Over the past years, Alzheimer's disease has emerged as a serious concern for people's health. Researchers are facing challenges in effectively categorizing and diagnosing the different stages of Alzheimer's disease (AD). Current promising studies have shown that multimodal Neuroimaging has the potential to offer vital information about the structural and functional alterations associated with Alzheimer's. Using advanced computational techniques, Machine Learning calculations have been demonstrated to be highly precise in deciphering patterns and connections within the multimodal Neuroimaging data, eventually aiding in the arrangement of Alzheimer's illness stages. This research aimed to survey the adequacy of Machine Learning techniques in correctly categorizing stages of Alzheimer's disease by working on multiple neuroimaging modalities. In this review, a detailed analysis was carried out on the classification algorithms included. The study specifically examines publications published between 2016 and 2024. From the review, it was found that deep learning frameworks are more robust in Alzheimer's disease classification.
{"title":"Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review","authors":"Prashant Upadhyay , Pradeep Tomar , Satya Prakash Yadav","doi":"10.1016/j.compeleceng.2024.109796","DOIUrl":"10.1016/j.compeleceng.2024.109796","url":null,"abstract":"<div><div>Over the past years, Alzheimer's disease has emerged as a serious concern for people's health. Researchers are facing challenges in effectively categorizing and diagnosing the different stages of Alzheimer's disease (AD). Current promising studies have shown that multimodal Neuroimaging has the potential to offer vital information about the structural and functional alterations associated with Alzheimer's. Using advanced computational techniques, Machine Learning calculations have been demonstrated to be highly precise in deciphering patterns and connections within the multimodal Neuroimaging data, eventually aiding in the arrangement of Alzheimer's illness stages. This research aimed to survey the adequacy of Machine Learning techniques in correctly categorizing stages of Alzheimer's disease by working on multiple neuroimaging modalities. In this review, a detailed analysis was carried out on the classification algorithms included. The study specifically examines publications published between 2016 and 2024. From the review, it was found that deep learning frameworks are more robust in Alzheimer's disease classification.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109796"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.compeleceng.2024.109832
Mohsen Ghorbian, Mostafa Ghobaei-Arani
In recent years, serverless computing has become one of the popular approaches to developing and running applications, allowing developers to run their code directly in the cloud without worrying about managing server infrastructure. One of the critical aspects of serverless computing is offloading approaches, which refers to transferring computing tasks or data to other locations to reduce the processing load of local devices. Considering the use of different approaches and strategies in the offloading process in serverless computing, not choosing the right approach can cause the unloading process to face challenges such as network delay, security problems, and complexity of resource management. Therefore, a detailed understanding of the loading approaches used in serverless computing can significantly reduce the challenges in this process. This paper provides a comprehensive and systematic review of various commonly used offloading approaches in serverless computing in the form of a taxonomy. The applied approaches are based on machine learning (ML), frameworks, in-network computing (INC), and heuristics. This classification is done to identify the strengths and weaknesses of each of these approaches to help developers improve the productivity and efficiency of their systems by choosing the best offloading strategies. Another goal of this article is to identify and analyze open challenges and issues related to the offloading process in serverless computing to propose effective solutions to these challenges and provide future research directions. Finally, this article expands the existing knowledge in the offloading field and creates new fields for research and development.
{"title":"Function offloading approaches in serverless computing: A Survey","authors":"Mohsen Ghorbian, Mostafa Ghobaei-Arani","doi":"10.1016/j.compeleceng.2024.109832","DOIUrl":"10.1016/j.compeleceng.2024.109832","url":null,"abstract":"<div><div>In recent years, serverless computing has become one of the popular approaches to developing and running applications, allowing developers to run their code directly in the cloud without worrying about managing server infrastructure. One of the critical aspects of serverless computing is offloading approaches, which refers to transferring computing tasks or data to other locations to reduce the processing load of local devices. Considering the use of different approaches and strategies in the offloading process in serverless computing, not choosing the right approach can cause the unloading process to face challenges such as network delay, security problems, and complexity of resource management. Therefore, a detailed understanding of the loading approaches used in serverless computing can significantly reduce the challenges in this process. This paper provides a comprehensive and systematic review of various commonly used offloading approaches in serverless computing in the form of a taxonomy. The applied approaches are based on machine learning (ML), frameworks, in-network computing (INC), and heuristics. This classification is done to identify the strengths and weaknesses of each of these approaches to help developers improve the productivity and efficiency of their systems by choosing the best offloading strategies. Another goal of this article is to identify and analyze open challenges and issues related to the offloading process in serverless computing to propose effective solutions to these challenges and provide future research directions. Finally, this article expands the existing knowledge in the offloading field and creates new fields for research and development.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109832"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.compeleceng.2024.109840
Georgios Fotis
The increasing number of electric vehicles (EVs) will result in a rise in electric vehicle charging stations (EVCSs), which will have a significant effect on the electrical grid. One major issue is deciding where to place EVCSs in the power grid in the most optimal way. The distribution network is greatly impacted by inadequate EVCS prediction, which results in issues with frequency and voltage stability. This paper suggests an optimization method called Binary Random Dynamic Arithmetic Optimization Algorithm (BRDAOA) that is applied on an IEEE 33 bus network to determine the best position for EVCSs as efficiently as possible, and the Loss Sensitivity Factor (LSF) was used in the analysis. Considering the system voltage, the load (actual power), and the system losses, LSF was calculated for a variety of buses. The efficacy of the suggested method is demonstrated by a final comparison of its findings with those of the Arithmetic Optimization Algorithm (AOA) and two additional metaheuristic algorithms. In addition to reducing line losses by 2% compared to the AOA method and 4% compared to the other two metaheuristic optimization methods, the suggested optimization approach known as BRDAOA requires less computing time than the other three methods. Finally, a reliability test was conducted to determine the best location for EVCS in the IEEE 33 BUS system.
{"title":"An improved arithmetic method for determining the optimum placement and size of EV charging stations","authors":"Georgios Fotis","doi":"10.1016/j.compeleceng.2024.109840","DOIUrl":"10.1016/j.compeleceng.2024.109840","url":null,"abstract":"<div><div>The increasing number of electric vehicles (EVs) will result in a rise in electric vehicle charging stations (EVCSs), which will have a significant effect on the electrical grid. One major issue is deciding where to place EVCSs in the power grid in the most optimal way. The distribution network is greatly impacted by inadequate EVCS prediction, which results in issues with frequency and voltage stability. This paper suggests an optimization method called Binary Random Dynamic Arithmetic Optimization Algorithm (BRDAOA) that is applied on an IEEE 33 bus network to determine the best position for EVCSs as efficiently as possible, and the Loss Sensitivity Factor (LSF) was used in the analysis. Considering the system voltage, the load (actual power), and the system losses, LSF was calculated for a variety of buses. The efficacy of the suggested method is demonstrated by a final comparison of its findings with those of the Arithmetic Optimization Algorithm (AOA) and two additional metaheuristic algorithms. In addition to reducing line losses by 2% compared to the AOA method and 4% compared to the other two metaheuristic optimization methods, the suggested optimization approach known as BRDAOA requires less computing time than the other three methods. Finally, a reliability test was conducted to determine the best location for EVCS in the IEEE 33 BUS system.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109840"},"PeriodicalIF":4.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.compeleceng.2024.109823
Jun-Hyeok Kim
This study proposes and validates an artificial intelligence (AI)-based method for estimating the water content in the insulating oil of distribution-level transformers. The methodology includes data augmentation using noise addition, outlier removal via Isolation Forest, and data normalization through square root transformation. A Random Forest (RF) model is developed to estimate water content based on the usage period of the transformer. Correlation analyses identified the usage period as the key variable affecting water content. The model demonstrated high estimation accuracy with an R-squared value of 0.83, closely aligning estimated values with measured data. This approach provides a practical solution for real-world applications, expanding the focus to distribution-level transformers and ensuring reliable estimations through validation with actual field data. Despite limitations due to a dataset comprising 100 samples of transformer usage and oil analysis data, the method shows promise for accurate transformer lifespan assessment and efficient asset management. Future research will enhance model performance by incorporating diverse environmental conditions and comparative analyses with other machine learning (ML) algorithms, aiming to optimize estimation reliability and safety for distribution-level transformers. Consistency in the methodology description and actual models used will be maintained to avoid discrepancies.
{"title":"A study on the water content in distribution pole transformer using random forest model","authors":"Jun-Hyeok Kim","doi":"10.1016/j.compeleceng.2024.109823","DOIUrl":"10.1016/j.compeleceng.2024.109823","url":null,"abstract":"<div><div>This study proposes and validates an artificial intelligence (AI)-based method for estimating the water content in the insulating oil of distribution-level transformers. The methodology includes data augmentation using noise addition, outlier removal via Isolation Forest, and data normalization through square root transformation. A Random Forest (RF) model is developed to estimate water content based on the usage period of the transformer. Correlation analyses identified the usage period as the key variable affecting water content. The model demonstrated high estimation accuracy with an R-squared value of 0.83, closely aligning estimated values with measured data. This approach provides a practical solution for real-world applications, expanding the focus to distribution-level transformers and ensuring reliable estimations through validation with actual field data. Despite limitations due to a dataset comprising 100 samples of transformer usage and oil analysis data, the method shows promise for accurate transformer lifespan assessment and efficient asset management. Future research will enhance model performance by incorporating diverse environmental conditions and comparative analyses with other machine learning (ML) algorithms, aiming to optimize estimation reliability and safety for distribution-level transformers. Consistency in the methodology description and actual models used will be maintained to avoid discrepancies.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109823"},"PeriodicalIF":4.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.compeleceng.2024.109830
Iman Firmansyah , Bambang Setiadi , Agus Subekti , Heri Nugraha , Edi Kurniawan , Yoshiki Yamaguchi
The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.
{"title":"Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework","authors":"Iman Firmansyah , Bambang Setiadi , Agus Subekti , Heri Nugraha , Edi Kurniawan , Yoshiki Yamaguchi","doi":"10.1016/j.compeleceng.2024.109830","DOIUrl":"10.1016/j.compeleceng.2024.109830","url":null,"abstract":"<div><div>The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109830"},"PeriodicalIF":4.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.compeleceng.2024.109841
Hesam addin Yousefian, Abolfazl Jalilvand, Amir Bagheri
The climatic circumstances of the world have altered due to the world warming up, and this issue has increased high-impact and low-probability (HILP) events more than before. Supplying energy requirements has turned into one of the main challenges of utilities especially electrical distribution companies considering the frequency and intensity of HILP events. On the other hand, developments in storing electricity have varied expectations and will change the solutions leading to resilient electrical distribution networks (EDNs). Some researchers have studied and analyzed numerous aspects of resilient EDNs but hybridization of different types of energy storage systems (ESSs) has not evaluated before. This paper considers energy management of emergency-operated EDNs equipped with two different types of energy storage systems which are batteries and flywheels. Convex equations in all parts of the problem, including different types of energy storage systems are proposed and modeled as an MIQCP to optimize the resilient networks considering all limitations. The proposed framework is developed in GAMS software and the results are provided in the form of Pareto optimal solutions. Applicability of the conducted model is evaluated by the IEEE 33-bus test system aiming at outstanding the effects of flywheels in improving the resiliency of electrical distribution networks. The proposed model analyzed by various energy storing scenarios based on technical and economical limitations. Results showed that among the considered case studies, the 50 % of the cases included with flywheel while batteries participated in 30 % that were the most expensive ones. On the other hand, the lowest amount of objective function belongs to the case that is only included with flywheels. Accordingly, by considering flywheels as a newly born energy storage system in the emergency-operated EDNs, the flexibility of energy management is facilitated and can be developed economically.
{"title":"Multi-type energy conversion for managing the consumption by enhancing the resiliency of electrical distribution networks","authors":"Hesam addin Yousefian, Abolfazl Jalilvand, Amir Bagheri","doi":"10.1016/j.compeleceng.2024.109841","DOIUrl":"10.1016/j.compeleceng.2024.109841","url":null,"abstract":"<div><div>The climatic circumstances of the world have altered due to the world warming up, and this issue has increased high-impact and low-probability (HILP) events more than before. Supplying energy requirements has turned into one of the main challenges of utilities especially electrical distribution companies considering the frequency and intensity of HILP events. On the other hand, developments in storing electricity have varied expectations and will change the solutions leading to resilient electrical distribution networks (EDNs). Some researchers have studied and analyzed numerous aspects of resilient EDNs but hybridization of different types of energy storage systems (ESSs) has not evaluated before. This paper considers energy management of emergency-operated EDNs equipped with two different types of energy storage systems which are batteries and flywheels. Convex equations in all parts of the problem, including different types of energy storage systems are proposed and modeled as an MIQCP to optimize the resilient networks considering all limitations. The proposed framework is developed in GAMS software and the results are provided in the form of Pareto optimal solutions. Applicability of the conducted model is evaluated by the IEEE 33-bus test system aiming at outstanding the effects of flywheels in improving the resiliency of electrical distribution networks. The proposed model analyzed by various energy storing scenarios based on technical and economical limitations. Results showed that among the considered case studies, the 50 % of the cases included with flywheel while batteries participated in 30 % that were the most expensive ones. On the other hand, the lowest amount of objective function belongs to the case that is only included with flywheels. Accordingly, by considering flywheels as a newly born energy storage system in the emergency-operated EDNs, the flexibility of energy management is facilitated and can be developed economically.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109841"},"PeriodicalIF":4.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compeleceng.2024.109786
Rohit Beniwal, Pavi Saraswat
Due to the psychological strain that depression causes, there has been a noticeable increase in the number of persons compromising their lives in recent years. Social media platforms provide researchers with an entirely novel viewpoint on identifying individuals who are depressed. Previous research on automatic learning models for depression detection revealed low detection accuracy and an absence of optimizing techniques that could enhance detection accuracy. Furthermore, there is no such dataset, and very little study has been done on the multimodal pure Hindi and code-mixed Hinglish language domains. In light of this, we developed a Hindi dataset and suggested reliable methods for depression detection based on multimodal data, i.e., text and images, using the Hindi and Hinglish languages. This study aims to accomplish three things: first, it will evaluate text data using an effective Bidirectional Encoder Representations from Transformers (BERT) approach and compare it with other transfer learning variants; second, it will analyze image data by suggesting a Convolutional Neural Network (CNN) optimized with a nature-inspired algorithm, namely Particle Swarm Optimization (PSO), or CPSO; and third, it will classify the multimodal data into depressive and non-depressive posts by suggesting a hybrid of the best-performing models on text and images, namely BERT-CPSO (BTCPSO). The results produced with the BERT model showed the best accuracy of 95% for text data, in contrast to RoBERTa, DistilBERT, and XLNet. Further, CPSO outperforms other Machine Learning (ML) and Deep Learning (DL) algorithms for image data with an accuracy of 95%. Additionally, comparing the proposed CPSO with a basic CNN revealed that integrating the PSO technique with CNN increased the model's accuracy in detecting depressed posts by 5%. In conclusion, hybrid BERT-CPSO outperforms other BERT combinations with ML and DL algorithms for multimodal data, achieving 97%, 95%, 98%, and 96%, respectively, in accuracy, recall, precision, and F1-scores. As a result, the findings of comparing the suggested technique with the earlier models show the effectiveness of the approach that has been provided and can help medical professionals diagnose depression with precision.
{"title":"A hybrid BERT-CPSO model for multi-class depression detection using pure hindi and hinglish multimodal data on social media","authors":"Rohit Beniwal, Pavi Saraswat","doi":"10.1016/j.compeleceng.2024.109786","DOIUrl":"10.1016/j.compeleceng.2024.109786","url":null,"abstract":"<div><div>Due to the psychological strain that depression causes, there has been a noticeable increase in the number of persons compromising their lives in recent years. Social media platforms provide researchers with an entirely novel viewpoint on identifying individuals who are depressed. Previous research on automatic learning models for depression detection revealed low detection accuracy and an absence of optimizing techniques that could enhance detection accuracy. Furthermore, there is no such dataset, and very little study has been done on the multimodal pure Hindi and code-mixed Hinglish language domains. In light of this, we developed a Hindi dataset and suggested reliable methods for depression detection based on multimodal data, i.e., text and images, using the Hindi and Hinglish languages. This study aims to accomplish three things: first, it will evaluate text data using an effective Bidirectional Encoder Representations from Transformers (BERT) approach and compare it with other transfer learning variants; second, it will analyze image data by suggesting a Convolutional Neural Network (CNN) optimized with a nature-inspired algorithm, namely Particle Swarm Optimization (PSO), or CPSO; and third, it will classify the multimodal data into depressive and non-depressive posts by suggesting a hybrid of the best-performing models on text and images, namely BERT-CPSO (BTCPSO). The results produced with the BERT model showed the best accuracy of 95% for text data, in contrast to RoBERTa, DistilBERT, and XLNet. Further, CPSO outperforms other Machine Learning (ML) and Deep Learning (DL) algorithms for image data with an accuracy of 95%. Additionally, comparing the proposed CPSO with a basic CNN revealed that integrating the PSO technique with CNN increased the model's accuracy in detecting depressed posts by 5%. In conclusion, hybrid BERT-CPSO outperforms other BERT combinations with ML and DL algorithms for multimodal data, achieving 97%, 95%, 98%, and 96%, respectively, in accuracy, recall, precision, and F1-scores. As a result, the findings of comparing the suggested technique with the earlier models show the effectiveness of the approach that has been provided and can help medical professionals diagnose depression with precision.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109786"},"PeriodicalIF":4.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compeleceng.2024.109821
Rui Fei, Jianwen Cui
This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reliability, and resource utilization.
{"title":"Cooperative resource sharing and cost minimization in energy hub systems using an improved grasshopper optimization algorithm approach","authors":"Rui Fei, Jianwen Cui","doi":"10.1016/j.compeleceng.2024.109821","DOIUrl":"10.1016/j.compeleceng.2024.109821","url":null,"abstract":"<div><div>This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reliability, and resource utilization.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109821"},"PeriodicalIF":4.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compeleceng.2024.109714
Liang Ning
Distributed generation sources provide self-governing power during outages, making microgrids and islanded distribution networks vital for service endurance, superior power quality, reliability, and operative efficiency. However, microgrids structure are difficult to control, particularly in islanded mode where no main power source exists if the main grid fails. Fast responses from discrete generation sources using power electronics can undermine the grid during faults or normal operations without proper regulations. The double-fed induction generator (DFIG) has become the preferred wind turbine generator owing to its low cost and flexibility to varying wind speeds. This paper presents a probabilistic scheduling for day-ahead microgrid programming that includes EV parking lots and dispersed generation resources. The microgrid works in both normal and islanded modes depending on main grid conditions. The uncertainty in EV parking lot usage is modeled hourly using the Z-number method, while wind and solar generation, market prices, and loads are modeled using the Monte Carlo method. Scenario-based incidents in the upstream grid that lead to microgrid islanding are considered, focusing on the time and duration of impact. The optimization model accounts for uncertainty, EV charging/discharging, and operational costs under normal and fault conditions. The fault ride-through (FRT) method for maintaining DFIG stability in islanded microgrids are proposed. In this technique stabilizes terminal voltage during faults by employing a resistor in series with the DFIG stator, enhancing voltage stability and FRT capability. Without these methods, the DFIG may lose stability after clearing transient errors, risking generator loss and threatening microgrid stability, particularly in islanded mode. The effectiveness of these control and protection strategies is validated through comprehensive simulations in MATLAB.
分布式发电在停电期间提供自我管理的电力，这使得微电网和孤岛式配电网络对服务的持久性、卓越的电能质量、可靠性和运行效率至关重要。然而，微电网结构难以控制，特别是在孤岛模式下，如果主电网发生故障，就不存在主电源。在故障或正常运行期间，如果没有适当的规定，使用电力电子设备的离散发电源的快速反应可能会破坏电网。双馈异步发电机（DFIG）因其低成本和适应不同风速的灵活性，已成为风力涡轮发电机的首选。本文介绍了一种用于日前微电网编程的概率调度方法，其中包括电动汽车停车场和分散的发电资源。根据主电网条件，微电网可在正常模式和孤岛模式下工作。电动汽车停车场使用情况的不确定性采用 Z 数法按小时建模，而风能和太阳能发电、市场价格和负荷则采用蒙特卡罗法建模。考虑了上游电网中导致微电网孤岛的情景事件，重点关注影响的时间和持续时间。优化模型考虑了不确定性、电动汽车充电/放电以及正常和故障条件下的运营成本。提出了故障穿越（FRT）方法，用于维持孤岛微电网中 DFIG 的稳定性。该技术通过采用与双馈变流器定子串联的电阻器来稳定故障期间的终端电压，从而提高电压稳定性和故障穿越能力。如果不采用这些方法，DFIG 可能会在清除瞬态误差后失去稳定性，从而导致发电机损耗并威胁微电网的稳定性，尤其是在孤岛模式下。这些控制和保护策略的有效性通过 MATLAB 的全面仿真得到了验证。
{"title":"Probabilistic modeling and optimization of microgrids with EV parking lots and dispersed generation","authors":"Liang Ning","doi":"10.1016/j.compeleceng.2024.109714","DOIUrl":"10.1016/j.compeleceng.2024.109714","url":null,"abstract":"<div><div>Distributed generation sources provide self-governing power during outages, making microgrids and islanded distribution networks vital for service endurance, superior power quality, reliability, and operative efficiency. However, microgrids structure are difficult to control, particularly in islanded mode where no main power source exists if the main grid fails. Fast responses from discrete generation sources using power electronics can undermine the grid during faults or normal operations without proper regulations. The double-fed induction generator (DFIG) has become the preferred wind turbine generator owing to its low cost and flexibility to varying wind speeds. This paper presents a probabilistic scheduling for day-ahead microgrid programming that includes EV parking lots and dispersed generation resources. The microgrid works in both normal and islanded modes depending on main grid conditions. The uncertainty in EV parking lot usage is modeled hourly using the Z-number method, while wind and solar generation, market prices, and loads are modeled using the Monte Carlo method. Scenario-based incidents in the upstream grid that lead to microgrid islanding are considered, focusing on the time and duration of impact. The optimization model accounts for uncertainty, EV charging/discharging, and operational costs under normal and fault conditions. The fault ride-through (FRT) method for maintaining DFIG stability in islanded microgrids are proposed. In this technique stabilizes terminal voltage during faults by employing a resistor in series with the DFIG stator, enhancing voltage stability and FRT capability. Without these methods, the DFIG may lose stability after clearing transient errors, risking generator loss and threatening microgrid stability, particularly in islanded mode. The effectiveness of these control and protection strategies is validated through comprehensive simulations in MATLAB.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109714"},"PeriodicalIF":4.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compeleceng.2024.109813
Wenjie Wang , Gang Yuan , Duc Truong Pham , Honghao Zhang , Dekun Wang , Guangdong Tian
The well-accepted three-stage remanufacturing system scheduling aims to achieve intelligent and green remanufacturing by reasonably coordinating limited resources in the system involving disassembly, reprocessing, reassembly production stages. Currently, the lot-streaming production mode is increasingly favoured by scholars and enterprise managers due to its remarkable performance in reducing machines’ idle time and improving production efficiency. This paper investigates an energy-efficient scheduling issue for three-stage remanufacturing systems under the lot-streaming environment where each large-sized lot is split into its constituent small-sized sublots whose sizes may be inequal but remain consistent among various operations. Foremost, a dual-objective optimization mathematical model aiming at concurrently minimizing the makespan and total energy consumption is built. Then, since its NP-hard property, an improved fruit fly optimization (IFFO) algorithm is accordingly introduced. IFFO adopts a problem-specific three-layer encoding mechanism that contains three key pieces of scheduling information, i.e., lot sequence, machine assignment, and lot size splitting. Besides, based on the lot-streaming property, two distinct decoding strategies, i.e., sublot preemption and lot preemption are also correspondingly integrated. In addition, several effective optimization techniques, such as the simulated annealing-based replacement mechanism and Sigma method, are also employed to seek high-quality Pareto solutions. A real case and several designed random small/large-sized instances are tested on IFFO and its peers under three performance indicators. To obtain a convincing and solid conclusion, the Wilcoxon signed-rank statistical test is executed as well. The overall experimental results show that IFFO is feasible and effective in addressing the studied problem.
{"title":"Lot-streaming in energy-efficient three-stage remanufacturing system scheduling problem with inequal and consistent sublots","authors":"Wenjie Wang , Gang Yuan , Duc Truong Pham , Honghao Zhang , Dekun Wang , Guangdong Tian","doi":"10.1016/j.compeleceng.2024.109813","DOIUrl":"10.1016/j.compeleceng.2024.109813","url":null,"abstract":"<div><div>The well-accepted three-stage remanufacturing system scheduling aims to achieve intelligent and green remanufacturing by reasonably coordinating limited resources in the system involving disassembly, reprocessing, reassembly production stages. Currently, the lot-streaming production mode is increasingly favoured by scholars and enterprise managers due to its remarkable performance in reducing machines’ idle time and improving production efficiency. This paper investigates an energy-efficient scheduling issue for three-stage remanufacturing systems under the lot-streaming environment where each large-sized lot is split into its constituent small-sized sublots whose sizes may be inequal but remain consistent among various operations. Foremost, a dual-objective optimization mathematical model aiming at concurrently minimizing the makespan and total energy consumption is built. Then, since its NP-hard property, an improved fruit fly optimization (IFFO) algorithm is accordingly introduced. IFFO adopts a problem-specific three-layer encoding mechanism that contains three key pieces of scheduling information, i.e., lot sequence, machine assignment, and lot size splitting. Besides, based on the lot-streaming property, two distinct decoding strategies, i.e., sublot preemption and lot preemption are also correspondingly integrated. In addition, several effective optimization techniques, such as the simulated annealing-based replacement mechanism and Sigma method, are also employed to seek high-quality Pareto solutions. A real case and several designed random small/large-sized instances are tested on IFFO and its peers under three performance indicators. To obtain a convincing and solid conclusion, the Wilcoxon signed-rank statistical test is executed as well. The overall experimental results show that IFFO is feasible and effective in addressing the studied problem.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109813"},"PeriodicalIF":4.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号