A two-stage online inertia estimation: Identification of primary frequency control parameters and regression-based inertia tracking

In recent years, power system inertia has significantly decreased and has become more variable due to the massive integration of converter-interfaced renewable energy sources. Real-time awareness of the inertia present in the system is essential for operators to take preventive actions and mitigate potential instability risks. Online inertia tracking methods based on field data have been used to accomplish this task. However, most existing methods are disturbance-based and few have proven effective under normal operating conditions. In addition, some methods require prior knowledge of the primary frequency control dynamics, which are usually unknown, especially in presence of power converters. To overcome these limitations, this paper proposes a two-stage online inertia estimation method. The first stage estimates the primary frequency control parameters. The second stage uses a regression-based approach to track the inertia in real time. A sensitivity analysis of the parameters of the regression model is used to determine the conditions under which the primary frequency control parameters must be updated. The performance of the method is validated using the IEEE 39-bus benchmark network under normal operating conditions and under the occurrence of large disturbances. The algorithm is also tested in the presence of converter-interfaced sources controlled in both grid-following and grid-forming modes. Real-time tests validate the applicability of the method.