Dynamic Estimation of Load-Side Equivalent Inertia by Using Clustering Method

With the increasing share of renewable energy and power electronics, the power system is gradually showing the characteristics of low inertia and spatial distribution. This transition deteriorates system's frequency response and poses a major threat to system stability. The majority of research in this area investigates the methods of providing inertia from the supply side. However, the load response also plays a crucial role in determining the frequency response. Hence, in depth knowledge about the amount of inertia provided by load is extremely important for a future application of units supplying synthetic inertia. In order to accurately grasp the load inertia level, a data-driven equivalent inertia aggregation estimation method is proposed. To achieve the load-side inertia aggregation estimation under different fault scenarios, a dynamic aggregating method is proposed, which uses the $K$-means algorithm to aggregation the grid based on load-side spectral features. Then, according to voltage dependency and rotating characteristic under disturbances, an area inertia estimation model is constructed to estimate the inertia of the aggregated area. By applying the proposed method, the accuracy of inertia estimation under multiple operating conditions is increased by considering the dynamic behaviour of inertia distribution. Finally, using the IEEE 29 buses system, the proposed method is illustrated.