In order to enable the wind power system run smoothly and reduce the economic loss caused by power system fluctuation, and at the same time improve the competitiveness of the wind power system, it is of great and practical significance to find a stable and accurate wind speed prediction method. Among the machine learning methods, a neural network based on the back propagation (BP) algorithm to adjust the weight is one of the most commonly used and most effective methods. Although the BP neural network has a strong ability to fit nonlinearity, it converges slowly in the process of adjusting weight and easily falls into local optimal values. In order to effectively solve these two problems that will occur in the prediction process of the BP neural network, genetic algorithm (GA) was used to optimize the neural network in this paper. On this basis, considering the intermittency, non?stationarity and difference of wind speed series, a short?term wind speed prediction model EMD?GA?BPNN based on empirical mode decomposition (EMD), genetic algorithm (GA) and BP neural network was proposed. The reliability and advantages of this model in short?term wind speed prediction were verified by transverse comparisons with other models.

Due to the interference of noise, periodicity, nonlinearity and nonstationarity, most of the existing causal analysis methods are often unreliable and inaccurate in industrial process control systems. In order to improve the plant-level oscillation source location performance, a causal relationship detector based on improved convergence cross mapping was proposed. First, the adverse effects of noise and periodicity on causality detection were pointed out. Then, the empirical mode decomposition (EMD) and the trend fluctuation analysis were combined to realize the oscillation signal denoising. The periodicity of the signal was effectively removed by singular spectrum analysis. The denoising and periodicity?removing signal was analyzed by using convergent cross mapping, and then the source of plant level oscillation could be accurately located. Simulation results show that, the proposed method can improve the accuracy of plant?level oscillation source location in process control system.

The Finite Element Method was used to study the flow field of different screw combination sections of single?screw extruder. The Polyflow software was used to calculate the pressure field, shear rate field, velocity vector, mixing index and other parameters of the flow field of ordinary screw, ordinary screw and pineapple head combined screw, ordinary screw and pin combined screw. The post?processing results were compared and analysed.The results show that the ordinary threaded screw can provide a greater axial velocity for the fluid; although the pineapple head screw has greater resistance, the more oblique section design can provide better axial and circumferential velocity for the fluid; the barrier effect of the screw causes a part of the fluid to flow back. The three new types of screws can effectively stretch and shear the fluid while providing a good linear velocity for the fluid to make its dispersion and mixing more uniform. According to the analysis of the curve results, the mixing effect of pin?screw is the best.