Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847791
Ashish Kumar, Sudhanshu K. Mishra, Ayush Kejriwal
In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets.
{"title":"Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm","authors":"Ashish Kumar, Sudhanshu K. Mishra, Ayush Kejriwal","doi":"10.1109/CONIT55038.2022.9847791","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847791","url":null,"abstract":"In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124929079","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848014
Mukul Soni, Mayank Singhal, Jatin, R. Katarya
Owing to ongoing rapid developments in network related technologies combined with the great surge in their usage, the methodologies for cyber-attacks like intrusions are also constantly modernizing leading to a greater rate of accuracy, effect and frequency of such network-related issues. In this research exercise, we establish an innovative and efficient methodology for Deep Learning-based solutions for Intrusion detection. To establish this, we propose a Deep Neural Network (DNN) trained by an Enhanced Artificial Bee Colony Algorithm for efficient and accurate intrusion detection over wireless and interconnected environments. This research effort constitutes a holistic and comparative analysis of the complete functionality and technicality of the proposed system. The proposed model performed much better than many other state-of-the-art models. Furthermore, the comprehensive explanation provided by this research can be leveraged into the development of more precocious and modern Intrusion Detection System.
{"title":"Optimizing Deep Neural Network using Enhanced Artificial Bee Colony Algorithm for an Efficient Intrusion Detection System","authors":"Mukul Soni, Mayank Singhal, Jatin, R. Katarya","doi":"10.1109/CONIT55038.2022.9848014","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848014","url":null,"abstract":"Owing to ongoing rapid developments in network related technologies combined with the great surge in their usage, the methodologies for cyber-attacks like intrusions are also constantly modernizing leading to a greater rate of accuracy, effect and frequency of such network-related issues. In this research exercise, we establish an innovative and efficient methodology for Deep Learning-based solutions for Intrusion detection. To establish this, we propose a Deep Neural Network (DNN) trained by an Enhanced Artificial Bee Colony Algorithm for efficient and accurate intrusion detection over wireless and interconnected environments. This research effort constitutes a holistic and comparative analysis of the complete functionality and technicality of the proposed system. The proposed model performed much better than many other state-of-the-art models. Furthermore, the comprehensive explanation provided by this research can be leveraged into the development of more precocious and modern Intrusion Detection System.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043863","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848119
S.Rakesh Kumar, Shashank Swaroop
Brain tumor is one of life threatening diseases for humans and the treatment is challenging. Recently the disease diagnosis industry is seeing enormous developments. Brain tumors can be identified from Magnetic Resonance Imaging (MRI) images. There are existing techniques available for brain tumor detection using image processing techniques. Some recent studies used machine learning approaches for brain tumor detection. However, an effective model and application is required for this life threatening disease. Availability of dataset is an added advantage for these studies. Nowadays, large amounts of data can be preserved for research and these can be used effectively by deep learning models. Disease diagnosis through deep learning techniques are emerging these days. In this paper, brain tumor detection is proposed through a deep learning model, Convolutional Neural Network (CNN). Deep learning models are achieving good results on brain tumor detection. In this work, an application is proposed, in which users can upload the MRI image and detect whether it is a tumor or normal MRI. CNN based classification for brain tumor detection has achieved highest classification accuracy around 99.5%. Experimental results showed that high precision value 99.3% for optimized training epochs.
{"title":"An Effective Application to Identify Brain Tumor using Deep Learning Model","authors":"S.Rakesh Kumar, Shashank Swaroop","doi":"10.1109/CONIT55038.2022.9848119","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848119","url":null,"abstract":"Brain tumor is one of life threatening diseases for humans and the treatment is challenging. Recently the disease diagnosis industry is seeing enormous developments. Brain tumors can be identified from Magnetic Resonance Imaging (MRI) images. There are existing techniques available for brain tumor detection using image processing techniques. Some recent studies used machine learning approaches for brain tumor detection. However, an effective model and application is required for this life threatening disease. Availability of dataset is an added advantage for these studies. Nowadays, large amounts of data can be preserved for research and these can be used effectively by deep learning models. Disease diagnosis through deep learning techniques are emerging these days. In this paper, brain tumor detection is proposed through a deep learning model, Convolutional Neural Network (CNN). Deep learning models are achieving good results on brain tumor detection. In this work, an application is proposed, in which users can upload the MRI image and detect whether it is a tumor or normal MRI. CNN based classification for brain tumor detection has achieved highest classification accuracy around 99.5%. Experimental results showed that high precision value 99.3% for optimized training epochs.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132986501","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847754
Jai Garg, Jatin Papreja, Kumar Apurva, Goonjan Jain
Effective and efficient grading has been recognized as an important issue in any educational institution. In this study, a grading system involving BERT for Automatic Short Answer Grading (ASAG) is proposed. A BERT Regressor model is fine-tuned using a domain-specific ASAG dataset to achieve a baseline performance. In order to improve the final grading performance, an effective strategy is proposed involving careful integration of BERT Regressor model with Semantic Text Similarity. A set of experiments is conducted to test the performance of the proposed method. Two performance metrics namely: Pearson's Correlation Coefficient and Root Mean Squared Error are used for evaluation purposes. The results obtained highlights the usefulness of proposed system for domain specific ASAG tasks in real life.
{"title":"Domain-Specific Hybrid BERT based System for Automatic Short Answer Grading","authors":"Jai Garg, Jatin Papreja, Kumar Apurva, Goonjan Jain","doi":"10.1109/CONIT55038.2022.9847754","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847754","url":null,"abstract":"Effective and efficient grading has been recognized as an important issue in any educational institution. In this study, a grading system involving BERT for Automatic Short Answer Grading (ASAG) is proposed. A BERT Regressor model is fine-tuned using a domain-specific ASAG dataset to achieve a baseline performance. In order to improve the final grading performance, an effective strategy is proposed involving careful integration of BERT Regressor model with Semantic Text Similarity. A set of experiments is conducted to test the performance of the proposed method. Two performance metrics namely: Pearson's Correlation Coefficient and Root Mean Squared Error are used for evaluation purposes. The results obtained highlights the usefulness of proposed system for domain specific ASAG tasks in real life.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133659298","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848285
Yatharth Saxena, Nirdesh Mishra, M. Sameer, Pankaj Dahiya
Edge detection is substantial in helping us to pre-process any image for various applications from helping us to detect objects to detecting various medical conditions. The paper tackled one major shortcoming with the currently present system which is edge thickness. To improve there is an implementation of multiple thresholds instead of two thresholds generally used by techniques like that in Canny. The selected method solves multiple problems perfecting the handling of errors and more real to truth results. Our aim of refining the method helps us in better edge detection in images with low contrast as well as medical images like MRIs and X-rays.
{"title":"Improved Edge Detection Approach to Tackle Edge Thickness and Better Edge Connectivity","authors":"Yatharth Saxena, Nirdesh Mishra, M. Sameer, Pankaj Dahiya","doi":"10.1109/CONIT55038.2022.9848285","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848285","url":null,"abstract":"Edge detection is substantial in helping us to pre-process any image for various applications from helping us to detect objects to detecting various medical conditions. The paper tackled one major shortcoming with the currently present system which is edge thickness. To improve there is an implementation of multiple thresholds instead of two thresholds generally used by techniques like that in Canny. The selected method solves multiple problems perfecting the handling of errors and more real to truth results. Our aim of refining the method helps us in better edge detection in images with low contrast as well as medical images like MRIs and X-rays.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122242648","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847920
Padam Dhar Dwivedi, Ariiit Baral, S. Dutta
The Suspension insulator is an indispensable component in a power system network. With the increasing electricity demand, it's the utility's responsibility to provide reliable power to the consumer. Thus, condition monitoring of overhead insulators is necessary because contaminants present in the environment cause insulation flashover and affect the power system operation. In the current work, an 11kV porcelain disc insulator is used, artificially contaminated. After that, Detrended Fluctuation Analysis (DFA) and Harmonic Ratio method are applied to estimate the contamination level using surface leakage current data.
{"title":"Harmonic Ratio and Detrended Fluctuation Analysis Aided Reliable Estimation of contamination Level On Outdoor Suspension Insulators","authors":"Padam Dhar Dwivedi, Ariiit Baral, S. Dutta","doi":"10.1109/CONIT55038.2022.9847920","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847920","url":null,"abstract":"The Suspension insulator is an indispensable component in a power system network. With the increasing electricity demand, it's the utility's responsibility to provide reliable power to the consumer. Thus, condition monitoring of overhead insulators is necessary because contaminants present in the environment cause insulation flashover and affect the power system operation. In the current work, an 11kV porcelain disc insulator is used, artificially contaminated. After that, Detrended Fluctuation Analysis (DFA) and Harmonic Ratio method are applied to estimate the contamination level using surface leakage current data.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122357216","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848266
S. Sanjay, S. Soorya, R. Vengatesh, K. C. S. H. Priya
COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization.
{"title":"Security Access Control System Enhanced with Face Mask Detection and Temperature Monitoring for Pandemic Trauma","authors":"S. Sanjay, S. Soorya, R. Vengatesh, K. C. S. H. Priya","doi":"10.1109/CONIT55038.2022.9848266","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848266","url":null,"abstract":"COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121041588","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847677
Ravi Nandan Ray, M. M. Tripathi, Chaudhary Indra Kumar
This paper presents an energy efficient voltage CMOS voltage level shifter. Voltage level shifter is used for multi-supply design applications. The main purpose of voltage level shifter is to convert the voltage level from one level to another. We verified our voltage level shifter in ASAP7 7nm Fin-Fet technology. The proposed voltage level shifter is based on differential cascade voltage switch logic, which takes an input voltage in the range of 0.25V to 0.6V and provides an output of 0.7V. Our voltage level shifter improves propagation delay and power dissipation with 48% and 43%, respectively, with recently reported Wilson current mirror voltage level shifter with Zero-Vth design. The proposed design technique comes up with significantly lower power consumption and drastically reduced propagation delay over a wide range of temperatures (-25 to 25 degree Celsius), as compared to existing technologies.
{"title":"High Performance Energy Efficient CMOS Voltage Level Shifter Design","authors":"Ravi Nandan Ray, M. M. Tripathi, Chaudhary Indra Kumar","doi":"10.1109/CONIT55038.2022.9847677","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847677","url":null,"abstract":"This paper presents an energy efficient voltage CMOS voltage level shifter. Voltage level shifter is used for multi-supply design applications. The main purpose of voltage level shifter is to convert the voltage level from one level to another. We verified our voltage level shifter in ASAP7 7nm Fin-Fet technology. The proposed voltage level shifter is based on differential cascade voltage switch logic, which takes an input voltage in the range of 0.25V to 0.6V and provides an output of 0.7V. Our voltage level shifter improves propagation delay and power dissipation with 48% and 43%, respectively, with recently reported Wilson current mirror voltage level shifter with Zero-Vth design. The proposed design technique comes up with significantly lower power consumption and drastically reduced propagation delay over a wide range of temperatures (-25 to 25 degree Celsius), as compared to existing technologies.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114959286","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-vehicle Human-Machine Interface (HMI) plays a significant role for conditionally automated vehicles in realizing effective communications from driving automation systems to drivers during either automated driving period or control transitions. The present study aimed to investigate the effects of in-vehicle HMI on drivers' eye-tracking characteristics pre and post takeover request (TOR). A driving simulator-based experiment was conducted comparing the differences of drivers' visual behaviors with or without HMI under two TB (time budget) conditions (TB = 4 s; TB = 10 s). The visual HMI adopted in the experiments consisted of vehicle status display and a bird-view depiction of the traffic situation. Experiment results showed fixations prior to the TOR were more frequently shifted from real traffic situation to HMI which was effective in indirectly maintaining drivers' mode and situation awareness. Pre TOR entropy measures indicated a more dispersed but still ordered scanning pattern in spatial sampling. Saccadic behaviors were shown to be encouraged for a less cognitively demanded but a more visually loaded acquisition of surrounding information with the assistance of HMI. Post TOR fixation measure showed a prolonged Eyes-on-Traffic-Time (EoTT) when HMI was provided. And as a physiological indicator for mental workload, blink rate and blink latency did not show an additional increase after the issue of TOR under “with HMI” condition. We conclude that the introduction of in-vehicle visual HMI can be a valid option to support drivers in both automated driving and takeover time.
{"title":"Research on the Effects of in-Vehicle Human-Machine Interface on Drivers' Pre and Post Takeover Request Eye-tracking Characteristics","authors":"Weimin Liu, Qingkun Li, Zhenyuan Wang, Wenjun Wang, Chao Zeng, Bo Cheng","doi":"10.1109/CONIT55038.2022.9848040","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848040","url":null,"abstract":"In-vehicle Human-Machine Interface (HMI) plays a significant role for conditionally automated vehicles in realizing effective communications from driving automation systems to drivers during either automated driving period or control transitions. The present study aimed to investigate the effects of in-vehicle HMI on drivers' eye-tracking characteristics pre and post takeover request (TOR). A driving simulator-based experiment was conducted comparing the differences of drivers' visual behaviors with or without HMI under two TB (time budget) conditions (TB = 4 s; TB = 10 s). The visual HMI adopted in the experiments consisted of vehicle status display and a bird-view depiction of the traffic situation. Experiment results showed fixations prior to the TOR were more frequently shifted from real traffic situation to HMI which was effective in indirectly maintaining drivers' mode and situation awareness. Pre TOR entropy measures indicated a more dispersed but still ordered scanning pattern in spatial sampling. Saccadic behaviors were shown to be encouraged for a less cognitively demanded but a more visually loaded acquisition of surrounding information with the assistance of HMI. Post TOR fixation measure showed a prolonged Eyes-on-Traffic-Time (EoTT) when HMI was provided. And as a physiological indicator for mental workload, blink rate and blink latency did not show an additional increase after the issue of TOR under “with HMI” condition. We conclude that the introduction of in-vehicle visual HMI can be a valid option to support drivers in both automated driving and takeover time.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"421 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169778","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847816
Nirzari Vora, Siddharth Joshi, Darshan Patel
In today's time, fuel price and shortage of conventional sources like coal are the biggest concern worldwide. Henceforth, world is moving towards adapting green energy i.e. renewable energy for the production of the electricity. Renewable sources are available in nature. One can harness in the forms of the solar energy, the wind energy, the tidal energy, the biomass energy, the geothermal energy etc. These sources are environment friendly and clean to use to produce electricity. One issue which has to be addressed while using these sources is that they are weather and location dependent. So reliability on these sources alone is less which leads to combining other source, be it conventional or other renewable sources. This combination of two or more sources to generate power is called hybrid system and in this paper, we are considering PVES (Photo-Voltaic Energy System) as main source and BESS (Battery Energy Storage System) for storage purpose. The simulations studies and analysis for the parallel & standalone Operation of PVES and BESS is performed and proposed in this paper. The system is used for the DC microgrid applications. The MATLAB simulation analysis is done by varying climatic conditions i.e. change in insolation and change in temperature.
{"title":"Analysis of the Parallel & Standalone Operation of PVES and BESS for Microgrid Applications with Varying Climatic Condition","authors":"Nirzari Vora, Siddharth Joshi, Darshan Patel","doi":"10.1109/CONIT55038.2022.9847816","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847816","url":null,"abstract":"In today's time, fuel price and shortage of conventional sources like coal are the biggest concern worldwide. Henceforth, world is moving towards adapting green energy i.e. renewable energy for the production of the electricity. Renewable sources are available in nature. One can harness in the forms of the solar energy, the wind energy, the tidal energy, the biomass energy, the geothermal energy etc. These sources are environment friendly and clean to use to produce electricity. One issue which has to be addressed while using these sources is that they are weather and location dependent. So reliability on these sources alone is less which leads to combining other source, be it conventional or other renewable sources. This combination of two or more sources to generate power is called hybrid system and in this paper, we are considering PVES (Photo-Voltaic Energy System) as main source and BESS (Battery Energy Storage System) for storage purpose. The simulations studies and analysis for the parallel & standalone Operation of PVES and BESS is performed and proposed in this paper. The system is used for the DC microgrid applications. The MATLAB simulation analysis is done by varying climatic conditions i.e. change in insolation and change in temperature.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117054203","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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