IEEE Latin America Transactions最新文献_第7页

Table of Contents May 2025 目录2025年5月

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-04-22 DOI: 10.1109/TLA.2025.10974371

引用次数: 0

Automatic Detection of Dynamic Wind Conditions in Mexican California: A Machine Learning-Driven Advancement in Wind Management 墨西哥加利福尼亚州动态风况的自动检测：机器学习推动风能管理进步

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-04-22 DOI: 10.1109/TLA.2025.10974369

Magali Arellano Vázquez;Marlene Zamora Machado;Miguel Robles Pérez;Oscar Jaramillo Salgado;Carlos Minutti Martinez

{"title":"Automatic Detection of Dynamic Wind Conditions in Mexican California: A Machine Learning-Driven Advancement in Wind Management","authors":"Magali Arellano Vázquez;Marlene Zamora Machado;Miguel Robles Pérez;Oscar Jaramillo Salgado;Carlos Minutti Martinez","doi":"10.1109/TLA.2025.10974369","DOIUrl":"https://doi.org/10.1109/TLA.2025.10974369","url":null,"abstract":"Accurate detection and classification of wind states is crucial for accurately assessing and predicting wind energy production, fire spread behavior, air quality monitoring, and understanding complex meteorological phenomena. The complex nature of wind necessitates advanced data analysis techniques to extract meaningful patterns from meteorological datasets. This study presents a stochastic wind classification and identification analysis based on a Gaussian Mixture Model (GMM) clustering method applied to a case study in La Rumorosa'', Mexican California. By analyzing four meteorological variables (relative humidity, atmospheric pressure, wind speed, and wind direction) over five years, the method automatically identifies distinct wind conditions that can be defined as climate states, including well-known regional phenomena like Santa Ana winds and local orographic winds. Accurate detection of wind states enables better forecasting of wind energy potential at favorable sites, wildfire risk management through predicted fire behavior and monitoring pollutant/allergen dispersal patterns. The proposed approach offers a reliable, computationally efficient method for detecting wind patterns, extending to different geographical regions impacted by diverse wind phenomena.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 5","pages":"387-396"},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860838","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}

引用次数: 0

Fault Detection System for Bearings in Electric Motors using Variational Auto Encoders 基于变分自动编码器的电机轴承故障检测系统

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-04-22 DOI: 10.1109/TLA.2025.10974368

Alonso Menéndez-González;Luis Magadán;Juan Carlos Granda Candás;Francisco José Suárez Alonso

引用次数: 0

A 2.4 GHz Wireless Temperature Sensor with 0.93 C precision designed in 130 nm CMOS technology for Internet of Things applications 采用130纳米CMOS技术设计的2.4 GHz无线温度传感器，精度为0.93℃，适用于物联网应用

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-04-22 DOI: 10.1109/TLA.2025.10974372

Hugo Dias Gilo;Francisco de Assis Brito Filho

引用次数: 0

Real-time inertia estimation via ARMAX model representation and synchrophasor measurements 实时惯性估计通过ARMAX模型表示和同步量测量

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-04-22 DOI: 10.1109/TLA.2025.10974367

Alexander Sanchez-Ocampo;Mario R. Arrieta Paternina;Jose M. Ramos-Guerrero;Gabriel E. Mejia-Ruiz;Juan M. Ramirez-Arredondo;Lucas Lugnani;Felix Munguia-Perez;Alejandro Zamora-Mendez;Juan R. Rodriguez-Rodriguez

引用次数: 0

FishEye Matlock: A Random Functional Encoding Mechanism for Secure Location Sharing 鱼眼锁：一种安全位置共享的随机功能编码机制

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-03-19 DOI: 10.1109/TLA.2025.11007191

Pedro Wightman;Nicolás Avilán;Augusto Salazar

{"title":"FishEye Matlock: A Random Functional Encoding Mechanism for Secure Location Sharing","authors":"Pedro Wightman;Nicolás Avilán;Augusto Salazar","doi":"10.1109/TLA.2025.11007191","DOIUrl":"https://doi.org/10.1109/TLA.2025.11007191","url":null,"abstract":"Location tracking is difficult to protect due to the sequential nature of the data and the need for accuracy to offer a proper service and monetize the location information. Homomorphic encryption can partially solve the problem, but it typically has a minimal set of operations that are not feasible for geographical operations. Randomized Functional Encoding (RFE) allows the creation of random keys to decrypt data, according to the user's needs. Now, creating keys that only focus on a portion of the path while protecting the rest of the path has not been proposed in the literature, to the knowledge of the authors. This work proposes a RFE mechanism for Matlock-coded location data, called FishEye Matlock. This technique generates disposable random key matrices that only reveal a desired portion of the path, with the possibility for the user to add random noise to protect the revealed data and to control the amount of noise added to the rest of the path. This allows secure information sharing with particular actors, like law enforcement, so that the information of interest is shared without affecting the user's privacy. The algorithm is tested in two different path scenarios to show the technique's applicability, the level of protection, and the impact of the parameter value selection. Results show that the mechanism can be tailored to generate key matrices for different scenarios: at the lowest value of k, the level of noise reaches several thousands of kilometers of noise along the path, and between 60 and 100 times the level of noise, and with, the highest k value, between 40% to 80% of the maximum distance radius on average at the point of interest, and between 1.1 and 10 times the defined noise level at the path.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 6","pages":"452-461"},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090774","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}

引用次数: 0

Table of Contents June 2025 目录2025年6月

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-03-19 DOI: 10.1109/TLA.2025.11007190

引用次数: 0

A Review of Voltage Distribution on Metal Oxide Surge Arrester and Suggestions for Improvement in High Voltage Applications 金属氧化物避雷器在高压应用中的电压分布及改进建议

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-03-19 DOI: 10.1109/TLA.2025.11007196

Valsalal Prasad

{"title":"A Review of Voltage Distribution on Metal Oxide Surge Arrester and Suggestions for Improvement in High Voltage Applications","authors":"Valsalal Prasad","doi":"10.1109/TLA.2025.11007196","DOIUrl":"https://doi.org/10.1109/TLA.2025.11007196","url":null,"abstract":"Stray capacitive effect of MOSA may cause non-uniform voltage distribution in both non-conduction and conduction modes. Also, during conduction, there is a delay in operation of arrester, particularly when faced with very fast transient overvoltages (VFTOs). Consequently, there is a necessity to reduce stray capacitive effect for improvement in voltage distribution and reduce delay in response of arrester for VFTOs. One approach is placement of grading rings with appropriate number and size. Numerous researchers have conducted extensive studies on causes of non-uniform voltage distribution, computation of stray capacitances and remedial measures to achieve a more uniform voltage distribution. Further, this investigation holds great significance in polluted environments, damaged arresters and broken sheds, as proper operation of surge arresters relies on environmental conditions, material properties, and shapes of arrester assembly. This paper provides a summary of research conducted by various researchers from the year 1970 and also offers suggestions for further studies of achieving a more uniform voltage distribution and reducing response delay of MOSAs.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 6","pages":"479-486"},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090769","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}

引用次数: 0

Fault Section Identification in Distribution Networks with DFIG and PMSG Generators Using Current Transients 基于暂态电流的DFIG和PMSG配电网故障区段识别

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-03-19 DOI: 10.1109/TLA.2025.11007188

Juan Carlos Peqqueña Suni;Marina Gabriela Sadith Pérez Paredes;Marcelo Vinicius de Paula;Ernesto Ruppert Filho;Juan Antonio Martinez Velasco

{"title":"Fault Section Identification in Distribution Networks with DFIG and PMSG Generators Using Current Transients","authors":"Juan Carlos Peqqueña Suni;Marina Gabriela Sadith Pérez Paredes;Marcelo Vinicius de Paula;Ernesto Ruppert Filho;Juan Antonio Martinez Velasco","doi":"10.1109/TLA.2025.11007188","DOIUrl":"https://doi.org/10.1109/TLA.2025.11007188","url":null,"abstract":"This paper presents a methodology for fault section identification (FSI) in distribution networks with embedded wind power generation. The phase currents are measured only at the distribution substation (DS), using a waveform window of two cycles (one before and one after the fault detection). The proposed approach is divided into two stages: the first stage, Fault Identification (FI), aims to identify whether a short-circuit fault lies on a main feeder or one of the branches effectively addressing the challenge of multiple fault locations that may arise when several branches correspond to the estimated fault point; the second stage, Fault Location (FL), estimates the distance between the DS and the fault location. The algorithm employs discrete wavelet transform (DWT) in combination with artificial neural networks (ANNs). Energy and Relative Energy Entropy, both in per unit (EPU and REEPU), are proposed and calculated from DWT decomposition, with regularization indexes applied to EPU and REEPU. These indexes serve as input to multi-layer ANN models, which work as classifiers for FI and predictors for FL. Various fault scenarios with different fault inception angle, fault type, fault resistance and fault location are simulated using MATLAB software and the IEEE 34-node benchmark feeder as test system. The results demonstrate that the proposed methodology performs effectively the FSI task, achieving an accuracy of up to 95% for FI and a maximum error of 5.2% for FL.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 6","pages":"487-496"},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090770","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}

引用次数: 0

Short Term Residential Load Forecasting Using Temporal Weather Based Embedding Stacked LSTMs 基于时间天气的嵌入堆叠lstm短期住宅负荷预测

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-03-19 DOI: 10.1109/TLA.2025.11007195

Srinivasa Raghavan Vangipuram;Giridhar A V

{"title":"Short Term Residential Load Forecasting Using Temporal Weather Based Embedding Stacked LSTMs","authors":"Srinivasa Raghavan Vangipuram;Giridhar A V","doi":"10.1109/TLA.2025.11007195","DOIUrl":"https://doi.org/10.1109/TLA.2025.11007195","url":null,"abstract":"Resource management is crucial to balance human needs with sustainability, prevent overuse, and preserve natural resources like water, forests, and minerals for future generations. Managing electricity at the root of human usage can be a crucial first step, helping us move toward better resource management and reducing the strain on natural resources. Superior forecasting approaches are needed to determine usage patterns. Accurate predictions can serve as key input to the Home Energy Management Systems (HEMS) mechanisms in optimizing electricity operation, reducing energy waste, and increasing resource utilization. Neural network-based methods are being developed to forecast electricity usage in residential buildings by learning behavioral patterns over time. These approaches leverage historical data to identify trends and predict future consumption, offering a promising direction for more accurate forecasting methods. Although still evolving, they provide a foundation for optimizing energy management by anticipating demand and enabling more efficient resource allocation. However, these approaches primarily rely on historical patterns to predict future electricity usage, often overlooking the impact of daily weather conditions. In this paper, we explore a method that incorporates weather information to enhance electricity usage predictions. We propose a simple Stacked LSTM-based neural network that integrates historical usage data and weather information as learned inputs for more effective electricity usage prediction. Our approach demonstrates improved prediction performance compared to methods that do not account for weather factors and the CNN-SLSTM model. For the BR04 hourly test dataset, our proposed model achieves a 56% and 67% reduction in RMSE compared to the SLSTM with weather and CNN-SLSTM models, respectively.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 6","pages":"497-507"},"PeriodicalIF":1.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090753","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}

引用次数: 0