Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system’s operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models.

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

Based on 3-d, uncompressible onflow model with steady N-S equation and the k-epsilon double equation, aerodymic characteristics of EMU and windbreaks on bridge under cross wind were studied numerically, the results show: (1) compared to no windbreak, EMU overturning moment was decreased 50% by setting general windbreak , 75% by setting ventilated windbreak; ventilated windbreak’s protective effect on train and pantograph-catenary system is better especially when H≥2.5m ; (2) aerodynamic load on ventilated windbreak is far lower than general windbreak; (3)the higher cross-wind velocity is, the more aerodynamic load decreased when setting ventilated windbreak. Besides, ventilated windbreak’s leak form could significantly reduce bridge’s self gravity and wind load, improve wind break ability and EMU operation safety.

No abstract