoring and mitigating these power quality problems while integrating RES with the grid is the scope of research. This paper presents a comprehensive review of various power quality issues associated with the integration of DG units and their mitigation methods using different algorithms, control techniques, and devices.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115984750","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752118
Jaison Jacob Mathunny, V. Karthik, Ashokkumar Devaraj, S Hari Krishnan
Kinovea is a low-cost 2D motion analysis software. The main objective of this study is to examine the performance of Kinovea to compute spatial parameters related to perturbation training as it is not yet studied, allowing a less computational cost analysis of reactive stepping made possible. Thirty-eight healthy young adults performed the Lean and release perturbation technique to produce spatial parameters such as the Step length (SL), Step width (SW), and Leg angle (LA). The Intra-class Correlation Coefficient (ICC(3,1)) quantified the intra- and inter-rater reliability. The Standard Error of Measurement (SEM), SEM% and Minimal Detectable Change (MDC95) for the 95% confidence interval is estimated to find the absolute reliability. The Bland-Altman plot of intra-rater reliability is computed to find the limit of agreement. The results suggest that the Kinovea software is a reliable tool that can produce original values with acceptable error while repeating the analysis. Kinovea software’s excellent ICC(3,1) value, absolute reliability, and low MDC95 value suggests that the software may be a valuable tool to analyze and provide a reliable output related to perturbation training.
{"title":"Reliability of Kinovea software in measuring spatial parameters associated with perturbation training","authors":"Jaison Jacob Mathunny, V. Karthik, Ashokkumar Devaraj, S Hari Krishnan","doi":"10.1109/ComPE53109.2021.9752118","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752118","url":null,"abstract":"Kinovea is a low-cost 2D motion analysis software. The main objective of this study is to examine the performance of Kinovea to compute spatial parameters related to perturbation training as it is not yet studied, allowing a less computational cost analysis of reactive stepping made possible. Thirty-eight healthy young adults performed the Lean and release perturbation technique to produce spatial parameters such as the Step length (SL), Step width (SW), and Leg angle (LA). The Intra-class Correlation Coefficient (ICC(3,1)) quantified the intra- and inter-rater reliability. The Standard Error of Measurement (SEM), SEM% and Minimal Detectable Change (MDC95) for the 95% confidence interval is estimated to find the absolute reliability. The Bland-Altman plot of intra-rater reliability is computed to find the limit of agreement. The results suggest that the Kinovea software is a reliable tool that can produce original values with acceptable error while repeating the analysis. Kinovea software’s excellent ICC(3,1) value, absolute reliability, and low MDC95 value suggests that the software may be a valuable tool to analyze and provide a reliable output related to perturbation training.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116839999","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752416
J. D. Louis Lovenia Karunya, D. Darling Jemima, R. Raghhul, Eugene Kingsley
The Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the severe acute respiratory syndrome. The disease was first instigated in December 2019 in the place called Wuhan which is the capital of a province in China named Hubei and meanwhile, it has spread universally throughout the world. The impact is greatly influenced so that World Health Organization (WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency. Artificial intelligence renders its support to analyze chest X-ray (CXR) images for COVID-19 diagnosis. This proposed system is aimed to automatically detect COVID-19 patients using digital chest x- ray images while increasing the accuracy of the model tried with different convolution layers. The dataset was created as a mixture of publicly available X-ray images from patients with confirmed COVID-19 disease and healthy folks. To alleviate the small number of samples, we have inculcated many data augmentation techniques that further enhance the accuracy of the model with different convolution layers. The research aims to design a Deep Learning based model for Covid 19 prediction through X-Ray images. The parameters chosen are applied over 3 different models designed by varying Convolution Layers and proved that accuracy enhances when number of layers increases.
{"title":"Comparative Study on Detection of COVID-19 using different Convolution Layers","authors":"J. D. Louis Lovenia Karunya, D. Darling Jemima, R. Raghhul, Eugene Kingsley","doi":"10.1109/ComPE53109.2021.9752416","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752416","url":null,"abstract":"The Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the severe acute respiratory syndrome. The disease was first instigated in December 2019 in the place called Wuhan which is the capital of a province in China named Hubei and meanwhile, it has spread universally throughout the world. The impact is greatly influenced so that World Health Organization (WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency. Artificial intelligence renders its support to analyze chest X-ray (CXR) images for COVID-19 diagnosis. This proposed system is aimed to automatically detect COVID-19 patients using digital chest x- ray images while increasing the accuracy of the model tried with different convolution layers. The dataset was created as a mixture of publicly available X-ray images from patients with confirmed COVID-19 disease and healthy folks. To alleviate the small number of samples, we have inculcated many data augmentation techniques that further enhance the accuracy of the model with different convolution layers. The research aims to design a Deep Learning based model for Covid 19 prediction through X-Ray images. The parameters chosen are applied over 3 different models designed by varying Convolution Layers and proved that accuracy enhances when number of layers increases.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574488","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751950
Jagriti Jagriti, D. K. Lobiyal
Vehicular Adhoc Networks (VANET) is the fundamental unit for intelligent transportation systems (ITS) controlling and real-time monitoring the Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. Due to wireless communication channels, vehicles and Roadside Units (RSU) are susceptible to various network attacks, especially security and privacy. Many existing schemes focused on message authentication but did not respond quickly, hence inefficient. There is a great demand to develop an efficient and anonymous authentication protocol to ensure secure communication between different vehicles without revealing their identities. To address these issues, this article presents a lightweight anonymous authentication key agreement scheme between vehicles and RSUs using smart cards and biometrics only. In addition, authentication involves only two entities, at a time, without involving the Registration Centre. The proposed scheme bypasses costly operations like bilinear pairing and multiplication on an elliptic curve instead uses lightweight operations hash function and exclusive-OR operations to ensure the scheme is computationally efficient. Along with all the known attacks, the proposed scheme attains forward secrecy, detects unauthorized login quickly, and revokes smart cards.
{"title":"An Efficient and Anonymous Authentication Key Agreement Protocol for Smart Transportation System","authors":"Jagriti Jagriti, D. K. Lobiyal","doi":"10.1109/ComPE53109.2021.9751950","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751950","url":null,"abstract":"Vehicular Adhoc Networks (VANET) is the fundamental unit for intelligent transportation systems (ITS) controlling and real-time monitoring the Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. Due to wireless communication channels, vehicles and Roadside Units (RSU) are susceptible to various network attacks, especially security and privacy. Many existing schemes focused on message authentication but did not respond quickly, hence inefficient. There is a great demand to develop an efficient and anonymous authentication protocol to ensure secure communication between different vehicles without revealing their identities. To address these issues, this article presents a lightweight anonymous authentication key agreement scheme between vehicles and RSUs using smart cards and biometrics only. In addition, authentication involves only two entities, at a time, without involving the Registration Centre. The proposed scheme bypasses costly operations like bilinear pairing and multiplication on an elliptic curve instead uses lightweight operations hash function and exclusive-OR operations to ensure the scheme is computationally efficient. Along with all the known attacks, the proposed scheme attains forward secrecy, detects unauthorized login quickly, and revokes smart cards.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114338405","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752005
Naga Praveen Babu Mannam, Basa Sidvik, P. Rajalakshmi
Extra-terrestrial space exploration projects have been a matter of interest with many space agencies given the possibility of non – Earth life forms, life-nurturing environments, or the presence of valuable minerals within our solar system. The present study conducts an aerodynamic analysis on a submarine body capable of examining the extra-terrestrial seas of Titan, which is expected to harbor life forms using CFD. Computational Fluid Dynamics or CFD is used to analyze the aerodynamic properties of the submarine. The present case is set up by enabling the liquid properties like that of Titan's seas with submarine deeply submerged inside the lake moving at a velocity of 1 – 3 m/s. The average drag coefficient observed for the L/D ratios 10.8, 12.5, and 14.4 is 0.059, 0.067, and 0.072, respectively, for the assumed geometry when the submarine is submerged deep inside the seas.
{"title":"Hydrodynamic Analysis of Extra-Terrestrial Submarine in Lakes of Titan using CFD","authors":"Naga Praveen Babu Mannam, Basa Sidvik, P. Rajalakshmi","doi":"10.1109/ComPE53109.2021.9752005","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752005","url":null,"abstract":"Extra-terrestrial space exploration projects have been a matter of interest with many space agencies given the possibility of non – Earth life forms, life-nurturing environments, or the presence of valuable minerals within our solar system. The present study conducts an aerodynamic analysis on a submarine body capable of examining the extra-terrestrial seas of Titan, which is expected to harbor life forms using CFD. Computational Fluid Dynamics or CFD is used to analyze the aerodynamic properties of the submarine. The present case is set up by enabling the liquid properties like that of Titan's seas with submarine deeply submerged inside the lake moving at a velocity of 1 – 3 m/s. The average drag coefficient observed for the L/D ratios 10.8, 12.5, and 14.4 is 0.059, 0.067, and 0.072, respectively, for the assumed geometry when the submarine is submerged deep inside the seas.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115774798","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752210
Swapnil Banerjee, R. Manikandan, R. R. Singh, Harshit Mohan
Power electronic converters are essential in the conditioning of electrical energy in a wide range of applications. To enhance the reliability of these power converters, different condition monitoring approaches are adapted that can analyze the health condition of power converters in real-time. The Intelligent Health Surveillance and IoT fault diagnosis system has recently gained attention due to its readiness and operational flexibility. This article proposes an intelligent fault diagnostic algorithm for an IoT-integrated three-phase two-level voltage converter. The proposed fault diagnosis scheme detects single and double open circuit switch faults using simple Park’s vector-based technique. The normalized mean value and the absolute average value of phase currents are used as fault indicators in the proposed scheme. Obtained real-time fault data is further processed in cloud platforms for long-term storage and analysis. An application is developed for the acquisition and visualization of fault data, as well as to receive an alert message in their registered e-mail account.
{"title":"IoT enabled Intelligent Fault Diagnosis System for Two-level Voltage Source Converter","authors":"Swapnil Banerjee, R. Manikandan, R. R. Singh, Harshit Mohan","doi":"10.1109/ComPE53109.2021.9752210","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752210","url":null,"abstract":"Power electronic converters are essential in the conditioning of electrical energy in a wide range of applications. To enhance the reliability of these power converters, different condition monitoring approaches are adapted that can analyze the health condition of power converters in real-time. The Intelligent Health Surveillance and IoT fault diagnosis system has recently gained attention due to its readiness and operational flexibility. This article proposes an intelligent fault diagnostic algorithm for an IoT-integrated three-phase two-level voltage converter. The proposed fault diagnosis scheme detects single and double open circuit switch faults using simple Park’s vector-based technique. The normalized mean value and the absolute average value of phase currents are used as fault indicators in the proposed scheme. Obtained real-time fault data is further processed in cloud platforms for long-term storage and analysis. An application is developed for the acquisition and visualization of fault data, as well as to receive an alert message in their registered e-mail account.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804222","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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