Adaptive Substation Infrastructure for Bangladesh: Harnessing Artificial Intelligence and Machine Learning for Enhanced Performance and Grid Resilience

Abstract

This groundbreaking research endeavors to revolutionize substation engineering in Bangladesh by introducing an innovative paradigm that integrates artificial intelligence (AI) and machine learning (ML) methodologies. In response to the dynamic and challenging operational environment, this study focuses on the development of an adaptive substation infrastructure capable of intelligently responding to fluctuating energy demands, environmental stresses, and emerging grid complexities. Through the application of advanced AI algorithms, the research addresses real-time fault detection, predictive maintenance, and comprehensive condition monitoring within the substation framework. Harnessing the capabilities of ML models, the proposed infrastructure aims to optimize energy flow, enhance grid resilience, and mitigate potential failures by autonomously adapting to evolving operational scenarios. By combining cutting-edge technology with the unique challenges of the Bangladeshi power landscape, this research not only aims to advance the field of substation engineering but also holds the promise of significantly contributing to the sustainable development of the nation's power infrastructure. The findings are anticipated to guide the design and implementation of intelligent substation systems, ushering in a new era of efficiency, reliability, and adaptability in the Bangladesh power grid.