Refined estimation of inertia can provide a reliable basis for power system operation and control. This paper proposes a data-driven approach for the estimation of inertia, which can estimate the effective inertia of different areas. Based on the theory of eigenstructure analysis, the intrinsic relationship between inertia, eigenvalue and eigenvector was analyzed using a linearized dynamic equation. Furthermore, a detailed mathematical expression between inertia and eigenvalues, eigenvectors was established. In addition, dynamic mode decomposition (DMD) was introduced to extract eigenvalues and eigenvectors from the synchronized measurements, which can ensure that the scheme proposed in this paper can realize the estimation of effective inertia by using only the outputs measured by PMU. The effectiveness of the proposed approach was demonstrated through numerical simulations on the IEEE 16-machine 5-area test system and the real measurements of an actual power system.

The inertia constant is a key parameter of synchronous grids that are robust to disturbances. This paper presents an inertia estimation method for a multi-area interconnected electric power system. The method utilizes the electromechanical oscillation response measured with a phasor measurement unit. On the basis of the classical swing equation, the mathematical relationships between inertia and electromechanical oscillation parameters (i.e., oscillation frequency and damping ratio of a mode) are determined. The equivalent inertia of the system can be estimated by extracting the frequency and damping ratio of a mode from the observed active power on the tie line in the electromechanical oscillation response. Moreover, the system damping coefficient can be estimated without additional calculation and measurement. The inertia estimation errors caused by the poor extraction of oscillation parameters are reduced by utilizing the advanced parameter identification technology called adaptive local iterative filtering decomposition to identify the frequency and damping ratio of a mode. The accuracy and robustness of the proposed method are verified by using two simulation examples and an actual system.

A novel approach for estimating the inertia of an interconnected power system is presented using the identification of interarea oscillation modes (frequency, damping and mode shape) extracted from ambient data. The proposed method concentrates on estimating the values of the effective inertia of each area rather than the equivalent inertia of the entire system. Based on an equivalent two-machine system (ETmS) obtained by combining small signal stability analysis (SSSA) with the structure of the power grid, we derive a mathematical relationship between the effective inertia of each area and the interarea oscillation modes. Furthermore, the interarea oscillation modes can be extracted from ambient data, and the developed scheme enables an online estimation of the inertia only by using the outputs measured by PMUs. The performance of the proposed methodology is tested via numerical simulation cases and real data.

An online, data-driven algorithm for tracking voltage sensitivity in active distribution networks is presented. This algorithm, based on power flow analysis and recursive least squares with a forgetting factor (FFRLS), extracts voltage sensitivity from measurement data. Its adaptive nature ensures accuracy despite changing operating conditions. Outlier mitigation with the Isolation Forest algorithm enhances the methods reliability. Simulation results on a 33-node test system validate the approachs effectiveness.

The battery energy storage system (BESS) integrated with a wind farm is an efficient way to smooth wind power fluctuations and improve wind farm dispatchability. The presented study proposed a model predictive control (MPC)-based power dispatch strategy for a wind farm incorporated with dual-battery energy storage system (DBESS). The state space model of the active power balance of the wind farm incorporated with DBESS was established to analyze the operation mode of the DBESS. The MPC was then applied to acquire the control inputs of the two batteries, thereby enabling both batteries to respectively be in a state of charging and discharging to track the alternating dispatch order. Actual wind power data was applied in the simulation, wherein the results indicate that the proposed MPC-based dispatching strategy of the DBESS-incorporated wind farm effectively tracks the dispatching order using the output of the wind/battery storage hybrid system, thereby improve the dispatchability of the wind farm while simultaneously reducing battery switching times and extending the lifetime of battery.

In this paper, a line modal potential energy (LMPE) method is proposed for the inter-area oscillation analysis and damping control of doubly fed induction generators (DFIGs). This work presents a novel attempt to design the damping control loop using the LMPE approach from the network viewpoint. First, an LMPE function containing a DFIG output line is constructed. The distribution characteristics of the LMPE in a network are analyzed in different modes, and the dominant mode of the system is identified. In addition, this approach provides the basis for damping regulation. Second, the wide-area control signal and control locations are selected. Third, a damping controller for DFIGs based on LMPE and Extended State Observer-H$\infin$ control is designed, and this design is shown to have a certain anti-interference ability. The proposed method can be used for sub-mode analysis and oscillation suppression. Finally, the correctness of the LMPE method is verified through the analysis and simulation of a four-generator, two-area system and an actual power system in China.