Oscillation issues of permanent magnet synchronous generator (PMSG) wind turbines (WTs) have an increasingly significant impact on grid stability, especially with their increased generation capacity. However, the machine-side model (MSM), involving machine-side converter (MSC) and generator, is generally simplified as an ideal voltage source to comprising the impedance model of a PMSG-WT used for stability analysis, thus causing deficiency in describing the machine dynamics. Besides, the impedance-based method considering the PMSG-WT as a black box only describes the system from the external output characteristics; as such, the shaping effect of the internal controllers on the impedance model has not been fully studied yet. This paper proposes the impedance model of PMSG-WTs consisting of generator, MSC, DC-bus and grid-side converter (GSC) for more accurate stability analysis. Furthermore, based on the General Nyquist Criteria (GNC) and eigenvalue sensitivity analysis method, the shaping effect of the controllers inside GSC and MSC on the PMSG external impedance is quantitatively studied. The influence of the MSM on the system stability is investigated by comparing with the simplified model through extensive simulation studies.

In power system communication and control, the wide-area measurement system (WAMS) is usually adversely affected by noisy measurements and data congestion, posing great challenges to the stability and functionality of modern power grids. This study proposes a stochastic event-triggered robust dynamic state estimation (DSE) method for non-Gaussian measurement noises, using the cubature Kalman filter (CKF) technique. To reduce the computational burden and data transmission congestion resulting from centrally processing the measurement data, the proposed event-triggered robust CKF (ET-RCKF) is deployed at a local level with appropriate system formulation. The proposition of the novel robust DSE strategy is detailed in this brief, with its stability mathematically analyzed and proven, and simulation study on the IEEE 39-bus benchmark test system verifies the effectiveness of the proposed ET-RCKF approach. This novel DSE method is able to cope with non-Gaussian measurement noises and produce highly satisfactory estimation results, leading to wide applicability in real-world power system applications.

The frequent oscillations in the weak-grid-tied permanent magnet synchronous generator (PMSG) wind farms can severely deteriorate the power system security. The impedance-based method is widely applied but limited to optimize the system, where the PMSG is regarded as a black box and only the external output characteristics are reflected. In this paper, a novel impedance-based analytical approach is proposed to analyze the weak-grid-tied PMSG wind farms from a control interaction perspective, to complement existing studies. The shaping effect of the interactions among various controllers on the impedance of PMSG is analyzed, including the phase-locked loop (PLL), the voltage control loop and the current control loop. Based on the analytical analysis, an optimization method is then devised to appropriately design the parameters of the voltage control loop and improve the system stability margin. Time-domain simulation results in Matlab/Simulink verify the efficacy of the proposed method. The outcome of this study can provide useful guidance on control designs for PMSG-WTs in weak grids.

Power system events such as load disconnection (LD), generator trip (GT) and short circuit fault, affect the stability of power grids. It is significant for grid operators to acquire the classifications and locations of events quickly and accurately, which can help prevent blackouts and economic losses. Utilizing phasor measurement unit (PMU) data, this paper proposes a two-layer algorithm that monitors the power system state and identifies types and locations of events. In the first layer, with Gramian Angular Field (GAF) and Short-Time Fourier Transform (STFT), PMUs data are mapped to 2D images containing frequency information of the events, and then frequency features are extracted to classify the events by Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). Based on the results of the first layer, the second layer further determines the locations of the events. Graph Attention Network (GAT) is used to extract spatial information in the power system topology, which is insensitive of event types. CNN can enhance the feature gap between different events, and LSTM can extract temporal information from the PMU data. With the extracted temporal-spatial information, the second layer can produce the locations of events with high accuracy. The proposed two-layer algorithm is tested in the IEEE Standard New England 39 Test System. Compared to benchmark models, the proposed method has higher event identification and location estimation accuracy and stronger noise resistance.