The global tourism business is expanding as more people travel and visit new locations both abroad and locally. Cox’s Bazar is the longest natural unbroken sea beach in the world situated in Bangladesh accommodating millions of foreign and local tourists every year. This has been a very popular destination with various activities and beautiful natural attraction spots. There are numerous Cox’s Bazar trip packages available for travelers to choose from. Compared to the large number of tour packages found online for this destination at present, this paper proposes a methodology to help the tourists to select a tour package based on their likings and different criteria. A decision-making selection process has been proposed using the Fuzzy Analytic Hierarchy Process(FAHP) and TOPSIS method to rank the packages based on 12 criteria that are closely knitted to the preference of users during the time of tour selection. A dataset of 100 alternative tour packages has been built with existing tour packages found from online travel agency websites and multiple decision makers are used to make pairwise criterion comparison. The tourists/users input fuzzy numbers, and then using the proposed methodology a ranking of all the alternatives is provided to aid them in choosing the optimal tour package.
{"title":"Multi-criteria Decision Theoritic Approach for Tour Package Selection using Fuzzy AHP and TOPSIS Methods: A Case Study on Cox’s Bazar","authors":"Marufa Kamal, Rakib Hossain Rifat, Abanti Chakraborty Shruti, Md. Golam Rabiul Alam","doi":"10.1109/I2CT57861.2023.10126334","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126334","url":null,"abstract":"The global tourism business is expanding as more people travel and visit new locations both abroad and locally. Cox’s Bazar is the longest natural unbroken sea beach in the world situated in Bangladesh accommodating millions of foreign and local tourists every year. This has been a very popular destination with various activities and beautiful natural attraction spots. There are numerous Cox’s Bazar trip packages available for travelers to choose from. Compared to the large number of tour packages found online for this destination at present, this paper proposes a methodology to help the tourists to select a tour package based on their likings and different criteria. A decision-making selection process has been proposed using the Fuzzy Analytic Hierarchy Process(FAHP) and TOPSIS method to rank the packages based on 12 criteria that are closely knitted to the preference of users during the time of tour selection. A dataset of 100 alternative tour packages has been built with existing tour packages found from online travel agency websites and multiple decision makers are used to make pairwise criterion comparison. The tourists/users input fuzzy numbers, and then using the proposed methodology a ranking of all the alternatives is provided to aid them in choosing the optimal tour package.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129643787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126372
B. T. Khoa, T. Huynh, Nguyen Thi Diem Huong
Using data collected monthly beginning in January 2010 and ending in December 2022, this research intends to forecast the returns of the Vietnam Dong/US Dollar Exchange Rate on the oil price worldwide. Following the current literature, this investigation develops a predictive model that considers the most important aspects of the predictor and the predicted series. Oil and the returns on the exchange rate were shown to have a linearly negative connection. Both net oil exporters and net oil importers might anticipate favorable exchange rate returns if oil prices remain stable. Finally, we evaluate how different forecasting methods perform in and out of the sample. While there was higher volatility in the out-of-sample period, the research indicated that the prediction error was still small.
{"title":"Predicting Returns of Exchange Rate from Oil Prices: Machine Learning Approach","authors":"B. T. Khoa, T. Huynh, Nguyen Thi Diem Huong","doi":"10.1109/I2CT57861.2023.10126372","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126372","url":null,"abstract":"Using data collected monthly beginning in January 2010 and ending in December 2022, this research intends to forecast the returns of the Vietnam Dong/US Dollar Exchange Rate on the oil price worldwide. Following the current literature, this investigation develops a predictive model that considers the most important aspects of the predictor and the predicted series. Oil and the returns on the exchange rate were shown to have a linearly negative connection. Both net oil exporters and net oil importers might anticipate favorable exchange rate returns if oil prices remain stable. Finally, we evaluate how different forecasting methods perform in and out of the sample. While there was higher volatility in the out-of-sample period, the research indicated that the prediction error was still small.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121204044","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}
Agriculture is seen as a key pillar of the economy of Bangladesh. The country produces an enormous variety of crops. To ensure stability by preventing losses and maintaining supply and market demand, the integration of advanced technology like Machine Learning (ML) in agriculture is beneficial. With the growth of Big Data techniques and powerful computers, ML has opened up new possibilities for data-intensive research in a variety of disciplines of crop cultivation. Since crops are one of the main components of agriculture, our main concerns are issues relating to crops such as disease detection, crop price and yield prediction. Disease infected crops cause significant loss. Producers need to have the right information on which crops should be harvested where and when. Again, ensuring fair price of crops is mandatory for economical balance and stability. This paper provides an in-depth review of the research on ML applications in agricultural systems. A combination of ML and agriculture can provide great suggestions and in-depth insights for farmers in decision support and action.
{"title":"A Comprehensive Review on Precision Agriculture and Machine Learning Approach in Bangladesh","authors":"Syed Ishtiak Rahman, Shifa Chowdhury Iwase, Arefin Ittesafun Abian, Tapotosh Ghosh, D. Farid","doi":"10.1109/I2CT57861.2023.10126189","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126189","url":null,"abstract":"Agriculture is seen as a key pillar of the economy of Bangladesh. The country produces an enormous variety of crops. To ensure stability by preventing losses and maintaining supply and market demand, the integration of advanced technology like Machine Learning (ML) in agriculture is beneficial. With the growth of Big Data techniques and powerful computers, ML has opened up new possibilities for data-intensive research in a variety of disciplines of crop cultivation. Since crops are one of the main components of agriculture, our main concerns are issues relating to crops such as disease detection, crop price and yield prediction. Disease infected crops cause significant loss. Producers need to have the right information on which crops should be harvested where and when. Again, ensuring fair price of crops is mandatory for economical balance and stability. This paper provides an in-depth review of the research on ML applications in agricultural systems. A combination of ML and agriculture can provide great suggestions and in-depth insights for farmers in decision support and action.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121417963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126431
D. Banerjee, V. Kukreja, S. Hariharan, Vandana Sharma
The majority of the people in India dependent on farming to earn a living. As a due to climate change, farmers face various challenges. One of them is a reduction in yield, and one of the causes of that is the development of diseases in the plant. The main economic agricultural activity is a banana plantation, particularly in Asian and African nations. Feature extraction using CNN and SVM was used to identify and classify the banana fruit leaf diseases. The dataset was initially improved, precompiled using Matlab code, and then divided into training and testing sections. During the conduct of this research, the ratio employed to divide the data into training and validation was 80:20. After the CNN was implemented successfully, and the SVM models, the maximum average accuracy measured was 94%. According to this study, the suggested model achieves the automatic right diagnosis of banana leaf diseases and gives a workable method for the detection of crop leaf diseases with high recognition accuracy.
{"title":"Precision Agriculture: Classifying Banana Leaf Diseases with Hybrid Deep Learning Models","authors":"D. Banerjee, V. Kukreja, S. Hariharan, Vandana Sharma","doi":"10.1109/I2CT57861.2023.10126431","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126431","url":null,"abstract":"The majority of the people in India dependent on farming to earn a living. As a due to climate change, farmers face various challenges. One of them is a reduction in yield, and one of the causes of that is the development of diseases in the plant. The main economic agricultural activity is a banana plantation, particularly in Asian and African nations. Feature extraction using CNN and SVM was used to identify and classify the banana fruit leaf diseases. The dataset was initially improved, precompiled using Matlab code, and then divided into training and testing sections. During the conduct of this research, the ratio employed to divide the data into training and validation was 80:20. After the CNN was implemented successfully, and the SVM models, the maximum average accuracy measured was 94%. According to this study, the suggested model achieves the automatic right diagnosis of banana leaf diseases and gives a workable method for the detection of crop leaf diseases with high recognition accuracy.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114238838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126489
Archana Tiwari, Bharti Masram, Kritika Bharatdwaj
The phenomenon of logging data occurs daily. When a person sees something or experiences an occurrence, their memory of it is stored in their brains for future use. Similar to that, data logging is the process of gathering, examining, and storing data for eventual use. Applications in science, medicine, and industry, knowledge is required for the course of temperature and relative humidity at a specific moment. For recovery of this data from different sources or sensors, data loggers may be employed. A data logger is an electronic instrument that senses temperature and relative humidity by combining analogue and digital measurements with programming techniques. The data loggers take input from the DHT11 sensor. The database receives this info after that. for storing and analyzing the monitored data. A temperature and humidity data logger's design and simulation are presented in this paper.
{"title":"Message Queuing Telemetry Transport Based Data Logger","authors":"Archana Tiwari, Bharti Masram, Kritika Bharatdwaj","doi":"10.1109/I2CT57861.2023.10126489","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126489","url":null,"abstract":"The phenomenon of logging data occurs daily. When a person sees something or experiences an occurrence, their memory of it is stored in their brains for future use. Similar to that, data logging is the process of gathering, examining, and storing data for eventual use. Applications in science, medicine, and industry, knowledge is required for the course of temperature and relative humidity at a specific moment. For recovery of this data from different sources or sensors, data loggers may be employed. A data logger is an electronic instrument that senses temperature and relative humidity by combining analogue and digital measurements with programming techniques. The data loggers take input from the DHT11 sensor. The database receives this info after that. for storing and analyzing the monitored data. A temperature and humidity data logger's design and simulation are presented in this paper.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121486223","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}
Defending against identity-based threats and attacks which have tremendously grown in number in the age of remote working and access, requires intelligent, strategic, nonconventional, and dynamic ways of authentication and authorization. This paper aims to make identity security risk-based and hence adaptive by devising risk-scoring algorithms for five real-time use cases in detail. Zero-trust security principles are incorporated by continually collecting sign-in logs and analyzing them to check for any suspicious activities or anomalies to make it a dynamic approach. Based on the risk scores calculated users are segregated as risky and non-risky. While many adaptive authentication approaches have been proposed, the identities are confined just to users. Moreover, they lack emphasis on practical risk evaluation techniques. This work considers devices as an identity too and categorizes them as registered and unregistered devices. Further, results are made available to security administrators by displaying them on a dashboard for them to analyze and make necessary decisions like mitigation, multi-level authentication, or any other access control policies as such.
{"title":"Defending Against Identity Threats using Adaptive Authentication","authors":"Lalitha Sravanti Dasu, Mannav Dhamija, Gurram Dishitha, Ajith Vivekanandan, Sarasvathi V","doi":"10.1109/I2CT57861.2023.10126295","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126295","url":null,"abstract":"Defending against identity-based threats and attacks which have tremendously grown in number in the age of remote working and access, requires intelligent, strategic, nonconventional, and dynamic ways of authentication and authorization. This paper aims to make identity security risk-based and hence adaptive by devising risk-scoring algorithms for five real-time use cases in detail. Zero-trust security principles are incorporated by continually collecting sign-in logs and analyzing them to check for any suspicious activities or anomalies to make it a dynamic approach. Based on the risk scores calculated users are segregated as risky and non-risky. While many adaptive authentication approaches have been proposed, the identities are confined just to users. Moreover, they lack emphasis on practical risk evaluation techniques. This work considers devices as an identity too and categorizes them as registered and unregistered devices. Further, results are made available to security administrators by displaying them on a dashboard for them to analyze and make necessary decisions like mitigation, multi-level authentication, or any other access control policies as such.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126333
Mainul Karim, Niloy Kumar Kundu, Dipu Saha, Sarah Kabir, Sumaiya Mim, Dewan Md. Farid
Federated Learning (FL) is a new concept in machine learning that trains predictive models across multiple nodes and machines holding local training data without sharing that data with other nodes. It’s also known as collaborative learning. In FL, instead of sending the training data, only parameter values are uploaded to the master node or server. On the contrary, Rainforest is a concept for dealing with big data using a decision tree (DT) classifier. DT is one of the popular machine learning algorithms, which is a top-down recursive divide and conquer method. In this paper, we utilized the concept of federated learning by applying the Rainforest algorithm, where datasets are divided into several subsets of data using the clustering technique, from which scalable decision trees are constructed. From each subset of data, we have an AVC (attribute-value, class-label) table, which is sent to the central master node or server to create the full decision tree by using matrix addition. To train the model, we used ten different datasets and evaluated the proposed model. The experimental analysis shows very good accuracy and precision in overall performance.
{"title":"Implementing Federated Learning based on RainForest Model","authors":"Mainul Karim, Niloy Kumar Kundu, Dipu Saha, Sarah Kabir, Sumaiya Mim, Dewan Md. Farid","doi":"10.1109/I2CT57861.2023.10126333","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126333","url":null,"abstract":"Federated Learning (FL) is a new concept in machine learning that trains predictive models across multiple nodes and machines holding local training data without sharing that data with other nodes. It’s also known as collaborative learning. In FL, instead of sending the training data, only parameter values are uploaded to the master node or server. On the contrary, Rainforest is a concept for dealing with big data using a decision tree (DT) classifier. DT is one of the popular machine learning algorithms, which is a top-down recursive divide and conquer method. In this paper, we utilized the concept of federated learning by applying the Rainforest algorithm, where datasets are divided into several subsets of data using the clustering technique, from which scalable decision trees are constructed. From each subset of data, we have an AVC (attribute-value, class-label) table, which is sent to the central master node or server to create the full decision tree by using matrix addition. To train the model, we used ten different datasets and evaluated the proposed model. The experimental analysis shows very good accuracy and precision in overall performance.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127635218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126368
Anil Kumar Dasari, Saroj K. Biswas, Saptarsi Sanyal, B. Purkayastha
An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.
{"title":"Intrusion Detection System using Ensemble Learning Analytics","authors":"Anil Kumar Dasari, Saroj K. Biswas, Saptarsi Sanyal, B. Purkayastha","doi":"10.1109/I2CT57861.2023.10126368","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126368","url":null,"abstract":"An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126363970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126351
Manisha Saini, Ajay Bhardwaj, Sarfaraz Nawaz
In the present scenario of the electricity energy market, power generation firms seek to maximize revenue by optimizing the bid in the electricity market. In a competitive market, Strategic bidding allows each participant to improve his individual profit; however, this has a detrimental effect on public benefit. This study presents a mechanism for developing a strategic bid for electricity producers and users in a pool co-style energy market. The system is dispatched to maximize social welfare, with each supplier/large consumer bidding a linear supply/demand function. Price takers require a proper bidding structure to identify the best bidding tactics. As a result, the model must be thought of as a two-level optimization issue. Price takers submit strategic bids to the Independent System Operator (ISO) at the lower level, while the ISO Market Clearing Price (MCP) is used to maximize social welfare at the upper level in a day-ahead power market to maximize social welfare at the upper level using a pay-as-bid mechanism in a sealed auction in the competitive power market. On the IEEE-30 bus system, the proposed method's efficiency was tested. Four different evolutionary algorithms such as NSGA-II, NSGA-III, MOGWO, and MOPSO were used to address the problem from two separate perspectives for solving proposed multi-objective problems. The result section presents a comparative analysis of the total profit and market clearing price, showing that the NSGA-III algorithm offers superior results than other methods.
{"title":"Multi-Objective Optimal Bidding Approach for both Small & Large Customers in Competitive power Market","authors":"Manisha Saini, Ajay Bhardwaj, Sarfaraz Nawaz","doi":"10.1109/I2CT57861.2023.10126351","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126351","url":null,"abstract":"In the present scenario of the electricity energy market, power generation firms seek to maximize revenue by optimizing the bid in the electricity market. In a competitive market, Strategic bidding allows each participant to improve his individual profit; however, this has a detrimental effect on public benefit. This study presents a mechanism for developing a strategic bid for electricity producers and users in a pool co-style energy market. The system is dispatched to maximize social welfare, with each supplier/large consumer bidding a linear supply/demand function. Price takers require a proper bidding structure to identify the best bidding tactics. As a result, the model must be thought of as a two-level optimization issue. Price takers submit strategic bids to the Independent System Operator (ISO) at the lower level, while the ISO Market Clearing Price (MCP) is used to maximize social welfare at the upper level in a day-ahead power market to maximize social welfare at the upper level using a pay-as-bid mechanism in a sealed auction in the competitive power market. On the IEEE-30 bus system, the proposed method's efficiency was tested. Four different evolutionary algorithms such as NSGA-II, NSGA-III, MOGWO, and MOPSO were used to address the problem from two separate perspectives for solving proposed multi-objective problems. The result section presents a comparative analysis of the total profit and market clearing price, showing that the NSGA-III algorithm offers superior results than other methods.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126409013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126209
A. Shirodkar, C. Vyjayanthi
DC microgrid technology provides a promising alternative to the currently existing AC microgrids, owing to ease of integration of renewable energy sources, lesser number of grid variables in need of control, and the rapid developments in DC energy storage technologies. The availibility of DC lighting loads, DC motors, and high efficiency DC-AC converters for high power applications make a strong case for the implementation of DC microgrids. This paper proposes a DC microgrid system, and a control algorithm for operating the aforementioned microgrid in Hybrid Mode. The microgrid integrates a PV array with MPP tracking, AC grid interfacing, a Battery Energy Storage System with CC-CV charging and cell balancing using a proposed cell balancing algorithm. Each component is studied and designed individually, and finally all are integrated to form the DC microgrid, whose operation in Hybrid Mode is demonstrated through simulated results.
{"title":"Design and Development of Control Algorithm for a DC Microgrid System","authors":"A. Shirodkar, C. Vyjayanthi","doi":"10.1109/I2CT57861.2023.10126209","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126209","url":null,"abstract":"DC microgrid technology provides a promising alternative to the currently existing AC microgrids, owing to ease of integration of renewable energy sources, lesser number of grid variables in need of control, and the rapid developments in DC energy storage technologies. The availibility of DC lighting loads, DC motors, and high efficiency DC-AC converters for high power applications make a strong case for the implementation of DC microgrids. This paper proposes a DC microgrid system, and a control algorithm for operating the aforementioned microgrid in Hybrid Mode. The microgrid integrates a PV array with MPP tracking, AC grid interfacing, a Battery Energy Storage System with CC-CV charging and cell balancing using a proposed cell balancing algorithm. Each component is studied and designed individually, and finally all are integrated to form the DC microgrid, whose operation in Hybrid Mode is demonstrated through simulated results.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126552915","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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