Abstract
This study discusses the problems occurring in a microgrid established with XLPE conductors. The use of XLPE in electric grids has increased, causing problems such as voltage rise that leads to breakdowns in high-voltage equipment, increased capacitive power, and harmonic distortion. The Ferranti Effect also occurs, especially in these grids with no-load or low-load situations. Power demand in such microgrids varies depending on working days, holidays, and seasons. It is important to understand such grids' behavior and dynamic power demands for the continuity and quality of energy. Firstly, the grid was modeled in the Matlab/Simulink environment, and end-of-line and beginning-of-line voltages were simulated for three different load cases and compared with the measured grid parameters. Then, artificial intelligence-supported power forecasting and analysis were carried out using three years of power consumption data to analyze the grid's behavior and determine the energy demand in any period. Results show the existence of voltage rises at the end of the line in low load cases. Artificial intelligence algorithms have successfully predicted dynamic power demands. The study results are pioneering steps for autonomous grid management (e.g., anomaly detection) and sustainable smart grid designs that can decide which devices will be fed from the grid.
Published Version
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