This research article presents a proposal and analysis of a multi-port converter designed to enhance electrical resilience for residential users located at the edge of the public service grid. The converter, which is capable of generating alternating current, is designed to decouple undesired effects at the common connection point with the electrical grid, particularly those commonly encountered in weak nodes. This objective is achieved through a decoupling stage that absorbs undesired dynamics by regulating a direct current link. Furthermore, by incorporating a self-generation and storage port, the system adds electrical energy management capability that allows the provision of auxiliary functions to the electrical grid and the maintenance of electrical supply during extended periods of grid unavailability. The validity of this proposal is supported by a reliability analysis based on Markov chains, and is experimentally validated through a prototype. The prototype is subjected to significant fluctuations in load to incorporate the worst-case scenario, as depicted by the CBEMA performance characteristic curve.

The growing adoption of renewable energy conversion systems and smart infrastructures has increased the demand for accurate monitoring solutions to ensure system performance and reliability, as well as seamless integration with cloud-based platforms. Voltage and current sensing are central to this task; however, sensor selection often involves a trade-off between cost and measurement precision. Rather than comparing technologies as equivalent options, this study investigates the practical impact of using low-cost versus high-precision sensors in electrical power generation monitoring. The evaluation includes representative low-cost sensors and high-precision alternatives based on instrumentation amplifiers and a closed-loop Hall-effect transducer. All sensors were characterized under controlled laboratory conditions and analyzed using statistical indicators, including MAE, RMSE, MAPE, and R². Results show that high-precision sensors achieved R² > 0.97 and MAPE < 4%, whereas low-cost sensors showed R² as low as 0.73 and errors exceeding 10% under dynamic irradiance conditions. Low-cost sensors present deviations of 5–8% in RMS measurement, while high-precision sensors maintain error below 1%.