Power system frequency nadir prediction based on data-driven and power-frequency polynomial fitting

As the proportion of renewable energy and power electronics equipment continues to rise, the level of rotational inertia decreases considerably, resulting in severe frequency stability challenges to the power grid. It is of great significance to accurately predict the frequency nadir following a large disturbance. This paper proposes a novel data-model fusion-driven approach for the prediction of frequency nadir. As the physics-driven part, a Simplified Prediction Model (SPM) based on power-frequency polynomial fitting is developed to quickly produce the frequency nadir. As the data-driven part, Back Propagation Neural Network (BPNN) is deployed to correct the errors of the SPM to achieve more accurate results. This serial integration scheme not only obtains the final prediction result with higher accuracy, but also meets the computational efficiency requirements of online prediction. Compared with existing integration-driven methods, SPM only focuses on the active power-frequency characteristics of the system, which retains the most critical effects and greatly reduces the dependence of BPNN on sample data quality. Case studies on a modified IEEE 39-bus system verify the effectiveness of the proposed approach. Copyright © 2024 Li, Wang, Qi, Wang, Wang, Zhou and Zheng.