This paper presents an innovative method for fault location in distribution networks based on classical circuit analyses. Two synchronized and few nonsynchronized pre- and during-fault voltages are required at few buses along with the impedance matrix. A new impedance matrix manipulation procedure is proposed so that the distribution system is investigated in partitions across multiple subsystems. This procedure allows the fault location process to be performed by solving systems of determined equations, making the method more technically accessible. The fault is located by analyzing the voltage sag at the terminal-bus of each subsystem separately. The proposed method is validated on the IEEE 33-bus, 12.66 kV distribution system with/without distributed generation (DG). Simulation results show the robustness and accuracy of the method under several pre- and during-fault scenarios and measurements errors.

In recent years, the integration of electric vehicles (EV) into urban fleets has seen a significant rise, leading to a considerable increase in the number of EV chargers and fast charging stations (FCS) connected to distribution networks. Depending on the characteristics of the electrical power system, such as short-circuit power and voltage harmonic distortion, due to the dynamic operation during charging sessions, EV charging stations may impacts the quality of power in the connection point. As a result, it is crucial for utility companies and facility managers to perform preliminary assessments to identify potential exceedances of quality limits.In this context, this work describes the development of an electrical grid impact indicator that evaluates the parameters that influence the electrical network during charging of electric vehicles. A case study and a simulation model were used to identify and incorporate into the indice the main relevant factors, such as power demand, short-circuit power, harmonic distortion, and power factor. The simulation models were employed to evaluate critical operational points, and measurement data further validated the model’s performance. The results highlighted the importance of considering these parameters to ensure effective and safe recharging of electric vehicles. The proposed electrical impact indicator offers an electrical network management tool, allowing a predictive assessment of the impact of EV charging and enabling the adoption of appropriate measures to ensure the quality of power of the distribution networks accessed by charging stations.

This paper proposes an innovative method for dimensioning and designing the zero-sequence electromagnetic filter applied to three-phase four-wire distribution power systems. As premises, the optimum ratio between zero-sequence and positive-sequence impedances of the filter must be established in order to potentiate the filtering percentage of zero-sequence currents and ensure the quality of the supply system displacement power factor. The calculations are based on the sequential harmonic transmittances of the equivalent circuit reduced to the coupling point of the electromagnetic filter. To validate the proposed method, the results obtained with the application of the electromagnetic filter designed in a test system using the MATLAB software are compared to the results of the system modeled in the PSIM software.

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs).