In order to improve the small-signal stability of AC/DC hybrid systems containing voltage-source converter based high-voltage direct current (VSC-HVDC), this paper extends damping torque analysis (DTA) theory to VSC-HVDC supplementary damping controller to analyze its dynamic impacts and inhibit oscillations. Firstly, this paper presents the mechanism and procedure of VSC supplementary damping controller and establishes the linearized model of AC/DC system with damping controllers. On this basis, the DTA theory of VSC supplementary damping controller and the equation of damping torque index (DTI) are derived. The theory can be applied to the design of the damping controller including the selection of installation location and channel as well as the parameter tuning. Finally, case studies are carried out in two AC/DC hybrid systems containing VSC-HVDC. The simulation results verify the correctness and feasibility of DTI analysis. VSC damping controllers designed by the proposed method can effectively suppress oscillations and improve the dynamic stability of the AC/DC system.

Quantifying the uncertainty in the wind power forecasting process is of great significance to the stability, security and reliability of the power system. The uncertainty can be effectively quantified by the interval prediction. In this paper, a novel interval prediction method based on skip-connection-based gate recurrent unite (skip-GRU) network and block bootstrap is proposed to predict short-term wind power. Both model parameters and data noise are concerned in uncertainty quantification. A large number of subsets are generated by different bootstrap methods firstly, and then skip-GRU models are trained by these subsets. The prediction intervals (PIs) on test datasets are generated with three confidence levels ranging from 85% to 95%. The results of the case studies demonstrate that the proposed method is able to obtain satisfactory PIs, and the overall quality of the PIs is higher than other three bootstrap methods and benchmark models.

To enhance wind farm frequency regulation in renewable-dominant power systems, this paper proposes a bi-level hybrid framework integrating deep learning and model predictive control (MPC) by retaining the critical wake propagation delay while neglecting higher-order turbulence effects. The upper layer employs a synthetic inertial intelligent control strategy based on contractive autoencoder (CAE) and deep neural network (DNN). Particle swarm optimization (PSO) obtains optimal synthetic inertial parameters for dataset construction, CAE extracts features from multi-dimensional inputs, and DNN outputs optimal coefficients to determine the total power deficit the wind farm needs to supply. The lower layer uses a nonlinear model predictive control (NMPC) strategy with the discretized rotor motion equation as the prediction model and optimization under constraints to allocate the total power deficit to each turbine. MATLAB/Simulink case studies show that, compared with fixed-coefficient synthetic inertial control, the proposed framework raises the frequency nadir by 0.01–0.02 Hz, shortens the settling time by over 200 s under 2–4% load disturbances, and maintains rotor speed within the safe range. This work significantly enhances the wind farm’s frequency regulation performance, contributing to power system and energy sustainability.