With the expanding share of wind energy in power grids, accurate forecasting has become critical for maintaining system stability and operational efficiency. Notwithstanding, forecasting accuracy is compromised by uncertainties from fluctuating wind speeds and meteorological conditions. This paper proposes a novel multi-phase short-term wind power forecasting framework (multi-step ahead forecasting over a 1-hour horizon). Thus, decomposition of the wind power signal and feature extraction are initially implemented using Variational Mode Decomposition (VMD) and Principal Components Analysis (PCA), respectively, aiming to enhance input quality and reduce computational burden. The proposed forecasting model is built on a hybrid DL architecture merging a Convolutional Neural Network (CNN), Attention Mechanism (AM), and Deep Feedforward Neural Network (DFFNN). Given the impact of decomposition levels and extracted PCA components count on forecasting performance, a search-based scheme is developed to explore a pre-defined space (maximum decomposition level and extracted components count) to determine the optimal configuration for each interval. In the next phase, a Fuzzy Decision-Making (FDM) technique is employed to select a balanced and optimal configuration for the proposed model across the year. To demonstrate the proposed architecture’s efficacy and generalizability, the model is tested on two real-world data from La Haute Borne wind farm in France and Hill of Towie wind farm in Scotland. Results demonstrate that the proposed architecture with the selected configurations achieves significant accuracy and generalization, with average NRMSEs and NMAEs values of 0.428% and 0.333% for La Haute Borne wind farm and 0.502% and 0.381% for Hill of Towie wind farm.

This paper presents a coordinated control strategy based on direct Lyapunov theory to handle the consistency of AC grids in a multi-terminal (MT) modular multilevel converter (MMC)-HVDC systems during varying both loads and DC link voltage. As the first contribution, a set of dynamic equations is proposed based on separating the dynamics of MMCs upper and lower arms state variables. The dynamics consists of only their related upper/lower arms state variables leading to more effective components for the steady state terms of proposed control technique. To develop the dynamic parts of the controller, the global asymptotical stability of MT MMC-HVDC system is assessed by direct Lyapunov method. As another advantage of the separated dynamic equations, the Lyapunov theory is able to exploit very simple decoupled components for the dynamic parts of proposed control functions. Moreover, in order to specify the variation trend of Lyapunov coefficients, further stability analysis contributes to demonstrating the effects of Lyapunov coefficients on the MMCs state variable errors and its dynamic. As another main contribution of this paper, two independent capability curves based on the power injection capability of the MMCs upper and lower arms, are obtained which will be assessed through changing the input and output voltages as well as MMC parameters. Finally, simulation results in MATLAB software are utilized to verify the validity of proposed control strategy.