In order to ensure the continuous and reliable supply of electrical energy to the power grid, it is necessary to evaluate and monitor the degree of impairment of the insulation of electrical cables, as throughout its service life, insulation around cables suffers degradation due to numerous stress factors, which can arise from both environmental and operational causes. This aspect has aroused deep interest among energy professionals, as well as the industrial sector, with focus mainly placed on the undesirable effect caused by unexpected and sudden process stoppages, as well as their consequent financial and social impacts. That said, this article presents a methodology for evaluating the degree of insulation aging using the partial discharge progression curve. For this purpose, a thermal oven was duly constructed, in accordance with the technical premises presented in the literature, capable of homogeneously heating conductor samples. After thermal cycles, these conductors were aptly handled and tested in a controlled laboratory environment to determine the partial discharge progression curve. Through accurate data processing, a correlation was obtained between the degradation of the insulation and the rate of increase in partial discharge. The results are promising, as they provide support for maintenance agents’ ability to monitor and intervene regarding conductors.

Accurate forecasting of active (P) and reactive (Q) power in electrical networks is fundamental to improving operational reliability and planning in modern power systems. This article proposes a forecasting model based on Long Short-Term Memory (LSTM) neural networks to estimate load demand in an IEEE 30-bus system. The model considers the active and reactive power supplied by the generators as input variables, while the power components of the loads are used as target variables for prediction. To improve performance, a Genetic Algorithm (GA) was used to optimize hyperparameters, which reduced the Mean Absolute Error (MAE) and increased the accuracy of the predictions. The results demonstrate that the proposed approach provides stable predictions of load behavior, highlighting its potential for application in smart grids and microgrid management systems.