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.

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.

This paper proposes a novel real-time adaptive under voltage load shedding (UVLS) method by using optimized fuzzy logic controllers to address fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability (STVIS) problems. An intelligent multi-input multi-output fuzzy logic load shedding controller is developed based on a short-term voltage recovery criterion (STVRC). For the controller, the magnitude deviation and the recovery time deviation of voltage trajectory are extracted as inputs, while the load shedding amount at the current load shedding stage and the time delay of the next stage are taken as outputs. Particle swarm optimization (PSO) technique is further employed for the tuning of the controller parameters to realize the voltage recovery requirement at the lowest cost of load shedding. The proposed UVLS method is formed by a group of the fuzzy logic controllers deployed at the load buses, each monitoring its own bus voltage and shedding load in closed-loop. Responding to the severity of voltage variations, the proposed method is able to adaptively determine the location, time and amount of load shedding in real-time to make the voltage satisfy the STVRC. The effectiveness of the proposed method compared to other methods is testified by simulations on the Nordic test system.

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.

A new generator phasor measurement unit (PMU) can provide a generator internal response during transient periods, thereby enabling online evaluation of generator damping in detail. In this paper, a method for the online detailed evaluation of the generator damping contribution during oscillation periods based on the damping loss factor (DLF) and multi-scale chirplet sparse signal decomposition (MCSSD) is proposed. First, the DLF in a simple elastic oscillation system, which can be used to quantitatively evaluate the damping characteristics of components, is introduced. After analyzing the feasibility of using the DLF during low-frequency oscillation, the generator damping loss factor (GDLF) is established to quantitatively evaluate the generator damping contribution, including the effects of the excitation system, governor system, and generator winding. Next, the modal composition of the generator dissipation energy is studied to extend the application of the GDLF to a multi-machine power system. Finally, a method for the online evaluation of generator damping during low-frequency oscillation based on the GDLF and MCSSD is presented. The proposed method is examined in detail using a single-machine infinite-bus system and then further demonstrated by applying it to a practical power system with multiple electromechanical oscillations.