Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847915
Priyanka A. Wagh, Sushil Karvekar
Integrating solar panels in DC micro grid is a big task, since it involves a battery for power storage and battery controller. The most tedious task is enhancement of battery life and efficiency. This paper introduces two separate models of DC micro grid, one regulated by fuzzy logic controller and the other one by a conventional PID controller. The fuzzy logic controller is implemented in MATLAB Simulink based upon Mamdani inference system. MPPT, fuzzy logic controller, lithium-ion battery and DC load are all integrated in one Simulink model. Best possible rule base is developed to regulate power flow through the DC micro grid, thus simultaneously enhancing the performance of lithium-ion battery. The fuzzy logic controller facilitates maintenance of SOC of lithium-ion battery within desired limits, which results in prevention of overcharging and over discharging. Also, conventional PID controller is implemented in MATLAB Simulink for maintaining SOC of lithium-ion battery within desired limits. This model involves integration of PID controller, MPPT, bi-directional DC-DC converter and lithium-ion battery. Based upon the obtained performance and results of both Simulink models, a comparison between the two controllers is deduced and corresponding results are analyzed. Transient response analysis is performed to compare the two controllers.
{"title":"SOC Control of Lithium–Ion Battery Using Fuzzy Logic Controller and PID Controller Employed in DC Micro Grid","authors":"Priyanka A. Wagh, Sushil Karvekar","doi":"10.1109/CONIT55038.2022.9847915","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847915","url":null,"abstract":"Integrating solar panels in DC micro grid is a big task, since it involves a battery for power storage and battery controller. The most tedious task is enhancement of battery life and efficiency. This paper introduces two separate models of DC micro grid, one regulated by fuzzy logic controller and the other one by a conventional PID controller. The fuzzy logic controller is implemented in MATLAB Simulink based upon Mamdani inference system. MPPT, fuzzy logic controller, lithium-ion battery and DC load are all integrated in one Simulink model. Best possible rule base is developed to regulate power flow through the DC micro grid, thus simultaneously enhancing the performance of lithium-ion battery. The fuzzy logic controller facilitates maintenance of SOC of lithium-ion battery within desired limits, which results in prevention of overcharging and over discharging. Also, conventional PID controller is implemented in MATLAB Simulink for maintaining SOC of lithium-ion battery within desired limits. This model involves integration of PID controller, MPPT, bi-directional DC-DC converter and lithium-ion battery. Based upon the obtained performance and results of both Simulink models, a comparison between the two controllers is deduced and corresponding results are analyzed. Transient response analysis is performed to compare the two controllers.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"41 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":"114973863","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/CONIT55038.2022.9847705
Anni.U. Gupta, Sarita Singh Bhadauria
Medical image segmentation is a strategy for extricating the ideal parts and highlights from the info medical image information. The presentation of classification stage depends on initial stages like preprocessing and segmentation. The traditional Fuzzy c-means (FCM) clustering algorithms have been generally utilized for grayscale and color image segmentation. In this work, we propose a super-pixel based FCM clustering algorithm that is altogether more hearty than best in clustering algorithms for image segmentation. We initially acquire a preprocessing stage by super-pixel image with exact contour for background separation. As opposed to customary neighboring window of fixed size and shape, the super-pixel image gives better adaptive and irregular local spatial neigh-borhoods that are helpful for improving Interstitial lung disease (ILD) image segmentation. Also after that the results are compared with preprocessing performed by adaptive median filtering to stay away from the noise effect on ILD images followed by Contrast-limited adaptive histogram equalization (CLAHE) enhancement to improve the image quality and then segmented by FCM. The outcomes are obtained for various number of clusters segmented with FCM with super-pixel approach and result are improve as contrast to conventional FCM and Otsu method on ILD images.
{"title":"FCM with Super-Pixel Approach for Interstitial Lung Disease Image Processing","authors":"Anni.U. Gupta, Sarita Singh Bhadauria","doi":"10.1109/CONIT55038.2022.9847705","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847705","url":null,"abstract":"Medical image segmentation is a strategy for extricating the ideal parts and highlights from the info medical image information. The presentation of classification stage depends on initial stages like preprocessing and segmentation. The traditional Fuzzy c-means (FCM) clustering algorithms have been generally utilized for grayscale and color image segmentation. In this work, we propose a super-pixel based FCM clustering algorithm that is altogether more hearty than best in clustering algorithms for image segmentation. We initially acquire a preprocessing stage by super-pixel image with exact contour for background separation. As opposed to customary neighboring window of fixed size and shape, the super-pixel image gives better adaptive and irregular local spatial neigh-borhoods that are helpful for improving Interstitial lung disease (ILD) image segmentation. Also after that the results are compared with preprocessing performed by adaptive median filtering to stay away from the noise effect on ILD images followed by Contrast-limited adaptive histogram equalization (CLAHE) enhancement to improve the image quality and then segmented by FCM. The outcomes are obtained for various number of clusters segmented with FCM with super-pixel approach and result are improve as contrast to conventional FCM and Otsu method on ILD images.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"89 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":"115224821","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/CONIT55038.2022.9847948
S. R, C. N
Scheduling the resources is a fundamental part of the cloud environment. The facilitation of an efficient scheduling approach is a challenging issue. An ideal resource allocation framework that utilizes square-fuzzy methodology and radial basis function network (RBFN) is discussed here. The main motive of this model is to reduce communication and computation costs. The sensor devices in the IoT sensor layer are clustered initially. Further, the sensor data gathered by cluster heads are transferred to the fog layer. The network traffic is optimized through the fuzzy technique. The energy consumption of the network is reduced by the fog layer and then the data is transferred to the cloud where essential attributes of the cloud server and input are used for scheduling of resources by utilizing modified RBFN. The proposed methodology is analyzed and compared with existing models.
{"title":"A framework for IoT-based Resource Scheduling in the Cloud","authors":"S. R, C. N","doi":"10.1109/CONIT55038.2022.9847948","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847948","url":null,"abstract":"Scheduling the resources is a fundamental part of the cloud environment. The facilitation of an efficient scheduling approach is a challenging issue. An ideal resource allocation framework that utilizes square-fuzzy methodology and radial basis function network (RBFN) is discussed here. The main motive of this model is to reduce communication and computation costs. The sensor devices in the IoT sensor layer are clustered initially. Further, the sensor data gathered by cluster heads are transferred to the fog layer. The network traffic is optimized through the fuzzy technique. The energy consumption of the network is reduced by the fog layer and then the data is transferred to the cloud where essential attributes of the cloud server and input are used for scheduling of resources by utilizing modified RBFN. The proposed methodology is analyzed and compared with existing models.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"13 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":"124460157","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/CONIT55038.2022.9847918
Himanshu Sharma, Swati Srivastava
In Computer Vision, image captioning is one of the most fascinating topics. Image captioning simply says generating sentences from a given picture. The process of generating captions from images includes two processes. One of them is image processing and the other one is natural language processing. In this, we use CNN that is a Convolution neural network, and RNN is a recurrent neural network. We use CNN for drawing out properties from the given picture and RNN for creating language. Though, the process is a bit complex and difficult, though we have put in a good amount of time in planning and research for the contentment of every attribute of it. The paper contains a comprehensive study of different models that are used for image captioning. Models that we will be discussing will be the Retrieval-Based Model, Template-Based Model, CNN-RNN based model, and CNN-CNN Model.
{"title":"Image Captioning: Methods and Evaluation Metrics","authors":"Himanshu Sharma, Swati Srivastava","doi":"10.1109/CONIT55038.2022.9847918","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847918","url":null,"abstract":"In Computer Vision, image captioning is one of the most fascinating topics. Image captioning simply says generating sentences from a given picture. The process of generating captions from images includes two processes. One of them is image processing and the other one is natural language processing. In this, we use CNN that is a Convolution neural network, and RNN is a recurrent neural network. We use CNN for drawing out properties from the given picture and RNN for creating language. Though, the process is a bit complex and difficult, though we have put in a good amount of time in planning and research for the contentment of every attribute of it. The paper contains a comprehensive study of different models that are used for image captioning. Models that we will be discussing will be the Retrieval-Based Model, Template-Based Model, CNN-RNN based model, and CNN-CNN Model.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"120 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":"116867535","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/CONIT55038.2022.9848122
Mayank Kaushik, Ashish Rawat, Varsha Sisaudia, Love Parashar
Shoulder surfing is an easy attack, where an unauthorized user tries to gain access to sensitive information. Unfortunately, none of the currently available content-based shoulder-surfing approaches for protected graphical concealed keywords are both trustworthy and professional. This paper proposes an authentication technique that operates on touchscreen devices with an aim to solve the problem of shoulder-surfing assaults over validation. Graphical passwords are required to get access to the system and to assist with its security. A graphical password is a type of password that can be used instead of a textual password to gain access to a system. It is safer than a written password, which is vulnerable to a shoulder-surfing assault. In this study, we used a falsification technique to create a system that is immune to shoulder-surfing assaults. As a result, the hacker will be perplexed when attempting to obtain and misuse the password. Using Android Studio, the proposed scheme was developed into an android application composed of shoulder-surfing resistant user registration and login process. When compared with other graphical password techniques based on images, the proposed the technique of graphical authentication mechanism with numbers/digits is more easy to memorize and has less cognitive strain on the user. Our graphical authentication technique has more complexity and larger password space than the conventional numeric password schemes, making it a superior method for safeguarding against shoulder surfing attacks. To meet customer expectations, this project seeks to design a graphical password by combining processes such as long key press, and acceleration detection mechanism of Android devices.
{"title":"A Novel Graphical Password Scheme to Avoid Shoulder-Surfing Attacks in Android Devices","authors":"Mayank Kaushik, Ashish Rawat, Varsha Sisaudia, Love Parashar","doi":"10.1109/CONIT55038.2022.9848122","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848122","url":null,"abstract":"Shoulder surfing is an easy attack, where an unauthorized user tries to gain access to sensitive information. Unfortunately, none of the currently available content-based shoulder-surfing approaches for protected graphical concealed keywords are both trustworthy and professional. This paper proposes an authentication technique that operates on touchscreen devices with an aim to solve the problem of shoulder-surfing assaults over validation. Graphical passwords are required to get access to the system and to assist with its security. A graphical password is a type of password that can be used instead of a textual password to gain access to a system. It is safer than a written password, which is vulnerable to a shoulder-surfing assault. In this study, we used a falsification technique to create a system that is immune to shoulder-surfing assaults. As a result, the hacker will be perplexed when attempting to obtain and misuse the password. Using Android Studio, the proposed scheme was developed into an android application composed of shoulder-surfing resistant user registration and login process. When compared with other graphical password techniques based on images, the proposed the technique of graphical authentication mechanism with numbers/digits is more easy to memorize and has less cognitive strain on the user. Our graphical authentication technique has more complexity and larger password space than the conventional numeric password schemes, making it a superior method for safeguarding against shoulder surfing attacks. To meet customer expectations, this project seeks to design a graphical password by combining processes such as long key press, and acceleration detection mechanism of Android devices.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"292 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":"123268940","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/CONIT55038.2022.9848083
Sagarmal Gupta, Jayant Mahajan, Anshu Saxena
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik”s wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower”s., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear.
{"title":"Measuring Consumer Perception for P2P Platform: NLP Approach","authors":"Sagarmal Gupta, Jayant Mahajan, Anshu Saxena","doi":"10.1109/CONIT55038.2022.9848083","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848083","url":null,"abstract":"The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik”s wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower”s., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"12 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":"123708595","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/CONIT55038.2022.9848134
Atharva Manjrekar, Devin J. McConnell, S. Gokhale
This paper mines social media conversations collected using #sideeffects during the early phases of the Covid-19 vaccine roll out. The multi-dimensional analysis seeks to uncover post-vaccination symptoms and their intensity, latent underlying emotions, how the discourse spreads and is received, and ultimately separate the content into two groups according to their severity. The paper infers that people share their severe symptoms that cause major inconvenience and disruption using exaggerated emotions, and the follower networks of these authors are stronger. Tweets outlining mild symptoms are liked and retweeted many more times, and the friends networks of these authors are stronger. Both types of tweets (severe and mild) express heavy surprise and some fear. Classification of the tweets into mild and severe groups is attempted using boosting, neural network and BERT-based natural language transformer methods. BERT significantly outperforms the former two achieving a F1-score of 0.88. Given that the Covid-19 vaccine was developed and deployed rapidly, with limited clinical data on its side effects, this research fills an important gap in gathering such information under real-life settings. The paper thus concludes with a discussion of how these findings could be leveraged by public health organizations to combat vaccine hesitancy and maximize vaccine uptake.
{"title":"Analyzing Twitter Conversations on Side Effects of Covid-19 Vaccine","authors":"Atharva Manjrekar, Devin J. McConnell, S. Gokhale","doi":"10.1109/CONIT55038.2022.9848134","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848134","url":null,"abstract":"This paper mines social media conversations collected using #sideeffects during the early phases of the Covid-19 vaccine roll out. The multi-dimensional analysis seeks to uncover post-vaccination symptoms and their intensity, latent underlying emotions, how the discourse spreads and is received, and ultimately separate the content into two groups according to their severity. The paper infers that people share their severe symptoms that cause major inconvenience and disruption using exaggerated emotions, and the follower networks of these authors are stronger. Tweets outlining mild symptoms are liked and retweeted many more times, and the friends networks of these authors are stronger. Both types of tweets (severe and mild) express heavy surprise and some fear. Classification of the tweets into mild and severe groups is attempted using boosting, neural network and BERT-based natural language transformer methods. BERT significantly outperforms the former two achieving a F1-score of 0.88. Given that the Covid-19 vaccine was developed and deployed rapidly, with limited clinical data on its side effects, this research fills an important gap in gathering such information under real-life settings. The paper thus concludes with a discussion of how these findings could be leveraged by public health organizations to combat vaccine hesitancy and maximize vaccine uptake.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"2 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":"125292846","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/CONIT55038.2022.9847985
Subash Kumar, Kartikeya, S. Sushanth Kumar, Nikhil Gupta, Agrima Yadav
Object tracking at dark is critical to minimizing the number of nocturnal traffic crashes. This paper presents a deep convolutional neural network dubbed M-YOLO to enhance the precision of nocturnal object recognition and to be suited for limited contexts (also including microcontrollers in automobiles). To begin, track line images are separated into other * 2S panels based on the features of uneven spatial and temporal dispersion densities. Additionally, the sensor frequency has been limited to four measurement levels, making it even more suited for tiny source localization, like lateral distance measurement. Thirdly, to optimize the connectivity, a fully connected layer throughout the basic Yolo v3 method is reduced by 53 to 49 levels. Lastly, characteristics like cluster center radius and backpropagation are enhanced.
{"title":"Lane and Vehicle Detection Using Hough Transform and YOLOv3","authors":"Subash Kumar, Kartikeya, S. Sushanth Kumar, Nikhil Gupta, Agrima Yadav","doi":"10.1109/CONIT55038.2022.9847985","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847985","url":null,"abstract":"Object tracking at dark is critical to minimizing the number of nocturnal traffic crashes. This paper presents a deep convolutional neural network dubbed M-YOLO to enhance the precision of nocturnal object recognition and to be suited for limited contexts (also including microcontrollers in automobiles). To begin, track line images are separated into other * 2S panels based on the features of uneven spatial and temporal dispersion densities. Additionally, the sensor frequency has been limited to four measurement levels, making it even more suited for tiny source localization, like lateral distance measurement. Thirdly, to optimize the connectivity, a fully connected layer throughout the basic Yolo v3 method is reduced by 53 to 49 levels. Lastly, characteristics like cluster center radius and backpropagation are enhanced.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"122 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":"122337496","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/CONIT55038.2022.9848324
Snehalatha, Suryanarayana Gangolu
Increased penetration of converter-interfaced renewable plants improves the current power system's resiliency. The integration of renewable plants also minimises line losses, ensuring the power supply's reliability. During a fault, the PV plant's control mechanism, which complies with grid standards, modulates the current and voltage signals dramatically. As a result, traditional protection relays are unable to give the circuit breaker a trip signal. In this article the concerns with the conventional protection system during the aforementioned circumstance are investigated and a new protection technique is devised for discriminating the internal and external fault by employing both ends current data by calculating the Phase Current Ratio (PCR). Taking consideration of the relay's reliability, sensitivity, dependability and security, threshold region is set. The proposed technique's effectiveness is demonstrated using a 100 kW solar PV array coupled to $a$ 25 kV, grid system at 50 Hz having transmission line of 50 km in software MATLAB/SIMULINK. Different shunt faults are tested and the results proves the validity and authenticity of the suggested relaying scheme in the identification of the internal fault and external fault accurately.
{"title":"Phase Current Ratio Based Protection Scheme for Grid Connected Renewable System","authors":"Snehalatha, Suryanarayana Gangolu","doi":"10.1109/CONIT55038.2022.9848324","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848324","url":null,"abstract":"Increased penetration of converter-interfaced renewable plants improves the current power system's resiliency. The integration of renewable plants also minimises line losses, ensuring the power supply's reliability. During a fault, the PV plant's control mechanism, which complies with grid standards, modulates the current and voltage signals dramatically. As a result, traditional protection relays are unable to give the circuit breaker a trip signal. In this article the concerns with the conventional protection system during the aforementioned circumstance are investigated and a new protection technique is devised for discriminating the internal and external fault by employing both ends current data by calculating the Phase Current Ratio (PCR). Taking consideration of the relay's reliability, sensitivity, dependability and security, threshold region is set. The proposed technique's effectiveness is demonstrated using a 100 kW solar PV array coupled to $a$ 25 kV, grid system at 50 Hz having transmission line of 50 km in software MATLAB/SIMULINK. Different shunt faults are tested and the results proves the validity and authenticity of the suggested relaying scheme in the identification of the internal fault and external fault accurately.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"214 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":"122598368","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：