Abstract: A structural dynamic parameter identification method aided by a rPCK surrogate model comprises the following steps. Establish a finite element model that roughly reflects the structural system to be analyzed. Establish the dynamic parameter space sample set. The structural system response space sample set driven by the dynamic parameter space sample set is established by using the probabilistic finite element analysis. The robust polynomial Chaos Kriging surrogate model is obtained by mapping the dynamic parameter space sample set to the structural system response space sample set. The measured structural system response is used to drive the rPCK surrogate model, and then Bayesian inference is used to identify the structural dynamic parameters. The mean value of Bayesian posterior estimation is used as the estimated value of structural dynamic parameters. The proposed method creates conditions for establishing a high-fidelity finite element model of the actual engineering structural system.

Abstract: A forecast method and system of wind power probability density. The forecast method includes: acquiring wind power data, preprocessing the wind power data, establishing a data set; then, constructing a time-variant deep feed-forward neural network forecast model, where the model includes multiple layers of neural networks, and each layer of neural network includes an input layer, a hidden layer and an output layer which are connected in sequence; taking wind power data at adjacent moments as an input of two input layers of two adjacent layers of neural networks, taking probability density distribution of wind power at adjacent moments as an output of two output layers of two adjacent layers of neural networks, and training and testing the model; inputting the wind power data to be forecasted into the trained time-variant deep feed-forward neural network forecast model for forecasting to obtain a more accurate and reliable wind power forecast result.