IEEE Latin America Transactions

Power Quality Analysis of a Hybrid Microgrid based on Renewable Energy Sources

Christian René Jiménez Román — 2024-06-16

Today, and mainly in the academic environment, one of the fields of study that is receiving the most attention, in reference to electrical microgrids (MGs), is precisely how to integrate all the elements of the MGs to maintain a stable operation, resilient, reliable and quality. This can be achieved through power converters, which allow for the decentralization of electricity generation, but on the other hand, create the challenge of power quality. In this paper, a model of an MG based on the IEEE 14–Bus distribution network is proposed for power quality studies when the MG operates in grid–connected mode. MG model is developed in the software MATLAB–Simulink® and the results obtained are compared with the compatibility levels of the IEEE–519 standard.

Efficient and Fast Wind Turbine MPPT Algorithm Using TS Fuzzy Logic and Optimal Relation Methods

David R. López-Flores — 2024-06-16

This paper proposes an efficient and fast maximum power point tracking (MPPT) algorithm for a wind turbine (WT) connected to a battery bank via a permanent magnet synchronous generator, a three-phase diode rectifier, and a dc-dc boost converter. The algorithm is based on the Takagi-Sugeno (TS) fuzzy system and optimal relation methods and is called TS-MPPT. The fuzzy system computes the converter duty cycle using an input that combines the error and its rate of change. The error is the difference between the reference current computed from the optimal relation and the rectifier current. The methods used in the algorithm resulted in a five-rule TS fuzzy system, which contributed to a fast algorithm in terms of its total execution time (TET): 89.12 µs on the F28069M board. The TET attained enabled a synchronized operation of the algorithm with the converter switching frequency. Additionally, the results based on the processor-in-the-loop simulation approach show that the TS-MPPT algorithm achieves an effective MPP tracking process with an energy conversion efficiency of 99.43% and behaves properly when evaluated over the typical WT power curve. Furthermore, the effectiveness and performance of the proposed algorithm are demonstrated against others using the proportional-integral controller, the Mamdani fuzzy method, and a TS fuzzy model from the literature.

Reclosers Modeling for Temporal Simulation of Distribution Networks in Simulink/ Matlab®

Ramayana Pereira — 2024-06-16

Over the past few years, Brazil’s increasing dependence on electricity has caused a continuous growth in demand and, therefore, the need to guarantee long-term energy supply to custumers. Hence, studying the devices that are responsible ensuring this continuity is critical since the improper operation of this equipment can reduce the reliability of the electrical energy distribution system, therefore requiring detailed study that incorporates simulations, modeling, and analysis of response capacity in the face of real loads. Simulink/Matlab is one of the most widely used software programs in academia. However, it does not have readymade templates for protection system equipment such as relays, fuses, and reclosers. Herein, the aim is to model the digital recloser using the S-function block of Simulink/Matlab®. The proposed model for the recloser follows the configurations required by Brazilian electricity distribution companies, i.e. four operations divided into fast and slow, the latter one being responsible for permanently opening the section of the system on fault. To validate the modeled device, the IEEE 34-bar system was used, in which several operational cases were considered. The results obtained show that the proposed digital recloser model performed successfully proved to be a promising proposal for protection studies of power distribution systems.

Power System Stability Improvement Using STATCOM: New Practical Method for Placement and Sizing

Jimmy Cesar Gonzales Arispe — 2024-06-16

In Latin America, in recent years, several projects with FACTS equipment have been materialized to improve the stability of the power system. Among these, for example, use of SVC equipment in Peru to regulate voltage, and STATCOM in Colombia to support voltage and prevent collapse of electrical areas. Several variables influence the way a STATCOM operates, its size is an important factor, and also the electrical substation where it is installed; these two aspects, although they can be calculated separately, in the real time operation it is evident that their contributions can be optimized, considering them jointly. This article aims to show a new practical methodology to evaluate the location and size of a STATCOM through a sequence of calculations and the application of routine electrical studies executed by system operators, in order to improve voltage and/or angular stability of the units. As a case of practical application, the stability problems of the South Bolivian system were analyzed, as well as the installation of a STATCOM to improve the operational limitations.

A Connectivity Strategy for the Evaluation of Smart Grid Models Based on the Ethernet Technology

ANA ISABEL NARVAEZ VILLOTA — 2024-06-16

A simulation strategy that enables data transmission between the modeled components of a Smart Grid is proposed in this paper. The proposed simulation strategy, referred to as the connectivity strategy, enables the integration of a physical communication network into Smart Grids simulations. The connectivity strategy comprises three steps: selection of Smart Grids functionality, data transmission over a TCP/IP network, and connectivity strategy evaluation. Each step is described to ensure transparency and reproducibility in Smart Grid simulations, addressing the limitations associated with the lack of specifications when a communication network is implemented into power systems simulations. Furthermore, a Hardware-in-the-loop (HIL) approach is presented for developing and evaluating the proposed connectivity strategy using the HIL technique. Through this approach, the strategy is validated by establishing the communication between simulation and embedded systems via a physical Ethernet network. In a case study, the use of the connectivity strategy to simulate a distribution system automation (DA) functionality is demonstrated. This simulation allows the evaluation of protection schemes in a Smart Grid using MATLAB/Simulink and a Texas Instruments development kit. Results show that the proposed connectivity strategy could estimate the communication delays for different simulation scenarios.

Simplified SEP Approximations of Coherent Digital Modulation Schemes over \alpha-\kappa-\mu Fading Channel

Shreya Tated — 2024-06-16

In this paper, we propose novel, simplified yet tight approximations of the error probability expressions of numerous digital modulation schemes over a popular \alpha-\kappa-\mu fading channel. With the help of a suitable approximation of the Gaussian Q-function and utilization of Taylor's series expansion, we facilitate the cumbersome integrals which play a key role in simplification of the performance evaluation metrics like symbol error probability (SEP) of various digital modulation schemes. This facilitates cost effective receiver's design making the overall system economically viable. We further illustrate the accuracy of the proposed SEP expressions with the help of the relative error. An insight on the truncation error (and its upper bound) is also highlighted in this paper. We also compute the relative error in the upper bound of the truncation error to further justify the accuracy of the proposed integrals. Moreover, the asymptotic expressions for the integrals are also provided which gives an idea regarding diversity order of the wireless communication systems for large signal to noise ratios.

Incremental Relaying with Partial Relay Selection for Enhancing the Performance of Underlay Cognitive NOMA Networks

Archana P — 2024-06-16

In this paper, we consider an underlay cognitive non-orthogonal multiple access (CNOMA) network, where incremental relaying with partial relay selection (PRS) scheme (i.e., IRP scheme) is proposed for the secondary network. We derive analytical expressions for the outage probabilities of the secondary users (SUs) and the system throughput of the IRP-CNOMA network. The proposed approach is compared to conventional cooperative relaying-based CNOMA (CR-CNOMA) networks with PRS scheme (i.e., CRP-CNOMA) and random relay selection (RRS) scheme. The results show that the proposed scheme significantly lowers the outage probability of the SUs while improving the system throughput.

Exploring COVID-19 Trends in Mexico during the Winter Season with Explainable Artificial Intelligence (XAI)

Angélica Guzmán-Ponce — 2024-06-16

COVID-19 has become the most significant pandemic in recent years. Today, Mexico has recorded millions of infections and deaths since the pandemic started. Around the world, machine learning methods have been used to understand, predict or develop strategies to manage the virus and the pandemic. Although algorithms provide good results, it is necessary to understand why a model makes specific predictions with a particular data set. To explain this question, we apply Explainable Artificial Intelligence (XAI) in this paper. With this, it is possible to understand the characteristics that influence the model decisions when denoting between deaths and survivors. As a case of study, the positive cases detected during the winter season of 2020-2021 and 2021-2022 were considered. In this season, respiratory diseases increased considerably, and in the study period, they influenced the increase in positive cases and the spread of COVID-19. Preliminary results suggest that age is essential when using a Random Forest model. Preliminary results suggest that age is essential when determining the prognosis of a patient infected by COVID-19 in winter seasons.

Trajectory control based on On/Off, Fuzzy Logic and Convolutional Neural Networks for an Industrial Robot Arm: an experimental comparison

José Raúl Castro — 2024-06-16

The objective of the present study is to compare three control approaches: ON/OFF control, fuzzy logic, and convolutional neural networks (CNN) implemented in Python for controlling the real-time trajectory tracking of a six-axis industrial robotic arm. This analysis has significant applications in fields that require a high level of precision, such as automated welding and surgical interventions in the medical domain. To evaluate the performance and adaptability of the control models, we will analyze the results using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), as well as metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Jaccard Index, and Pearson's correlation coefficient. The results obtained reveal valuable information about the advantages and limitations of each control approach, highlighting the effectiveness of CNNs in visual perception and trajectory tracking. The ability of CNNs to interpret visual complexities is presented as a key factor for their success in industrial robotics and automation applications, suggesting a promising future for these technologies in dynamic environments.

Dual-View Fusion of Heterogeneous Information Network Embedding for Recommendation

Jinlong Ma — 2024-06-16

Heterogeneous Information Networks (HINs) contain rich semantic information due to their involvement of multiple types of nodes and edges. Heterogeneous network embedding is used to analyze HINs by embedding network information in low-dimensional node representations. However, existing heterogeneous embedding methods either ignore the implicit topological relationships between distant nodes or neglect nodes features and meta-paths information disparities, which reflects that extracting HIN embeddings from a single view may lead to incomplete information extraction. In order to make the information extraction more complete, we propose a dual-view fusion heterogeneous information network embedding method (DFHE) for recommendation tasks. Specifically, it extracts effective features from HINs from both the remote topology view and the semantic aggregation view: the remote topology view uses a meta-graph-guided random walk to capture the topological relationships between remote nodes and learns embeddings through a graph convolutional network (GCN) encoder, while the semantic aggregation view uses an attention mechanism to learn the importance of different meta-paths, node relationships, and aggregates the semantic information of each meta-path. Experimental results on two real-world network datasets demonstrate an enhancement in recommendation task performance under the application of DFHE, compared to the baseline. This improvement persists even when some meta-paths are deleted, thereby verifying the method’s effectiveness.

Table of Contents July 2024

Daniel Ulises Campos Delgado — 2024-06-16

Table of Contents July 2024