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.

To solve the problems of weak inertia support and poor frequency regulation capability of power system, a novel coordinated control strategy for frequency regulation of MMC-HVDC connecting offshore wind farms (OWFs) is proposed to improve system frequency stability. In terms of inertia support, based on analyzing the available energy margin of MMC-HVDC, a virtual inertia control method based on decoupling control of DC voltage and capacitor voltage is proposed, which can fully utilize the energy margin of capacitors and improve its inertia support capability. The coupling relationship between the rotor speed of wind turbines and the frequency is directly established through DC voltage, an inertia support coordinated control strategy for MMC-HVDC and OWFs is designed to realize the rational utilization of available energy. In terms of primary frequency regulation, according to the local DC voltage, a primary frequency regulation control strategy is proposed for OWFs based on the variable speed control and pitch angle control. The overall coordination is realized through the reasonable design of control links and parameters, so as to improve the frequency stability of the system. Finally, the simulation model of MMC-HVDC connecting OWFs is built to verify the validity of the proposed control strategies.

Modular multilevel converter based high voltage direct current (MMC-HVDC) transmission is the prospective technology for large-scale wind farms integration. In this paper, a comprehensive coordinated control strategy for DC fault ride-through (FRT) of wind farms connected to the grid through bipolar MMC-HVDC transmission is proposed. To reduce the requirement of breaking speed and capacity of DC breakers, an active current-limiting control method is proposed to limit the rise rate of DC fault current, and the principle of parameter selection is designed. After DC breakers isolate fault, the control mode switching methods of bipolar MMC are designed to make the fault pole MMC operate as a STATCOM to support the AC grid and improve the active power transmission capability of the healthy pole MMC, which can reduce the impact range of unbalanced power between wind farms and converter station. In addition, a coordinated control strategy for wind farms load-shedding considering speed constraints is designed to realize the optimal distribution of load-shedding power among wind turbines, which can regulate unbalanced power together with the healthy pole MMC to realize DC FRT. Finally, the effectiveness and feasibility of the proposed control methods are verified by simulation results.

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 accuracy and reliability of the Q/V sensitivity for voltage security assessment is influenced by the outliers present in the calculation results. An unsupervised machine learning approach, empirical- cumulative- distribution- based outlier detection (ECOD), is introduced in this letter to detect and eliminate outliers to address this issue. A comparison of the results with those of the proposed approaches on the standard test power system CSEE-VS demonstrate that, compared with advanced outlier detection algorithms, ECOD can eliminate outliers from the Q/V sensitivities with higher accuracy and less computation time and realize online voltage security assessment with superior accuracy and reliability.