No abstract

With the increasing integration of wind and photovoltaic power, the security and stability of the power system operations are greatly influenced by the intermittency and fluctuation of these renewable sources of energy generation. The accurate and reliable short-term forecasting of renewable energy generation can effectively reduce the impacts of uncertainty on the power system. In this paper, we propose an adaptive, data-driven stacking ensemble learning framework for the short-term output power forecasting of renewable energy. Five base-models are adaptively selected via the determination coefficient (R2) indices from twelve candidate models. Then, cross-validation is used to increase the data diversity, and Bayesian optimization is used to tune hyperparameters. Finally, base modes with different weights determined by minimizing the cross-validation error are ensembled using a linear model. Four datasets in different seasons from wind farms and photovoltaic power stations are used to verify the proposed model. The results illustrate that the proposed stacking ensemble learning model for renewable energy power forecasting can adapt to dynamic changes in data and has better prediction precision and a stronger generalization performance compared to the benchmark models.

With the increasing penetration of wind power into modern power systems, accurate forecast models play a crucial role in large-scale wind power consumption and power system stability. To improve the accuracy and reliability of ultrashort-term wind power prediction, a novel deterministic prediction model and uncertainty quantification with interval estimation were proposed in this study. In consideration of the dynamic characteristics of a generator and conditional dependence, the generator rotor speed and pitch angle were regarded as the indicators of the dynamic characteristics of the generator, and light gradient boosting machine (LGBM) with a Bayesian optimization method was explored to build the deterministic prediction model. Considering the conditional dependence between output power and forecast error, A fuzzy C-means clustering method was used to cluster forecast errors into different clusters, and the best error probability distribution was obtained by fitting the error histogram with nonparametric kernel density estimation. Prediction intervals at different confidence levels were calculated and the error certainty was quantified. A case study was conducted to compare prediction accuracy and reliability by using the present and proposed methods. Results demonstrate that the LGBM deterministic prediction model combined with Bayesian optimization has better prediction accuracy and lower computational cost than the comparative models, specifically when the input features are high-dimensional big data. The nonparametric estimation method with conditional dependence is reliable for interval prediction. The proposed method has a certain reference value for wind turbines participating in frequency regulation and power control of power grid.

Abstract-Direct-driven wind turbine has become one of the mainstream technologies in wind power generation. Studying the system structure and the control technology of direct-driven wind turbine is the basis for optimization operation of direct-driven wind turbine. The system structure of direct-driven wind turbines is analyzed and the main subsystem mathematical model of direct-driven wind turbine are established, including wind speed model, aerodynamic model, generator model, and grid side converter model. The overall model of direct-driven wind turbine is established by integration of each subsystem mathematical model. The overall model of the direct-driven wind turbine is modeled and analyzed, and the simulation results show that the direct-driven wind turbine model can accurately reflect the dynamic characteristics of the directdriven wind turbine operation process.