Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering

Abstract

This work presents a proactive distributed model for power system frequency stability. High-level penetration of renewable energy sources into the grid have introduced unforeseen and unmodeled system dynamics. Underfrequency load shedding state-of-the-art solutions are reactive in design, with efficiency constrained by the modeling error. Being able to detect unstable conditions early makes it possible to generate optimized corrective actions. In this work, phasor measurement units are used to predict frequency values. When a disturbance is detected, the state of frequency is predicted a few seconds into the future via a particle filter algorithm. Corrective actions are modeled through a mixed integer linear programming algorithm within system areas established through spectral clustering. The solution is implemented on Matlab, considering IEEE test systems. The proactive design of the method combined with its multiple layers of optimization deliver results that outperform state-of-the-art solutions. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects towards real-life implementation.