A real-time machine learning-based methodology for short-term frequency nadir prediction in low-inertia power systems

Abstract

In the modern era, electricity is vital for societal advancement, driving economic growth and essential functions. However, the landscape of power systems is swiftly changing due to the integration of renewable energy sources and the decline of traditional synchronous generation, which reduces the total rotational inertia of the systems. This reduction in inertia leads to more frequent and severe frequency deviations, directly impacting power system behavior. Therefore, there is a pressing need to anticipate frequency grid disturbances to maintain stability and prevent disruptions. A machine learning approach is proposed to address this issue, providing accurate and responsive frequency forecasting in power systems. This paper introduces a novel methodology that leverages machine learning for short-term minimum frequency prediction, emphasizing efficiency and rapid response. A comprehensive experimentation process was conducted using several popular machine learning models, with their hyperparameters optimized through a Bayesian algorithm and evaluated via cross-validation. Results highlight the effectiveness of Decision Trees, offering a balance between efficiency and efficacy. Validation was conducted using the SCADA of a Typhoon HIL real-time simulator, verifying that the proposed methodology is suitable for real-time applications.