Based on the semi-active suspension controller of an automobile, the control law can be adjusted based on the control law reorganization idea, and the active fault-tolerant controller of the semi-active suspension is designed to make the fault closed loop system and the fault-free suspension semi-active suspension. Active suspension closed-loop systems have the same closed-loop pole or proximity system performance. Bench test and simulation results show that: the fault suspension under the control of the active fault-tolerant controller lags behind its performance level after some time and can quickly recover to the same performance as the fault-free automotive semi-active suspension level. And the simulation test and bench test results are basically consistent. Based on the concept of control law reorganization to design the active fault-tolerant control strategy of semi-active suspension, it can effectively realize the active fault-tolerant control of the semi-active suspension of the vehicle to improve the suspension control quality and reliability, and optimize the suspension design.
{"title":"Active fault-tolerant control and performance simulation of electric vehicle suspension based on improved algorithms","authors":"Caiyuan Xiao","doi":"10.4108/ew.6146","DOIUrl":"https://doi.org/10.4108/ew.6146","url":null,"abstract":"Based on the semi-active suspension controller of an automobile, the control law can be adjusted based on the control law reorganization idea, and the active fault-tolerant controller of the semi-active suspension is designed to make the fault closed loop system and the fault-free suspension semi-active suspension. Active suspension closed-loop systems have the same closed-loop pole or proximity system performance. Bench test and simulation results show that: the fault suspension under the control of the active fault-tolerant controller lags behind its performance level after some time and can quickly recover to the same performance as the fault-free automotive semi-active suspension level. And the simulation test and bench test results are basically consistent. Based on the concept of control law reorganization to design the active fault-tolerant control strategy of semi-active suspension, it can effectively realize the active fault-tolerant control of the semi-active suspension of the vehicle to improve the suspension control quality and reliability, and optimize the suspension design.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826641","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}
Mingyue Liu, L. Rajamanickam, Rajamohan Parthasarathy
The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.
{"title":"Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems","authors":"Mingyue Liu, L. Rajamanickam, Rajamohan Parthasarathy","doi":"10.4108/ew.6503","DOIUrl":"https://doi.org/10.4108/ew.6503","url":null,"abstract":"The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"23 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684106","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}
The study into Wireless Sensor Network (WSN) has grown more crucial as a result of the many Internet of Things (IoT) applications. Energy – Harvesting (EH) technology can extend the lifespan of WSN; however, because the nodes would be difficult to get to during energy harvesting, an energy-efficient routing protocol should be developed. The use of clustering in this study balances energy consumption across all Sensor Node (SN) and reduces traffic and overhead throughout the data transmission phases of WSN. Cluster Head (CH) selection step of the Optimized Energy Efficient-Hierarchical Clustering Based Routing (OEE-HCR) technique involves sending data to the closest CH. In order to analyse and transmit each cluster data, CH will need to use more energy, which will hasten and asymmetrically deplete the network. Whale Optimization Algorithm (WOA) algorithm is introduced for the best number of clusters formation with dynamically selecting the CH. Experimentation analysis, results are measured using First Node Dead (FND), the Half Node Dead (HND), Last Node Dead (LND), and Maximum Lifetime Coverage (MLC) at the time of number of data transmission rounds performed in routing algorithms.
{"title":"Optimized Energy Efficient- Hierarchical Clustering Based Routing (OEE-HCR) For Wireless Sensor Network (WSN)","authors":"G. Sophia Reena, S. Nithya","doi":"10.4108/ew.6504","DOIUrl":"https://doi.org/10.4108/ew.6504","url":null,"abstract":"The study into Wireless Sensor Network (WSN) has grown more crucial as a result of the many Internet of Things (IoT) applications. Energy – Harvesting (EH) technology can extend the lifespan of WSN; however, because the nodes would be difficult to get to during energy harvesting, an energy-efficient routing protocol should be developed. The use of clustering in this study balances energy consumption across all Sensor Node (SN) and reduces traffic and overhead throughout the data transmission phases of WSN. Cluster Head (CH) selection step of the Optimized Energy Efficient-Hierarchical Clustering Based Routing (OEE-HCR) technique involves sending data to the closest CH. In order to analyse and transmit each cluster data, CH will need to use more energy, which will hasten and asymmetrically deplete the network. Whale Optimization Algorithm (WOA) algorithm is introduced for the best number of clusters formation with dynamically selecting the CH. Experimentation analysis, results are measured using First Node Dead (FND), the Half Node Dead (HND), Last Node Dead (LND), and Maximum Lifetime Coverage (MLC) at the time of number of data transmission rounds performed in routing algorithms.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"207 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681404","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}
Recently, there has been a need for connectivity in places with no infrastructure. In order to meet this need, new technology known as MANET is used to fulfil the market demand. Despite the many benefits that MANET will provide, a number of shortcomings still need to be further studied, especially the energy consumption problems, that stand in the way of not allowing widespread acceptance of this technology. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management.
{"title":"Energy Aware On-Demand Routing Protocol Scheme of DSR Protocol (EAORP)","authors":"Hatem Sunni Tarus, Rajamohan Partharathy","doi":"10.4108/ew.6500","DOIUrl":"https://doi.org/10.4108/ew.6500","url":null,"abstract":"Recently, there has been a need for connectivity in places with no infrastructure. In order to meet this need, new technology known as MANET is used to fulfil the market demand. Despite the many benefits that MANET will provide, a number of shortcomings still need to be further studied, especially the energy consumption problems, that stand in the way of not allowing widespread acceptance of this technology. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680580","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}
BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.
无刷直流电机广泛应用于各行各业，包括数控机床、工业机器人和电动汽车。无刷直流电机体积小、效率高、扭矩功率比大，而且无刷运行维护要求低，因此成为许多工业自动化系统的支柱。然而，它们在速度控制方面却面临着巨大的挑战。在本研究中，无刷直流电机的速度由基于 PI 的速度控制器控制。在所述方法中，BLDC 电机使用霍尔效应传感器进行换向。所提出的方法使用 PI 以开环 PWM 方法调节 BLDC 电机的速度。使用 MATLAB/simulink对调速无刷直流电机进行了分析。还实现了拟议系统的硬件。
{"title":"Switched capacitor voltage boost converter for BLDC motor speed control of electric vehicles","authors":"Srinivasan P, M. K, Roshan M, Nissy Joseph","doi":"10.4108/ew.6036","DOIUrl":"https://doi.org/10.4108/ew.6036","url":null,"abstract":"BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"58 s204","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682107","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}
Prathigadapa Sireesha, Vishnu Priyan S, M. Govindarajan, Sounder Rajan, V. Rajakumareswaran
INTRODUCTION: This is the introductory text. Accurate data center resource projection will be challenging due to the dynamic and constantly changing workloads of multi-tenant co-hosted applications. Resource Management in the Cloud (RMC) becomes a significant research component. In the cloud's easy service option, users can choose to pay a fixed sum or based on the amount of time. OBJECTIVES: The main goal of this study is systematic method for estimating future cloud resource requirements based on historical consumption. Resource distribution to users, who require a variety of resources, is one of cloud computing main objective in this study. METHODS: This article suggests a Layer optimized based Long Short-Term Memory (LOLSTM) to estimate the resource requirements for upcoming time slots. This model also detects SLA violations when the QoS value exceeds the dynamic threshold value, and it then proposes the proper countermeasures based on the risk involved with the violation. RESULTS: Results indicate that in terms of training and validation the accuracy is 97.6%, 95.9% respectively, RMSE and MAD shows error rate 0.127 and 0.107, The proposed method has a minimal training and validation loss at epoch 100 are 0.6092 and 0.5828, respectively. So, the suggested technique performed better than the current techniques. CONCLUSION: In this work, the resource requirements for future time slots are predicted using LOLSTM technique. It regularizes the weights of the network and avoids overfitting. In addition, the proposed work also takes necessary actions if the SLA violation is recognized by the model. Overall, the proposed work in this study shows better performance compared to the existing methods.
{"title":"Revolutionizing Cloud Resource Allocation: Harnessing Layer-Optimized Long Short-Term Memory for Energy-Efficient Predictive Resource Management","authors":"Prathigadapa Sireesha, Vishnu Priyan S, M. Govindarajan, Sounder Rajan, V. Rajakumareswaran","doi":"10.4108/ew.6505","DOIUrl":"https://doi.org/10.4108/ew.6505","url":null,"abstract":"INTRODUCTION: This is the introductory text. Accurate data center resource projection will be challenging due to the dynamic and constantly changing workloads of multi-tenant co-hosted applications. Resource Management in the Cloud (RMC) becomes a significant research component. In the cloud's easy service option, users can choose to pay a fixed sum or based on the amount of time. \u0000OBJECTIVES: The main goal of this study is systematic method for estimating future cloud resource requirements based on historical consumption. Resource distribution to users, who require a variety of resources, is one of cloud computing main objective in this study. \u0000METHODS: This article suggests a Layer optimized based Long Short-Term Memory (LOLSTM) to estimate the resource requirements for upcoming time slots. This model also detects SLA violations when the QoS value exceeds the dynamic threshold value, and it then proposes the proper countermeasures based on the risk involved with the violation. \u0000RESULTS: Results indicate that in terms of training and validation the accuracy is 97.6%, 95.9% respectively, RMSE and MAD shows error rate 0.127 and 0.107, The proposed method has a minimal training and validation loss at epoch 100 are 0.6092 and 0.5828, respectively. So, the suggested technique performed better than the current techniques. \u0000CONCLUSION: In this work, the resource requirements for future time slots are predicted using LOLSTM technique. It regularizes the weights of the network and avoids overfitting. In addition, the proposed work also takes necessary actions if the SLA violation is recognized by the model. Overall, the proposed work in this study shows better performance compared to the existing methods.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681189","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}
Dr. Bijay Paikaray, Swarna Prabha Jena, Jayanta Mondal, Nguyen Van Thuan, Nguyen Trong Tung, Chandrakant Mallick
INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable.OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it; embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution.METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables.RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%.CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning.
{"title":"Electricity Consumption Classification using Various Machine Learning Models","authors":"Dr. Bijay Paikaray, Swarna Prabha Jena, Jayanta Mondal, Nguyen Van Thuan, Nguyen Trong Tung, Chandrakant Mallick","doi":"10.4108/ew.6274","DOIUrl":"https://doi.org/10.4108/ew.6274","url":null,"abstract":"INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable.OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it; embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution.METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables.RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%.CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371399","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}
B. Panigrahi, R. K. Kanna, Pragyan Paramita Das, S. Sahoo, Tanusree Dutta
INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.
{"title":"Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application","authors":"B. Panigrahi, R. K. Kanna, Pragyan Paramita Das, S. Sahoo, Tanusree Dutta","doi":"10.4108/ew.6272","DOIUrl":"https://doi.org/10.4108/ew.6272","url":null,"abstract":"INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. \u0000OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. \u0000METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. \u0000RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. \u0000CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"74 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385332","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}
An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.
{"title":"Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid","authors":"Jun Li, Qi Fu, Pei Ruan","doi":"10.4108/ew.5824","DOIUrl":"https://doi.org/10.4108/ew.5824","url":null,"abstract":"An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"8 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101064","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}
We consider a jamming problem in which a jammer aims to degrade a user’s communication in which the user might differ in applied applications or communication purposes. Such differences are reflected by different communication metrics used by the user. Specifically, signal-to-interference-plus-noise ratio (SINR) is used as a metric to reflect regular data transmission purposes. Meanwhile, as another metric, latency, modeled by the inverse SINR, is used to reflect emergency communication purposes. We consider the most difficult scenario for the jammer where it does not know which application (metric) the user employs. The problem is formulated as a Bayesian game. Equilibrium is found in closed form, and the dependence of equilibrium on network parameters is illustrated.
{"title":"A jamming power control game with unknown user’s communication metric","authors":"A. Garnaev, W. Trappe","doi":"10.4108/ew.5991","DOIUrl":"https://doi.org/10.4108/ew.5991","url":null,"abstract":"We consider a jamming problem in which a jammer aims to degrade a user’s communication in which the user might differ in applied applications or communication purposes. Such differences are reflected by different communication metrics used by the user. Specifically, signal-to-interference-plus-noise ratio (SINR) is used as a metric to reflect regular data transmission purposes. Meanwhile, as another metric, latency, modeled by the inverse SINR, is used to reflect emergency communication purposes. We consider the most difficult scenario for the jammer where it does not know which application (metric) the user employs. The problem is formulated as a Bayesian game. Equilibrium is found in closed form, and the dependence of equilibrium on network parameters is illustrated.","PeriodicalId":502230,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"329 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006986","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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