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.

The low and variable inertia due to the integration of large‐scale wind generation poses severe challenges to the frequency control of the power grid. In this study, a novel model predictive control (MPC)‐based load frequency controller (LFC), which can adapt to the changes in system inertia, is proposed to improve the frequency control effect. First, the inertia estimation scheme based on measurements is designed. Then, the state‐space model, which is the key that determines the control effect of MPC, is corrected in time based on the estimated inertia. The proposed method in this study can avoid the problem of MPC‐based LFC failure caused by inaccurate model parameters. The performance of the proposed strategy is demonstrated in two scenarios, i.e. wind power fluctuation and load multi‐step, using a two‐area interconnected power system with time‐variant inertia.

The existing inertia estimation based on electromechanical oscillation relies on a single mode, which is limited in complex systems. In this letter, the inertia is estimated without worrying about the number of modes in the oscillation. The inertia expression is derived based on the swing equation in the frequency domain, which considers the mode coupling in the electromechanical bandwidth using a summation calculation. The effectiveness of the proposed inertia estimation method is validated through simulation data and real measurements.

The harmonic pollution problem in power grids has become increasingly prominent with the large-scale application of power electronic equipment, nonlinear loads, and renewable energy. This study proposes a method based on adaptive variational mode decomposition (AVMD) and Hilbert transform (HT) that is applicable to harmonic detection in power system. The AVMD method constructs and solves the constrained variational model. Then, a single-frequency harmonic component with stable features can be obtained. The proposed method can effectively avoid the recursive process in empirical mode decomposition (EMD). In this study, the variational mode decomposition algorithm is used to obtain the periodic harmonic components concurrently. Subsequently, the characteristic parameters of each harmonic component are extracted via HT. Simulation analysis and measured data verify the validity and feasibility of the proposed algorithm. Compared with the detection results obtained using the EMD algorithm, the proposed method is proven to exhibit stronger applicability to harmonic detection in power system.