As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

Given the reverse distribution of energy supply and electricity demand in China, the Line-Commuted Converter High Voltage Direct Current (LCC-HVDC) delivery system has emerged as the primary solution to transmit renewable energy over long distances. The evaluation on potential damage of the wide-band oscillations to the grid stability requires the creation of a mathematical model of the LCC-HVDC delivery system to comprehensively analyze its interaction with renewable power stations. Traditionally, it has been assumed that the long DC transmission lines would separate the sending-end and receiving-end systems, leading to the simplification of the system into a single local rectifier station connected to a DC voltage source. This oversimplification, however, lacks a theoretical basis and hence, the scope of application of the equivalent model remains undefined. This study utilizes double Fourier transform to establish the impedance model of the LCC-HVDC delivery system, taking into account the receiving-end system which includes a remote inverter station and the AC grid. The internal harmonics distribution and propagation characteristics are then explored. Finally, the influence of the receiving-end AC grid on the complete system impedance is analyzed, providing insights into the dominant factors that determine the applicability of the equivalent model, which is limited to the rectifier station only.

The offshore wind power is an effective replacement for thermal power plants to realize the high renewable penetrated power system and guarantee the zero emissions. AC transmission and grid connection has the advantage among various solutions from the viewpoint of economics in implementation, where AC submarine cable is the medium of power transmission to deliver the offshore wind farm to the onshore grid. However, such offshore wind power system is more susceptible to the risk of high-frequency oscillation due to the highly distributed capacitance of AC submarine cables. To address this problem, this paper builds the impedance model for offshore wind power via ac submarine cable and further conducts the frequency-domain analysis towards the associated oscillation problem. Firstly, the impedance model of the collection network is constructed and validated. Furthermore, the system stability is analyzed based on the Nyquist criteria so as to analyze the effects of cable parameters on the oscillation frequency of the interconnected system. Finally, the simulation results are provided to verify the correctness of the proposed theoretical analysis.

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

The reliable operation of doubly-fed induction generator (DFIG) wind turbine (WT) systems rely on the accurate information of states. However, due to the unavailability of some states from phasor measurement units (PMUs), dynamic state estimation (DSE) for DFIG-WT connecting to the power system becomes essential. Although various DSEs have been applied, the variable stochastic wind speed was excluded into consideration, leading to the inaccurate estimation results. This paper develops the DSE using centralized Kalman filter (CKF) for DFIG-WT under the stochastic wind speed. The wind speed is modeled by stochastic differential equations (SDE), which can generate the trajectories with statistical properties similar to the wind speed historical data available for a particular location, so that the variable wind speed can be applied to the filtering process. Finally, the system involving a DFIG connected to a standard IEEE 14-bus system is utilized to verify the feasibility of the proposed method with the occurrence of electric faults.