A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.
{"title":"Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting","authors":"Haotian Chen, Xixia Huang","doi":"10.4108/ew.4653","DOIUrl":"https://doi.org/10.4108/ew.4653","url":null,"abstract":"A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"99 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748277","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}
INTRODUCTION: Water Distribution Networks are critical infrastructures that have garnered increasing interest from researchers. OBJECTIVES: This article conducts a bibliometric analysis to examine trends, the geographical distribution of researchers, hot topics, and international cooperation in using Machine Learning for Water Distribution Networks over the past decade. METHODS: Using “water distribution” AND (prediction OR “Machine learning” OR “ML” OR detection OR simulation), as search string, 4859 relevant publications have been retrieved from WoS database. After applying the PRISMA method, we retained 2427 documents for analysis with a Bibliometric library programmed in R. RESULTS: China and the USA are the most productive on the ground, and only one African country appears in this ranking in 14th place. We also identified two ways for future research works, which are: the assessment of water quality and the design of optimisation models. CONCLUSION: The application of this research in African countries would be fascinating for a better quality of service and efficient management of this resource, which is inaccessible to many African countries.
{"title":"Machine Learning Applied to Water Distribution Networks Issues: A Bibliometric Review","authors":"H. Denakpo, P. Houngue, T. Dagba, J. Degila","doi":"10.4108/ew.5567","DOIUrl":"https://doi.org/10.4108/ew.5567","url":null,"abstract":"INTRODUCTION: Water Distribution Networks are critical infrastructures that have garnered increasing interest from researchers. \u0000OBJECTIVES: This article conducts a bibliometric analysis to examine trends, the geographical distribution of researchers, hot topics, and international cooperation in using Machine Learning for Water Distribution Networks over the past decade. \u0000METHODS: Using “water distribution” AND (prediction OR “Machine learning” OR “ML” OR detection OR simulation), as search string, 4859 relevant publications have been retrieved from WoS database. After applying the PRISMA method, we retained 2427 documents for analysis with a Bibliometric library programmed in R. \u0000RESULTS: China and the USA are the most productive on the ground, and only one African country appears in this ranking in 14th place. We also identified two ways for future research works, which are: the assessment of water quality and the design of optimisation models. \u0000CONCLUSION: The application of this research in African countries would be fascinating for a better quality of service and efficient management of this resource, which is inaccessible to many African countries.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"16 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140374554","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}
This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.
本研究论文介绍了一种专为独立微电网设计的智能电力控制方法。所提出的混合系统由各种关键部件组成，包括光伏阵列、超级电容器、直流总线、电池组和交流总线，共同在微电网内发电和储电。为应对生态参数随机波动和负载需求变化带来的挑战，开发了一种监控控制器来增强独立混合微电网。这样就能优化微电网内的电力管理。利本伯格-马夸特（Liebenberg Marquardt）算法用于检索训练有素的 ANN 机器。两层和三层隐藏式 ANN 机器的平均准确率为 96%，而单层 ANN 机器的预测能力较差。使用 MATLAB/Simulink 对所提出的模型进行了实施和分析。模拟实验的观察结果验证了整合可用资源在确保微电网的弹性和可靠性方面的有效性。
{"title":"Smart Control Strategy for Adaptive Management of Islanded Hybrid Microgrids","authors":"S. Poonkuzhali, A. Geetha","doi":"10.4108/ew.5539","DOIUrl":"https://doi.org/10.4108/ew.5539","url":null,"abstract":"This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 1193","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382515","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}
Sanlei Dang, Fusheng Wei, Min Wu, Ruibiao Xie, Jintao Wu
This paper presents the crucial area of power load data acquisition with an integrated transmission system for precise calculation and detection of electric energy. With the advances in technology, management and optimization of energy has become critical for sustainability and economic reasons. Thus, we have targeted the cutting-edge methods for data gathering of power load along with its efficient transmission previously reviewed. We scrutinized the current methods and technologies used in power load data acquisition and identified their limitations along with areas of improvements. We followed advanced sensors and measuring devices for data collection employed an integrated transmission system with up-to-the-minute communication protocols and data processing algorithms. These were experimentally verified to improve the accuracy and reliability of the electric energy calculations. The real-world case studies were included for its practical implementations to provide an insight into its impacts. The results of this study provide a maturing outlook along with valuable analysis for electric energy calculation and detection. The system due to its potential for enhancing the energy management and efficiency can have a real-life and profound significance in sustainable and economic handling of the increasing load of energy.
{"title":"Research on Power Load Data Acquisition and Integrated Transmission Systems in Electric Energy Calculation and Detection","authors":"Sanlei Dang, Fusheng Wei, Min Wu, Ruibiao Xie, Jintao Wu","doi":"10.4108/ew.5521","DOIUrl":"https://doi.org/10.4108/ew.5521","url":null,"abstract":"This paper presents the crucial area of power load data acquisition with an integrated transmission system for precise calculation and detection of electric energy. With the advances in technology, management and optimization of energy has become critical for sustainability and economic reasons. Thus, we have targeted the cutting-edge methods for data gathering of power load along with its efficient transmission previously reviewed. We scrutinized the current methods and technologies used in power load data acquisition and identified their limitations along with areas of improvements. We followed advanced sensors and measuring devices for data collection employed an integrated transmission system with up-to-the-minute communication protocols and data processing algorithms. These were experimentally verified to improve the accuracy and reliability of the electric energy calculations. The real-world case studies were included for its practical implementations to provide an insight into its impacts. The results of this study provide a maturing outlook along with valuable analysis for electric energy calculation and detection. The system due to its potential for enhancing the energy management and efficiency can have a real-life and profound significance in sustainable and economic handling of the increasing load of energy.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140387830","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}
As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.
{"title":"Modelling and Simulation of Grid Connected Wind Turbine Induction Generator for Windfarm","authors":"A. Rathinavel, R. Ramya","doi":"10.4108/ew.5050","DOIUrl":"https://doi.org/10.4108/ew.5050","url":null,"abstract":"As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"274 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799448","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}
As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.
{"title":"Modelling and Simulation of Grid Connected Wind Turbine Induction Generator for Windfarm","authors":"A. Rathinavel, R. Ramya","doi":"10.4108/ew.5050","DOIUrl":"https://doi.org/10.4108/ew.5050","url":null,"abstract":"As the power generation sector moving towards the sustainability to achieve clean and renewable energy source, the wind power generation plays a vital role due to its abundance in nature. A big chunk of the decrease in carbon emission is a major attribute to the growth of the wind energy sector. Wind turbine production, structural development, logistics, maintenance and R&D are just some of the areas that could benefit from the growth of the wind energy industry. This brought out the attention of researchers of the electrical engineering to focus on wind power generation. It can be more efficient and cost-effective to operate wind turbines as a wind farm rather than individually. This has led to a surge in the construction of wind farms, both onshore and offshore wind farms. Therefore, in this paper, the study and analyse of single Induction generator with wind turbines and 33MW windfarm performance is presented. The simulation result demonstrates the efficiency of DFIG in producing energy at a constant wind speed, as well as its ability to regulate both active and reactive power at steady-state.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859092","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}
K. R. Pratiba, S. Ridhanya, J. Ridhisha, P. Hemashree
The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.
{"title":"Integrated Q-Learning with Firefly Algorithm for Transportation Problems","authors":"K. R. Pratiba, S. Ridhanya, J. Ridhisha, P. Hemashree","doi":"10.4108/ew.5047","DOIUrl":"https://doi.org/10.4108/ew.5047","url":null,"abstract":"The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"54 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859778","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}
K. R. Pratiba, S. Ridhanya, J. Ridhisha, P. Hemashree
The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.
{"title":"Integrated Q-Learning with Firefly Algorithm for Transportation Problems","authors":"K. R. Pratiba, S. Ridhanya, J. Ridhisha, P. Hemashree","doi":"10.4108/ew.5047","DOIUrl":"https://doi.org/10.4108/ew.5047","url":null,"abstract":"The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800083","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}
Geetha A, B. K S, Geetha P, Jemimah Carmicharl M, U. S
This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.
{"title":"Optimization of Core Loss for Power Transformer Using Taguchi Method","authors":"Geetha A, B. K S, Geetha P, Jemimah Carmicharl M, U. S","doi":"10.4108/ew.5051","DOIUrl":"https://doi.org/10.4108/ew.5051","url":null,"abstract":"This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859824","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}
Geetha A, B. K S, Geetha P, Jemimah Carmicharl M, U. S
This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.
{"title":"Optimization of Core Loss for Power Transformer Using Taguchi Method","authors":"Geetha A, B. K S, Geetha P, Jemimah Carmicharl M, U. S","doi":"10.4108/ew.5051","DOIUrl":"https://doi.org/10.4108/ew.5051","url":null,"abstract":"This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：