IEEE Latin America Transactions

Compliance Analysis of Series Arc-fault in AFCI- Equipped Inverters in Accordance with IEC 63027

2024-07-17T13:53:43-07:00

The National Institute of Metrology, Quality and Technology (Instituto Nacional de Metrologia, Qualidade e Tecnologia - INMETRO) introduces that, starting in 2024, all photovoltaic (PV) inverters sold in the Brazilian market must incorporate an Arc-Fault Circuit Interrupt (AFCI) function into their systems. These inverters are required to comply with the IEC 63027:2023 (Photovoltaic power systems – DC arc detection and interruption) standard. Considering this, the Electrical Engineering Laboratory at Mackenzie Presbyterian University (Universidade Presbiteriana Mackenzie – UPM) conducted a series of arc-faults tests on three inverters available in the market, following the IEC 63027 standard. Each of the three inverters underwent a total of 32 arcs, considering number of Maximum Power Point Tracking (MPPT) ports, different impedance topologies, arc position in the PV system, and maximum values of voltage and current. The experiments revealed that two of the three inverters are not capable of meeting the international standard for detecting and interrupting series arc-faults, highlighting the need evaluation of PV inverter sold in the Brazilian market. During the analysis, it was noted that for certain parameters proposed by IEC 63027, there is a gap of information regarding evaluation of the data relating to arc self-extinguish or actual AFCI intervention. It is show that this scenario can raise a concern: the possibility exists for an inverter meet the international standard without implementing an effective AFCI technology. The 96 tests conducted were compared in terms of arc detection time and arc energy. The data were analyzed and compared with respect to the phenomena of arc self-extinguishing and the operation of the AFCI. Suggestions for enhancements to the IEC 63027 standard were provided

Methodology Using Idle Capacity of Hydroelectric Substations for Sizing Floating Photovoltaic Plants

2024-07-19T06:06:08-07:00

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.

Assessment and Simulation of Strategies to Enhance Hosting Capacity and Reduce Power Losses in Distribution Networks

2024-07-17T14:04:01-07:00

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.

Performance enhancement of permanent magnet DC motor with sepic converter through higher order sliding surface

2024-08-05T09:10:57-07:00

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.

Towards a Machine-Learning-Based Application for Amorphous Drug Recognition

2024-08-20T16:22:54-07:00

The amorphous drug structure represents an important feature to be reached in the pharmaceutical field due to its possibility of increasing drug solubility, considering that at least 40% of commercially available crystalline drugs are poorly soluble in water. However, it is known that the amorphous local structure can vary depending on the amorphization technique used. Therefore, recognizing such variations related to a specific amorphization technique through the pair distribution function (PDF) method, for example, is an important tool for drug characterization concerns. This work presents a method to classify amorphous drugs according to their amorphization techniques and related to the local structure variations using machine learning. We used experimental PDF patterns obtained from low-energy X-rays scattering data to extract information and expanded the data through the Monte Carlo method to create a synthetic dataset. Then, we proposed the evaluation of such a technique using a Deep Neural Network. Based on the results obtained, it is suggested that the proposed technique is suitable for the amorphization technique and local structure recognition task.

Predictive Performance of Machine Learning Algorithms Regarding Obesity Levels Based on Physical Activity and Nutritional Habits: A Comprehensive Analysis

2024-07-24T05:15:53-07:00

Obesity is a complex chronic disease resulting from the interaction of multiple behavioral factors. This paper presents
the 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.

Comparison of sequential test strategies based on Monte Carlo simulations in the detection of auditory steady-state responses

2024-08-08T07:37:40-07:00

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.

Impact of the preprocessing stage on the performance of offline automatic vehicle counting using YOLO

2024-08-09T08:33:35-07:00

Vehicle counting systems detect, classify, and count vehicles with sensors or image processing, providing valuable information for road management. Image processing systems provide detailed information on vehicle flow with adequate lighting conditions and a higher computational cost compared to sensor systems. The image processing systems with higher accuracy require higher computational cost. This feature limits the number of application cases in cities with low technology level. This research analyzes urban vehicle counting using an automatic image processing system using YOLOv5 in the vehicle detection-classification stage and the SORT algorithm in the tracking stage. The study used videos recorded from a pedestrian bridge in Popayan, Colombia, for an exploratory study of the influence of preprocessing operations on the performance of a low-tech vehicle counting system. The study performed a comparative statistical analysis to determine the impact of different settings on system performance. An ANOVA analysis evaluates the incidence of frame cut and reshape on YOLO processing. The results indicate that a 30% cut of the image area prior to YOLO processing produces the lowest weighted average error. In addition, the frame reshape only increases the processing time. The study proposes improvements in the performance of an offline automatic vehicle counting system from the video preprocessing stage.

Disease-IncRNA associations prediction based on fast random walk with restart in heterogeneous networks

2024-08-16T04:18:14-07:00

Long non-coding RNAs (lncRNAs) represent a fundamental category of epigenetic modulators. Recent research has revealed that lncRNAs play critical roles in gene regulatory mechanisms, substantially influencing the pathogenesis of various human diseases. In this study, a multilayer heterogeneous network was created and we introduced the fast random walk with restart (FRWR) for predicting connections between lncRNAs and diseases. By combining the similarity network of lncRNA, similarity network of disease, and association network of existing lncRNA-disease, a multilayer heterogeneous network was constructed, and the fast random walk with restart method (FRWR) was applied on this network to predict additional potential lncRNA-disease associations. The AUROC value of 0.9034, achieved through leave-one-out cross-validation, underscored the predictive precision of the FRWR technique. Furthermore, a case study of three different diseases provided further validation of the reliability of prediction results. Overall, the multilayer network FRWR method proposed in this work could effectively forecasting the connections between lncRNAs and diseases, offering valuable insights into comprehending the functions of lncRNAs in the context of human health and disease. The source code for the FRWR method can be accessed at: https://github.com/TianTianTian14/FRWR.

A prediction model for heat exchanger fouling factor based on stacking model

2024-07-29T05:38:26-07:00

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.

Table of Contents September 2024

2024-08-19T08:34:01-07:00

Table of Contents September 2024