Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836404
M. H. Kabir, Md. Shahriar Rajib, Abu Saleh Md. Mahfujur Rahman, M. Rahman, Samrat Kumar Dey
Network intrusion has become a prime concern issue for the industry and government organizations in the domain of the cyber-threat landscape. To counter this threat, a network intrusion detection system has been considered to be vital in identifying network traffic as normal or anomaly. Correct identification of the potential threat as an anomaly depends on the accuracy of the Network Intrusion Detection System (NIDS). Several approaches like single classical, hybrid, and ensemble methods are in practice to develop a network intrusion detection model. In this paper, two different stacking Machine Learning (ML) models with Extra Tree (ET) Classifier and Mutual Information Gain feature selection methods are proposed for better accuracy of the NIDS. We applied the models on the UNSW-NB15 packet-based dataset which contains the most recent attack types and experimentally proved that the testing accuracy of the stacking models is better than all individual models. Comparative results also depict that one of our proposed models shows better accuracy (96.24%) than any other existing competing models.
{"title":"Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach","authors":"M. H. Kabir, Md. Shahriar Rajib, Abu Saleh Md. Mahfujur Rahman, M. Rahman, Samrat Kumar Dey","doi":"10.1109/icaeee54957.2022.9836404","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836404","url":null,"abstract":"Network intrusion has become a prime concern issue for the industry and government organizations in the domain of the cyber-threat landscape. To counter this threat, a network intrusion detection system has been considered to be vital in identifying network traffic as normal or anomaly. Correct identification of the potential threat as an anomaly depends on the accuracy of the Network Intrusion Detection System (NIDS). Several approaches like single classical, hybrid, and ensemble methods are in practice to develop a network intrusion detection model. In this paper, two different stacking Machine Learning (ML) models with Extra Tree (ET) Classifier and Mutual Information Gain feature selection methods are proposed for better accuracy of the NIDS. We applied the models on the UNSW-NB15 packet-based dataset which contains the most recent attack types and experimentally proved that the testing accuracy of the stacking models is better than all individual models. Comparative results also depict that one of our proposed models shows better accuracy (96.24%) than any other existing competing models.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116066595","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836388
Md. Rashedul Islam, M. Akhtar, M. Begum
The upcoming digital transformation of the modern industry will principally build upon the software systems. Certainly, any software system should commit to being fully reliable and free from any deficiency such as software faults. Maintaining the aforementioned consistency is the main objective of software reliability. The long short-term memory (LSTM) networks are employed for the first time in this kind of research to forecast software faults. The one-step walk-forward validation method is used to predict the software faults. Due to the exponential nature of data, we normalized our cumulative software fault count data using Min-Max Scalar and Box-Cox Transformation methods. Each type of normalized data is fed into the LSTM networks. With the same batch size, the number of neurons and epoch parameters were regulated with different tiers of combinations. The time-series-based software fault data were trained and tested after applying Min-Max and Box-Cox data transformation methods to obtain the root means square error (RMSE) values, and then both models were compared with each other. The RMSE values of the model with the Min-Max Scaler transforming method outperform the second model built with the Box-Cox Transformation method. From our very best knowledge, the obtained RMSE value from the software fault count data using LSTM is the first of its kind. Our models clearly show that the LSTM can be used to predict software faults. We also calculated the data dispersion from the observed independent RMSE data points of each model. The quantified data dispersion value of the second model was found to be less minimal than the first one.
{"title":"Long short-term memory (LSTM) networks based software fault prediction using data transformation methods","authors":"Md. Rashedul Islam, M. Akhtar, M. Begum","doi":"10.1109/icaeee54957.2022.9836388","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836388","url":null,"abstract":"The upcoming digital transformation of the modern industry will principally build upon the software systems. Certainly, any software system should commit to being fully reliable and free from any deficiency such as software faults. Maintaining the aforementioned consistency is the main objective of software reliability. The long short-term memory (LSTM) networks are employed for the first time in this kind of research to forecast software faults. The one-step walk-forward validation method is used to predict the software faults. Due to the exponential nature of data, we normalized our cumulative software fault count data using Min-Max Scalar and Box-Cox Transformation methods. Each type of normalized data is fed into the LSTM networks. With the same batch size, the number of neurons and epoch parameters were regulated with different tiers of combinations. The time-series-based software fault data were trained and tested after applying Min-Max and Box-Cox data transformation methods to obtain the root means square error (RMSE) values, and then both models were compared with each other. The RMSE values of the model with the Min-Max Scaler transforming method outperform the second model built with the Box-Cox Transformation method. From our very best knowledge, the obtained RMSE value from the software fault count data using LSTM is the first of its kind. Our models clearly show that the LSTM can be used to predict software faults. We also calculated the data dispersion from the observed independent RMSE data points of each model. The quantified data dispersion value of the second model was found to be less minimal than the first one.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609136","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836358
Sayed Md.Abrar Gani, Md. Rashidul Islam, M. Shafiullah, Jahid Hasan Tayeb, M. Hossain, Amjad Ali
An evolutionary algorithm-based power system stabilizers (PSS) design for low-frequency oscillations (LFO) damping in multi-machine power system networks (MMPSNs) is presented in this paper. A damping ratio-based objective function is developed to enhance the system damping where the widely employed lead-lag type PSS is considered in the problem formulation. The equilibrium Optimizer (EO), a recently developed metaheuristic algorithm that is capable of finding optimal solutions in complex engineering problems, is employed in this article. The algorithm's resilience is demonstrated by its ability to lead to the best PSS design regardless of the initial assumption made by the user. Two distinct multi-machine networks 2-area 4-machine and IEEE 10-machine 39-bus are used in this research. EO-based PSS results are compared with traditional PSS results to investigate which one yields better results for stability. According to the simulation findings, the EO technique reduces the settling time and overshoot significantly over the other techniques.
{"title":"Equilibrium Optimizer for LFO Damping in Multimachine Power System Networks","authors":"Sayed Md.Abrar Gani, Md. Rashidul Islam, M. Shafiullah, Jahid Hasan Tayeb, M. Hossain, Amjad Ali","doi":"10.1109/icaeee54957.2022.9836358","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836358","url":null,"abstract":"An evolutionary algorithm-based power system stabilizers (PSS) design for low-frequency oscillations (LFO) damping in multi-machine power system networks (MMPSNs) is presented in this paper. A damping ratio-based objective function is developed to enhance the system damping where the widely employed lead-lag type PSS is considered in the problem formulation. The equilibrium Optimizer (EO), a recently developed metaheuristic algorithm that is capable of finding optimal solutions in complex engineering problems, is employed in this article. The algorithm's resilience is demonstrated by its ability to lead to the best PSS design regardless of the initial assumption made by the user. Two distinct multi-machine networks 2-area 4-machine and IEEE 10-machine 39-bus are used in this research. EO-based PSS results are compared with traditional PSS results to investigate which one yields better results for stability. According to the simulation findings, the EO technique reduces the settling time and overshoot significantly over the other techniques.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123746870","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836365
Md. Ether Deowan, Ahsan Kabir Nuhel, Mohammed Shakawath Hossain, A. Ullah, Shuvra Saha
5G is the next-generation cellular network that will have substantial improvement on high data rates, low latency, and reliable data transfer to keep up with the modern world. This network will be providing a flexible platform for upcoming services such as IoT, Artificial Intelligence, Cloud computing, Smart grid, Industrial automation, Natural Language Processing, machine communication, and all other latest technologies. 5G have a data rate of 10–100 times greater than 4G, and now 5G is in the testing phase in Bangladesh. To facilitate the key features of 5G proper architecture, spectrum allocation and policy are essential. In this paper, the authors present an overview of all important aspects of 5G technology for getting higher efficiency. The different Scopes, safety issues, and Challenges of implementing 5G technology in Bangladesh were also reviewed.
{"title":"A Study on the Aspects of 5G Implementation in Bangladesh","authors":"Md. Ether Deowan, Ahsan Kabir Nuhel, Mohammed Shakawath Hossain, A. Ullah, Shuvra Saha","doi":"10.1109/icaeee54957.2022.9836365","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836365","url":null,"abstract":"5G is the next-generation cellular network that will have substantial improvement on high data rates, low latency, and reliable data transfer to keep up with the modern world. This network will be providing a flexible platform for upcoming services such as IoT, Artificial Intelligence, Cloud computing, Smart grid, Industrial automation, Natural Language Processing, machine communication, and all other latest technologies. 5G have a data rate of 10–100 times greater than 4G, and now 5G is in the testing phase in Bangladesh. To facilitate the key features of 5G proper architecture, spectrum allocation and policy are essential. In this paper, the authors present an overview of all important aspects of 5G technology for getting higher efficiency. The different Scopes, safety issues, and Challenges of implementing 5G technology in Bangladesh were also reviewed.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116546643","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836360
M. J. Hossain, P. C. Paul, S. S. Islam, M. Begum
A new mirror C-shaped with square split resonators engineered material unit cell structure proposed for L-, S-, and C-band applications. The design structure has shown the negative refractive index (NRI) property along with the x-axis wave propagation. The finite integration technique (FIT) based Computer Simulation Technology (CST) microwave studio software and finite element method based High Frequency Structure Simulator (HFSS) software are adopted to investigate the proposed design structure. The parametric studies have been done based on the substrate's thickness. The engineered material unit cell structure acquired higher effective medium ratio (21.71) and exhibited the NRI properties at 1.47 GHz frequency. The higher wideband (5.63 GHz) also attained by the proposed design structure. The results of the proposed engineered material demonstrated L-, S-, and C-bands response over the frequency ranges from 1 to 10 GHz. Hence, the proposed structure enables numerous application areas of L-, S-, and C-bands.
{"title":"Miniaturized Mirror C-Shaped Negative Index Engineered Material for L-, S-, and C-band Applications","authors":"M. J. Hossain, P. C. Paul, S. S. Islam, M. Begum","doi":"10.1109/icaeee54957.2022.9836360","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836360","url":null,"abstract":"A new mirror C-shaped with square split resonators engineered material unit cell structure proposed for L-, S-, and C-band applications. The design structure has shown the negative refractive index (NRI) property along with the x-axis wave propagation. The finite integration technique (FIT) based Computer Simulation Technology (CST) microwave studio software and finite element method based High Frequency Structure Simulator (HFSS) software are adopted to investigate the proposed design structure. The parametric studies have been done based on the substrate's thickness. The engineered material unit cell structure acquired higher effective medium ratio (21.71) and exhibited the NRI properties at 1.47 GHz frequency. The higher wideband (5.63 GHz) also attained by the proposed design structure. The results of the proposed engineered material demonstrated L-, S-, and C-bands response over the frequency ranges from 1 to 10 GHz. Hence, the proposed structure enables numerous application areas of L-, S-, and C-bands.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122516062","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836512
Andrew Das Shuvro, Arju Roy, Md. Soyaeb Hasan, Md. Rafiqul Islam
We presented here the theoretical study on the performance parameters of Si0.88Sn0.12 p-n junction solar cell. A detailed description of the dependences of short circuit current density $boldsymbol{(Jsc),}$ open circuit voltage $boldsymbol{(V oc),}$ fill factor $boldsymbol{(FF)}$ and conversion efficiency $boldsymbol{(eta)}$ on the diffusion lengths of both electron $(L_{n})$ and hole $(L_{p})$ has been illustrated. We also demonstrated in depth the effect of generation rate of charge carrier as well as temperature on the $boldsymbol{J-V}$ and $boldsymbol{P-V}$ characteristics of SiSn solar cell at the room temperature. The estimated results revealed that the p-n junction solar cell using Si0.88Sn0.12alloy gives $mathbf{{J}_{text{sc}}sim 39.6text{mA}/text{cm},^{2}mathrm{V}_{mathrm{o}mathrm{c}}sim 0.89mathrm{V}, text{FF}sim 0.828}$ and the maximum efficiency $mathbf{etasim 29.19%}$. Short circuit current density and open circuit voltage are found to be strongly dependent on the generation rate and the diffusion length of electrons and holes for $mathbf{Si_{1-x}Sn_{x}.}$ In particular, the sustainability of SiSn alloy as an active photovoltaic material is assessed here by analyzing different performance parameters.
{"title":"Performance Analysis of SiSn Based High Efficiency p-n Junction Solar Cell","authors":"Andrew Das Shuvro, Arju Roy, Md. Soyaeb Hasan, Md. Rafiqul Islam","doi":"10.1109/icaeee54957.2022.9836512","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836512","url":null,"abstract":"We presented here the theoretical study on the performance parameters of Si<inf>0.88</inf>Sn<inf>0.12</inf> p-n junction solar cell. A detailed description of the dependences of short circuit current density <tex>$boldsymbol{(Jsc),}$</tex> open circuit voltage <tex>$boldsymbol{(V oc),}$</tex> fill factor <tex>$boldsymbol{(FF)}$</tex> and conversion efficiency <tex>$boldsymbol{(eta)}$</tex> on the diffusion lengths of both electron <tex>$(L_{n})$</tex> and hole <tex>$(L_{p})$</tex> has been illustrated. We also demonstrated in depth the effect of generation rate of charge carrier as well as temperature on the <tex>$boldsymbol{J-V}$</tex> and <tex>$boldsymbol{P-V}$</tex> characteristics of SiSn solar cell at the room temperature. The estimated results revealed that the p-n junction solar cell using Si<inf>0.88</inf>Sn<inf>0.12</inf>alloy gives <tex>$mathbf{{J}_{text{sc}}sim 39.6text{mA}/text{cm},^{2}mathrm{V}_{mathrm{o}mathrm{c}}sim 0.89mathrm{V}, text{FF}sim 0.828}$</tex> and the maximum efficiency <tex>$mathbf{etasim 29.19%}$</tex>. Short circuit current density and open circuit voltage are found to be strongly dependent on the generation rate and the diffusion length of electrons and holes for <tex>$mathbf{Si_{1-x}Sn_{x}.}$</tex> In particular, the sustainability of SiSn alloy as an active photovoltaic material is assessed here by analyzing different performance parameters.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134320347","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836410
M. M. Hossain, N. Jahan, Rayhan Ul Hossain
A CdTe-based thin-film solar cell has been designed and analyzed using SCAPS-1D simulator. The proposed solar cell consists of a transparent conductive oxide (ZnO), an n-doped Cu2O, n-type cadmium sulphide (CdS), and p-type cadmium telluride (CdTe) layer. To achieve the maximum possible power conversion efficiency (PCE), the layer thickness, doping profile, and defect density of the absorber layer have been optimized. A back surface field (BSF) layer (p++ CdTe) is also incorporated to reduce the carrier recombination at the back electrode. The optimized cell has an open circuit voltage of 0.8858V, a short circuit current of 61.2699 mA/cm2, a fill factor of 69.75%, and a PCE of 37.86% considering AM 1.5 illuminations.
{"title":"Simulation and optimization of a highly efficient ZnO/Cu2O/CdS/CdTe solar cell using SCAPS-1D","authors":"M. M. Hossain, N. Jahan, Rayhan Ul Hossain","doi":"10.1109/icaeee54957.2022.9836410","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836410","url":null,"abstract":"A CdTe-based thin-film solar cell has been designed and analyzed using SCAPS-1D simulator. The proposed solar cell consists of a transparent conductive oxide (ZnO), an n-doped Cu2O, n-type cadmium sulphide (CdS), and p-type cadmium telluride (CdTe) layer. To achieve the maximum possible power conversion efficiency (PCE), the layer thickness, doping profile, and defect density of the absorber layer have been optimized. A back surface field (BSF) layer (p++ CdTe) is also incorporated to reduce the carrier recombination at the back electrode. The optimized cell has an open circuit voltage of 0.8858V, a short circuit current of 61.2699 mA/cm2, a fill factor of 69.75%, and a PCE of 37.86% considering AM 1.5 illuminations.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610191","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836420
Joy Chandra Gope, Tanjim Tabassum, Mir Md. Mabrur, Keping Yu, Md. Arifuzzaman
Due to the expansion of social networks and e-commerce websites, sentiment analysis or opinion mining has become a more active study issue in recent years. The objective of sentiment analysis is to identify and categorize the positive and negative sentiment expressed in a piece of text. Consumers can submit reviews with a specified rating on e-commerce websites like Amazon.com. As a result, in our paper, we sought to construct sentiment analysis related to product ratings and text reviews utilizing Amazon's dataset. Linear Support Vector Ma-chine, Random Forest, Multinomial Naive Bayes, Bernoulli Naive Bayes, and Logistic Regression were among the machine learning algorithms used. We acquired accuracy with the Random Forest classifier (91.90%). We also use RNN with LSTM as a deep learning approach in our paper and got maximum accuracy (97.52%). For our model RNN-LSTM is ideal approach.
{"title":"Sentiment Analysis of Amazon Product Reviews Using Machine Learning and Deep Learning Models","authors":"Joy Chandra Gope, Tanjim Tabassum, Mir Md. Mabrur, Keping Yu, Md. Arifuzzaman","doi":"10.1109/icaeee54957.2022.9836420","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836420","url":null,"abstract":"Due to the expansion of social networks and e-commerce websites, sentiment analysis or opinion mining has become a more active study issue in recent years. The objective of sentiment analysis is to identify and categorize the positive and negative sentiment expressed in a piece of text. Consumers can submit reviews with a specified rating on e-commerce websites like Amazon.com. As a result, in our paper, we sought to construct sentiment analysis related to product ratings and text reviews utilizing Amazon's dataset. Linear Support Vector Ma-chine, Random Forest, Multinomial Naive Bayes, Bernoulli Naive Bayes, and Logistic Regression were among the machine learning algorithms used. We acquired accuracy with the Random Forest classifier (91.90%). We also use RNN with LSTM as a deep learning approach in our paper and got maximum accuracy (97.52%). For our model RNN-LSTM is ideal approach.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944300","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836362
M. Iqbal, A. Hossain, Jahidul Islam, Amit Shaha Surja, M. Kabir
In this paper, a novel control scheme based on Reinforcement Learning (RL) controller for the Static Synchronous Compensator (STATCOM) is presented for inter-area multi-machine power system. It is a common criterion to develop an improved control strategy for STATCOM to enhance the voltage stability of multi-machine power system. To exemplify on this purpose, a STATCOM controller has been designed which does not depend on the structure and parameters of power system. Moreover, the proposed strategy also replaces the conventional PI controller by RL controller and provides more reliable controlling environment. In addition, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted for this work and the parameters of the controller are being updated by TD3 algorithm using only local information. Finally, a simulated result of inter-area multi-machine power system is given, where the result shows the effectiveness of stable voltage maintenance and preventing voltage collapse.
{"title":"Enhancing Voltage Stability of Inter-Area Multi-Machine Power Systems using Reinforcement Learning-based STATCOM","authors":"M. Iqbal, A. Hossain, Jahidul Islam, Amit Shaha Surja, M. Kabir","doi":"10.1109/icaeee54957.2022.9836362","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836362","url":null,"abstract":"In this paper, a novel control scheme based on Reinforcement Learning (RL) controller for the Static Synchronous Compensator (STATCOM) is presented for inter-area multi-machine power system. It is a common criterion to develop an improved control strategy for STATCOM to enhance the voltage stability of multi-machine power system. To exemplify on this purpose, a STATCOM controller has been designed which does not depend on the structure and parameters of power system. Moreover, the proposed strategy also replaces the conventional PI controller by RL controller and provides more reliable controlling environment. In addition, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted for this work and the parameters of the controller are being updated by TD3 algorithm using only local information. Finally, a simulated result of inter-area multi-machine power system is given, where the result shows the effectiveness of stable voltage maintenance and preventing voltage collapse.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294339","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 : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836479
Marzia Ahmed, Rony Shaha, Kaushik Sarker, Rifat Bin Mahi, M. A. Kashem
Rapid population expansion necessitated increased resource use in everyday living. As a result, the pace of trash gen-eration has increased dramatically, affecting the environment's hygiene system and other health concerns. Waste overflows in public spaces, and improved management is necessary. The purpose of this study is to develop a model of an intelligent trashcan for usage in smart cities. Additionally, to identify dangerous gases emitted by dustbins for subsequent management operations, as well as to monitor the amount of trash in the waste bin and warn the municipality through SMS. This system includes two ultrasonic sonar sensors for measuring trash level, a GSM module for sending SMS, three gas sensors for detecting harmful garbage gas, an infrared sensor for counting garbage droplets, and an Arduino Uno for managing all activities. The system notifies you whether the bin is full or empty and can also be controlled by voice command. Additionally, released gas may be monitored to determine the severity of the impairment and to notify the appropriate authorities. Most significantly, it will identify a failed trash drop in the bin and alert the user through alarm for truly considering the reduction of spilled garbage surrounding bins while using the system.
{"title":"Design and Implementation of Intelligent Dustbin with Garbage Gas Detection for Hygienic Environment based on IoT","authors":"Marzia Ahmed, Rony Shaha, Kaushik Sarker, Rifat Bin Mahi, M. A. Kashem","doi":"10.1109/icaeee54957.2022.9836479","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836479","url":null,"abstract":"Rapid population expansion necessitated increased resource use in everyday living. As a result, the pace of trash gen-eration has increased dramatically, affecting the environment's hygiene system and other health concerns. Waste overflows in public spaces, and improved management is necessary. The purpose of this study is to develop a model of an intelligent trashcan for usage in smart cities. Additionally, to identify dangerous gases emitted by dustbins for subsequent management operations, as well as to monitor the amount of trash in the waste bin and warn the municipality through SMS. This system includes two ultrasonic sonar sensors for measuring trash level, a GSM module for sending SMS, three gas sensors for detecting harmful garbage gas, an infrared sensor for counting garbage droplets, and an Arduino Uno for managing all activities. The system notifies you whether the bin is full or empty and can also be controlled by voice command. Additionally, released gas may be monitored to determine the severity of the impairment and to notify the appropriate authorities. Most significantly, it will identify a failed trash drop in the bin and alert the user through alarm for truly considering the reduction of spilled garbage surrounding bins while using the system.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115283426","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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