Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

The wind power interval prediction of offshore wind farms and power plan arrangement of conventional thermal power units are of vital importance in the consumption of offshore wind power, the reduction of greenhouse gas impact on the environment, and the electric power system safe and economic operating. With the purpose of selecting the appropriate Copula function on the basis of the results of wind speed and wind power normal test, establish the mathematical model of wind-fire joint optimal scheduling, and optimize coal-fired power units power generation after comparing the convergence performance of particle swarm optimization method and crow search algorithm. Results indicate that the selected Copula function meets the expected criteria, and the optimized thermal unit climbs more smoothly and through the optimization of CSA the complete economic consumption of running is lessened. An idea is presented by this paper, which considers the uncertainties of offshore wind power generation, and the basis for the operational performance of CSA over PSO, and which provides a joint wind-thermal economic optimal dispatch strategy.