Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705967
Xiaoyou Yu;Tianchu Li;Ziyun Tian;Miao Yu
We propose a novel deep learning (DL) based HBF design for the dual-functional radar-communication (DFRC) system with the millimeter wave (mmWave) massive multiple-in-multiple-output (MIMO) architecture, in which the HBF is formulated as a non-convex optimization problem. First, the DL-based HBF is designed to minimize the sum-MSE of downlink communications while carrying out necessary radar sensing concurrently. Then the synchronization noise is attached to the input channel data to enhance the robustness of the CNN. After that, an attention mechanism is added into the prediction stage to improve the prediction without affecting the accuracy of the prediction results. Finally, the numerical simulation results show significant tradeoff performance improvements between communication and radar sensing can be obtained over existing HBF designs.
{"title":"Deep Learning Based Hybrid Beamforming for mmWave Dual-Functional Radar-Communication","authors":"Xiaoyou Yu;Tianchu Li;Ziyun Tian;Miao Yu","doi":"10.1109/TLA.2024.10705967","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705967","url":null,"abstract":"We propose a novel deep learning (DL) based HBF design for the dual-functional radar-communication (DFRC) system with the millimeter wave (mmWave) massive multiple-in-multiple-output (MIMO) architecture, in which the HBF is formulated as a non-convex optimization problem. First, the DL-based HBF is designed to minimize the sum-MSE of downlink communications while carrying out necessary radar sensing concurrently. Then the synchronization noise is attached to the input channel data to enhance the robustness of the CNN. After that, an attention mechanism is added into the prediction stage to improve the prediction without affecting the accuracy of the prediction results. Finally, the numerical simulation results show significant tradeoff performance improvements between communication and radar sensing can be obtained over existing HBF designs.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"871-880"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705969
Baltazar López-Velasco;Agustin Ruiz-Garcia;José Guillermo Cebada-Reyes;Carlos Alberto Villaseñor-Perea
Modernizing the drying process will reduce agricultural product waste and environmental pollution. The aim of this study was to design a monitoring system based on the internet of things (IoT), temperature and relative humidity for a solar dryer. This system consists of a data collection module that gathers data regarding temperature (Ta), external relative humidity (RH) and on/off time of the solar dryer exhaust fans; a communication module that transmits Ta and RH information via LoRa and Wifi to ThingSpeak for monitoring on a mobile device; and a power module providing electrical power for system operation by solar energy. The operation of the IoT monitoring system was evaluated in three drying experiments of Dominican bananas (Musa paradisiaca var sapientum), in which system performance was satisfactory, allowing the user to visualize graphically in a web and mobile interface the behavior of Ta and RH inside the dryer. The data measured by the system were used to predict banana moisture content with an autoregressive model with exogenous variables (ARX) identified online. The mathematical model found predicted the behavior of moisture content with a good goodness of fit, with values of R2 = 0.99, MSE = 1.2910-5 and MAE = -5.0310-6. The solar dryer allowed reducing the moisture content of bananas up to 19.84 % wet basis (w.b.) in a period of 4 days and by 20.03% w.b. for 5 days in the presence of rainfall.
干燥过程的现代化将减少农产品浪费和环境污染。本研究的目的是为太阳能干燥机设计一个基于物联网（IoT）、温度和相对湿度的监控系统。该系统包括一个数据收集模块，用于收集有关温度（Ta）、外部相对湿度（RH）和太阳能烘干机排风扇开/关时间的数据；一个通信模块，用于通过 LoRa 和 Wifi 向 ThingSpeak 传输 Ta 和 RH 信息，以便在移动设备上进行监控；以及一个电源模块，通过太阳能为系统运行提供电力。在对多米尼加香蕉（Musa paradisiaca var sapientum）进行的三次干燥实验中，对物联网监控系统的运行情况进行了评估，系统性能令人满意，用户可以在网络和移动界面上以图形方式直观地看到干燥机内的温度和相对湿度的变化情况。系统测量到的数据被用来预测香蕉的水分含量，该预测是通过在线识别的外生变量自回归模型（ARX）得出的。所发现的数学模型能很好地预测水分含量的变化，拟合度为 R2 = 0.99，MSE = 1.2910-5 和 MAE = -5.0310-6。太阳能干燥机可在 4 天内将香蕉的湿基含水量降低到 19.84%，在降雨的情况下，5 天内可将湿基含水量降低 20.03%。
{"title":"IoT-based Environmental Monitoring and Prediction of Banana Moisture Content in a Solar Greenhouse Dryer","authors":"Baltazar López-Velasco;Agustin Ruiz-Garcia;José Guillermo Cebada-Reyes;Carlos Alberto Villaseñor-Perea","doi":"10.1109/TLA.2024.10705969","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705969","url":null,"abstract":"Modernizing the drying process will reduce agricultural product waste and environmental pollution. The aim of this study was to design a monitoring system based on the internet of things (IoT), temperature and relative humidity for a solar dryer. This system consists of a data collection module that gathers data regarding temperature (Ta), external relative humidity (RH) and on/off time of the solar dryer exhaust fans; a communication module that transmits Ta and RH information via LoRa and Wifi to ThingSpeak for monitoring on a mobile device; and a power module providing electrical power for system operation by solar energy. The operation of the IoT monitoring system was evaluated in three drying experiments of Dominican bananas (Musa paradisiaca var sapientum), in which system performance was satisfactory, allowing the user to visualize graphically in a web and mobile interface the behavior of Ta and RH inside the dryer. The data measured by the system were used to predict banana moisture content with an autoregressive model with exogenous variables (ARX) identified online. The mathematical model found predicted the behavior of moisture content with a good goodness of fit, with values of R2 = 0.99, MSE = 1.2910-5 and MAE = -5.0310-6. The solar dryer allowed reducing the moisture content of bananas up to 19.84 % wet basis (w.b.) in a period of 4 days and by 20.03% w.b. for 5 days in the presence of rainfall.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"881-890"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Point cloud videos capture the time-varying environment and are widely used for dynamic scene understanding. Existing methods develop effective networks for point cloud videos but do not fully utilize the prior information uncovered during pre-training. Furthermore, relying on a single supervised task with a large amount of manually labeled data may be insufficient to capture the foundational structures in point cloud videos. In this paper, we propose a pre-training framework Query-CP to learn the representations of point cloud videos through multiple self-supervised pretext tasks. First, tokenlevel contrast is developed to predict future features under the guidance of historical information. Using a position-guided autoregressor with learnable queries, the predictions are directly contrasted with corresponding targets in the high-level feature space to capture fine-grained semantics. Second, performing only contrastive learning fails to fully explore the complementary structures and dynamics information. To alleviate this, a decoupled spatio-temporal prediction task is designed, where we use a spatial branch to predict low-level features and a temporal branch to predict timestamps of the target sequence explicitly. By combining the above self-supervised tasks, multi-level information is captured during the pre-training stage. Finally, the encoder is fine-tuned and evaluated for action recognition and dynamic semantic segmentation on three datasets. The results demonstrate the effectiveness of our Query-CP. Especially, compared with the state-of-the-art methods, the fine-tuning accuracy on action recognition improves by 3.23% for 24-frame point cloud videos, and the mean accuracy increases by 4.21%.
{"title":"Learnable Query Contrast and Spatio-temporal Prediction on Point Cloud Video Pre-training","authors":"Xiaoxiao Sheng;Zhiqiang Shen;Longguang Wang;Gang Xiao","doi":"10.1109/TLA.2024.10705970","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705970","url":null,"abstract":"Point cloud videos capture the time-varying environment and are widely used for dynamic scene understanding. Existing methods develop effective networks for point cloud videos but do not fully utilize the prior information uncovered during pre-training. Furthermore, relying on a single supervised task with a large amount of manually labeled data may be insufficient to capture the foundational structures in point cloud videos. In this paper, we propose a pre-training framework Query-CP to learn the representations of point cloud videos through multiple self-supervised pretext tasks. First, tokenlevel contrast is developed to predict future features under the guidance of historical information. Using a position-guided autoregressor with learnable queries, the predictions are directly contrasted with corresponding targets in the high-level feature space to capture fine-grained semantics. Second, performing only contrastive learning fails to fully explore the complementary structures and dynamics information. To alleviate this, a decoupled spatio-temporal prediction task is designed, where we use a spatial branch to predict low-level features and a temporal branch to predict timestamps of the target sequence explicitly. By combining the above self-supervised tasks, multi-level information is captured during the pre-training stage. Finally, the encoder is fine-tuned and evaluated for action recognition and dynamic semantic segmentation on three datasets. The results demonstrate the effectiveness of our Query-CP. Especially, compared with the state-of-the-art methods, the fine-tuning accuracy on action recognition improves by 3.23% for 24-frame point cloud videos, and the mean accuracy increases by 4.21%.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"821-828"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705970","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10706024
Walter Paschoal;Igor Souza;Lucas Torres;Andre Murilo Pinto;Renan Ozelo
Automotive tires are crucial in vehicle dynamics, generating essential forces between the pavement and the vehicle. Active safety systems like Electronic Stability Control (ESC) rely on accurate tire force models. This paper presents a comparative analysis of the Pacejka Magic Formula (reference model), the brush model, and a proposed gain-saturation model using a single-track (bicycle) model with three degrees of freedom to evaluate lateral dynamics. Simulations conducted with a 14 Degree-of-Freedom (DOF) vehicle in VI-CarRealTime (VI-CRT) and analyzed in MATLAB revealed a significant correlation between simpler models and the benchmark reference for most relevant lateral vehicle dynamic variables, highlighting their capabilities and limitations through transient and stationary maneuvers. Simulation scenarios of the closed-loop ESC control system with the proposed tire models were carried out in real-time automotive software to compare performance with ESC homologation maneuver.
{"title":"Comparative Study of Tire Models Applied to Electronic Stability Control in Automotive Simulator","authors":"Walter Paschoal;Igor Souza;Lucas Torres;Andre Murilo Pinto;Renan Ozelo","doi":"10.1109/TLA.2024.10706024","DOIUrl":"https://doi.org/10.1109/TLA.2024.10706024","url":null,"abstract":"Automotive tires are crucial in vehicle dynamics, generating essential forces between the pavement and the vehicle. Active safety systems like Electronic Stability Control (ESC) rely on accurate tire force models. This paper presents a comparative analysis of the Pacejka Magic Formula (reference model), the brush model, and a proposed gain-saturation model using a single-track (bicycle) model with three degrees of freedom to evaluate lateral dynamics. Simulations conducted with a 14 Degree-of-Freedom (DOF) vehicle in VI-CarRealTime (VI-CRT) and analyzed in MATLAB revealed a significant correlation between simpler models and the benchmark reference for most relevant lateral vehicle dynamic variables, highlighting their capabilities and limitations through transient and stationary maneuvers. Simulation scenarios of the closed-loop ESC control system with the proposed tire models were carried out in real-time automotive software to compare performance with ESC homologation maneuver.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"835-841"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1109/TLA.2024.10669254
Victor Hugo de Souza Ragazzi;Alexandre Gomes Caldeira;Patrícia Nogueira Vaz;Felipe Antunes;Leonardo Bonato Felix
It is common to use sequential testing strategies to help reduce the time of automated detection of an auditory steady-state response (ASSR). However, the application of repeated tests leads to an increase of false positive rate. Monte Carlo-based strategies are used to overcome this obstacle. Despite several paper could be found describing such strategies, no comprehensive comparison was found in the literature. The chosen strategies are based on Monte Carlo simulations to calculate critical values and were faithfully replicated for comparison purposes, and then the test application parameters were varied to suggest an optimization. The detection rate and/or the detection speed improved with each implemented strategy, except for the one related to the year 2013, which increased the false positive rate to 15.3%. The other strategies kept the false positive rate under control. The Pareto curves compared the optimizations of the strategies and revealed that the modified 2015 strategy had the performance achieving 5.6% higher than the original parameters. The automated detection of ASSR improved with each implemented strategy, but not all of them kept a controlled false positive rate (2013 and 2015). The 2015 modified strategy had the highest detection rate in the shortest time.
{"title":"Comparison of sequential test strategies based on Monte Carlo simulations in the detection of auditory steady-state responses","authors":"Victor Hugo de Souza Ragazzi;Alexandre Gomes Caldeira;Patrícia Nogueira Vaz;Felipe Antunes;Leonardo Bonato Felix","doi":"10.1109/TLA.2024.10669254","DOIUrl":"https://doi.org/10.1109/TLA.2024.10669254","url":null,"abstract":"It is common to use sequential testing strategies to help reduce the time of automated detection of an auditory steady-state response (ASSR). However, the application of repeated tests leads to an increase of false positive rate. Monte Carlo-based strategies are used to overcome this obstacle. Despite several paper could be found describing such strategies, no comprehensive comparison was found in the literature. The chosen strategies are based on Monte Carlo simulations to calculate critical values and were faithfully replicated for comparison purposes, and then the test application parameters were varied to suggest an optimization. The detection rate and/or the detection speed improved with each implemented strategy, except for the one related to the year 2013, which increased the false positive rate to 15.3%. The other strategies kept the false positive rate under control. The Pareto curves compared the optimizations of the strategies and revealed that the modified 2015 strategy had the performance achieving 5.6% higher than the original parameters. The automated detection of ASSR improved with each implemented strategy, but not all of them kept a controlled false positive rate (2013 and 2015). The 2015 modified strategy had the highest detection rate in the shortest time.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 9","pages":"733-738"},"PeriodicalIF":1.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the pressing demand for energy conservation, the petrochemical sector faces increasingly stringent energy-saving mandates. Heat exchangers, essential to this sector, suffer efficiency losses and increased energy consumption due to fouling. To ensure optimal operation of heat exchange systems, regular assessment of solid deposits and the implementation of cleaning schedules are imperative. However, the multitude of influencing factors renders traditional estimation methods unreliable. Consequently, we developed a stacking model to predict the fouling factor of heat exchangers. Specifically, we first constructed fouling factor prediction models using various machine learning techniques, then selected the best-performing models random forest, extreme gradient boosting , and light gradient boosting machine for integration. Finally, the predictions from these three models were fed into a linear regression layer to form the final stacking model. The results indicate that the constructed stacking model significantly enhances the accuracy of fouling factor prediction. This model not only surpasses traditional multilayer perceptron neural network methods but also outperforms the well-performing gaussian process regression. This achievement not only validates the effectiveness of our model but also provides robust support for future research and applications in related fields.
{"title":"A prediction model for heat exchanger fouling factor based on stacking model","authors":"Zhiping Chen;Yongle Meng;Haoshan Yu;Ruiqi Wang;Wenwu Zhou","doi":"10.1109/TLA.2024.10670205","DOIUrl":"https://doi.org/10.1109/TLA.2024.10670205","url":null,"abstract":"Given the pressing demand for energy conservation, the petrochemical sector faces increasingly stringent energy-saving mandates. Heat exchangers, essential to this sector, suffer efficiency losses and increased energy consumption due to fouling. To ensure optimal operation of heat exchange systems, regular assessment of solid deposits and the implementation of cleaning schedules are imperative. However, the multitude of influencing factors renders traditional estimation methods unreliable. Consequently, we developed a stacking model to predict the fouling factor of heat exchangers. Specifically, we first constructed fouling factor prediction models using various machine learning techniques, then selected the best-performing models random forest, extreme gradient boosting , and light gradient boosting machine for integration. Finally, the predictions from these three models were fed into a linear regression layer to form the final stacking model. The results indicate that the constructed stacking model significantly enhances the accuracy of fouling factor prediction. This model not only surpasses traditional multilayer perceptron neural network methods but also outperforms the well-performing gaussian process regression. This achievement not only validates the effectiveness of our model but also provides robust support for future research and applications in related fields.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 9","pages":"746-754"},"PeriodicalIF":1.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The primary concern of this article is to stabilize the rotating speed of the permanent magnet DC (PMDC) motor driven by a DC-DC sepic converter under mismatched disturbances via higher order PID sliding surface (PIDSS) controller. This controller offers numerous benefits, including robustness, enhanced control performance, flexibility, simple implementation, and low cost. An algorithm for the above-said control is developed for the load torques such as: no-load, constant, frictional, and propeller types. Further, the features of PIDSS are compared with classical sliding surface, sliding mode control (SMC) and proportional integral controller (PIC) by taking into consideration of peak overshoot, steady-state error and settling time. Simulation and experimental results are obtained satisfactorily.
{"title":"Performance enhancement of permanent magnet DC motor with sepic converter through higher order sliding surface","authors":"Dhanasekar Ravikumar;Ganesh Kumar Srinivasan;Marco Rivera","doi":"10.1109/TLA.2024.10670234","DOIUrl":"https://doi.org/10.1109/TLA.2024.10670234","url":null,"abstract":"The primary concern of this article is to stabilize the rotating speed of the permanent magnet DC (PMDC) motor driven by a DC-DC sepic converter under mismatched disturbances via higher order PID sliding surface (PIDSS) controller. This controller offers numerous benefits, including robustness, enhanced control performance, flexibility, simple implementation, and low cost. An algorithm for the above-said control is developed for the load torques such as: no-load, constant, frictional, and propeller types. Further, the features of PIDSS are compared with classical sliding surface, sliding mode control (SMC) and proportional integral controller (PIC) by taking into consideration of peak overshoot, steady-state error and settling time. Simulation and experimental results are obtained satisfactorily.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 9","pages":"789-797"},"PeriodicalIF":1.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/TLA.2024.10669249
Breno Bezerra Freitas;Bruno Rodrigues Alves Bezerra;Carlos Alberto Teixeira Júnior;Celso Florindo de Oliveira Júnior;Dionízio Porfírio de Assis;Edvaldo de Sousa Queiroz Filho;Felipe Teles do Nascimento;Fernando Weslley Silva de Oliveira;Gabryel Ferreira Alves;João Victor Teixeira Alves;Marcos Felipe de Andrade Silva;Milton Cezar da Silva;Monilson de Sales Costa;Otacilio José de Macêdo Nunes;Paulo Cesar Marques de Carvalho;Rebeca Catunda Pereira
Photovoltaic (PV) generation has emerged as an alternative for reducing environmental impacts. Recently, floating photovoltaic (FPV) configurations have gained popularity, utilizing the water surface of reservoirs as installation sites. Recognizing its potential, this paper proposes a methodology to harness the idle capacity of substation facilities in hydroelectric power plants (HPP) for sizing FPV plants, aiming for the maximal utilization of the substation's capacity and promoting complementarity with HPP generation. The study introduces a sizing proposal for FPV based on complementarity with the worst day of HPP generation within a defined period, aiming to utilize 100% of the substation's capacity. As a case study, the FPV potential is identified as 59.81 GWp for Belo Monte and 55.35 GWp for Itaipu. This approach seeks to enhance the overall efficiency and sustainability of power generation systems by integrating FPV with existing hydroelectric infrastructure.
{"title":"Methodology Using Idle Capacity of Hydroelectric Substations for Sizing Floating Photovoltaic Plants","authors":"Breno Bezerra Freitas;Bruno Rodrigues Alves Bezerra;Carlos Alberto Teixeira Júnior;Celso Florindo de Oliveira Júnior;Dionízio Porfírio de Assis;Edvaldo de Sousa Queiroz Filho;Felipe Teles do Nascimento;Fernando Weslley Silva de Oliveira;Gabryel Ferreira Alves;João Victor Teixeira Alves;Marcos Felipe de Andrade Silva;Milton Cezar da Silva;Monilson de Sales Costa;Otacilio José de Macêdo Nunes;Paulo Cesar Marques de Carvalho;Rebeca Catunda Pereira","doi":"10.1109/TLA.2024.10669249","DOIUrl":"https://doi.org/10.1109/TLA.2024.10669249","url":null,"abstract":"Photovoltaic (PV) generation has emerged as an alternative for reducing environmental impacts. Recently, floating photovoltaic (FPV) configurations have gained popularity, utilizing the water surface of reservoirs as installation sites. Recognizing its potential, this paper proposes a methodology to harness the idle capacity of substation facilities in hydroelectric power plants (HPP) for sizing FPV plants, aiming for the maximal utilization of the substation's capacity and promoting complementarity with HPP generation. The study introduces a sizing proposal for FPV based on complementarity with the worst day of HPP generation within a defined period, aiming to utilize 100% of the substation's capacity. As a case study, the FPV potential is identified as 59.81 GWp for Belo Monte and 55.35 GWp for Itaipu. This approach seeks to enhance the overall efficiency and sustainability of power generation systems by integrating FPV with existing hydroelectric infrastructure.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 9","pages":"771-777"},"PeriodicalIF":1.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/TLA.2024.10669252
Ivo Benitez Cattani;Enrique Chaparro;Benjamin Baran
Distribution systems are increasingly experiencing the penetration of photovoltaic (PV) systems. Although PV penetration is beneficial up to a point, beyond that point, it begins to generate issues related to voltage levels and grid stability. In modern distribution system planning, it is essential to identify an optimal operational point where the integration of PV supports the voltage profile rather than causing any adverse effects. The purpose of this paper is to explore and evaluate strategies to enhance Hosting Capacity and reduce Power Losses in distribution systems through an optimization algorithm that iteratively uses power-flow simulations and a Multi-Objective Genetic Algorithm. Different strategies taking advantage of conventional distribution system assets are formulated to avoid new system reinforcement. The strategies include Network Reconfiguration, Capacitor Switching, On-Load Tap Changer Switching, Volt-VAR Control Settings and the Combination of all strategies. To evaluate the efficiency of each approach, a comprehensive simulation study is conducted on the IEEE 123 bus distribution system modeled in OpenDSS, with an algorithm created in Python to control the optimization process.
{"title":"Assessment and Simulation of Strategies to Enhance Hosting Capacity and Reduce Power Losses in Distribution Networks","authors":"Ivo Benitez Cattani;Enrique Chaparro;Benjamin Baran","doi":"10.1109/TLA.2024.10669252","DOIUrl":"https://doi.org/10.1109/TLA.2024.10669252","url":null,"abstract":"Distribution systems are increasingly experiencing the penetration of photovoltaic (PV) systems. Although PV penetration is beneficial up to a point, beyond that point, it begins to generate issues related to voltage levels and grid stability. In modern distribution system planning, it is essential to identify an optimal operational point where the integration of PV supports the voltage profile rather than causing any adverse effects. The purpose of this paper is to explore and evaluate strategies to enhance Hosting Capacity and reduce Power Losses in distribution systems through an optimization algorithm that iteratively uses power-flow simulations and a Multi-Objective Genetic Algorithm. Different strategies taking advantage of conventional distribution system assets are formulated to avoid new system reinforcement. The strategies include Network Reconfiguration, Capacitor Switching, On-Load Tap Changer Switching, Volt-VAR Control Settings and the Combination of all strategies. To evaluate the efficiency of each approach, a comprehensive simulation study is conducted on the IEEE 123 bus distribution system modeled in OpenDSS, with an algorithm created in Python to control the optimization process.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 9","pages":"778-788"},"PeriodicalIF":1.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/TLA.2024.10669246
Paulo Henrique Ponte de Lucena;Lidio Mauro Lima de Campos;Jonathan Cris Pinheiro Garcia
Obesity is a complex chronic disease resulting from the interaction of multiple behavioral factors. This paper presentsthe application of Machine Learning to identify the primary groups of behaviors contributing to the development of obesity.Supervised machine learning emphasizes decision trees and deep artificial neural networks from datasets. The study also references related work that utilizes predictive methods to estimate obesity levels based on physical activity and dietary habits. Furthermore,it compares the performance of classification algorithms such as J48, Naive Bayes, Multiclass Classification, Multilayer Perceptron, KNN, and decision trees when predicting diabetes cases. The objective is to analyze different tools in the assessment based on physical activity and dietary habits, contributing to the improvement of obesity risk diagnosis. In addition, MLP and J48 demonstrated strong performance among all the algorithms, but BPTT achieved the highest overall performance.
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