Improving Inertia Estimation Accuracy Using An Adaptive Framework for Centre of Inertia Selection

Abstract

The centre of inertia (CoI) is essential for accurate system inertia estimation. Assuming the CoI is static and based on fixed measurements ignores the fact that it changes over time. This can lead to inaccurate estimates since the CoI shifts as system conditions change. To address this issue, this paper presents an adaptive data-driven CoI selection framework that dynamically selects the optimal CoI in a multi-measurement power system. The optimal CoI is used based on selected parameters to estimate the total inertia between any two regions. This process is conducted at each time interval or whenever stamped data from system measurements are received. Simulation results verify that the proposed adaptive CoI selection, compared to a conventional method where the CoI is fixed to a specific pair of measurements, provides more accurate estimation results under varying loading conditions, perturbations, and inertia levels. This paper also explores future developments and identify gaps, while also discussing potential directions for upcoming research works.