Application of data models in power grids for loss reduction and disaster anticipation

Abstract

This paper addresses the critical objective of optimizing power flow within a region, particularly focusing on the Mangystau region, amidst evolving energy demands and the integration of renewable resources. The escalating challenges associated with maintaining both system stability and economic viability underscore the significance of this research, as suboptimal power flow conditions can exacerbate climate change. To expedite the solution to the optimal power flow problem, machine learning algorithms are explored. Initially, load data from the region is analyzed, and various supervised learning algorithms are tested using simulation data to predict power flow patterns. The primary concern in the Mangystau region lies in the aging infrastructure of oil companies, which operates under suboptimal conditions. This study employs neural networks in Matlab to simulate the electrical system’s parameters, unveiling the intricate relationship between optimal system parameters and those of the examined system. Comparing these results with analytical grid modeling, the study reveals that system optimization aligns with target values, particularly concerning optimal receiver replacement schemes.