Research Paper 2

Critical Assessment of Machine Learning-Based Approaches for predicting system inertia in power systems

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Critical Assessment of Machine Learning-Based Approaches for predicting system inertia in power systems

Project information

Category:Artificial intelligence , Power Sector
Keywords:system inertia, machine learning, power grid stability, renewable energy integration, inertia prediction
Research date: 30 July, 2023
Research URL: https://www.researchgate.net/publication/ 372717882_Critical_Assessment_of_Machine_Learning-Based_Approaches_ for_Predicting_System_Inertia_in_Power_Systems

Abstract

The increasing integration of renewable energy sources in power systems has led to declining system inertia, making power grid stability a significant challenge. Machine learning (ML) techniques have emerged as a promising approach to predicting system inertia and enhancing grid stability. This paper critically assesses various ML-based methods for predicting system inertia in power systems. We will discuss the current state of research, the challenges and limitations of existing ML approaches, and potential future directions for improving prediction accuracy and real-world implementation. This paper aims to provide researchers and practitioners with a comprehensive understanding of ML-based system inertia prediction techniques and their applicability in modern power systems.