Research On Prediction And Numerical Simulation For Short-term Wind Speed In Wind-farm

As global energy problem becomes increasingly serious, wind power is a kind of important renewable energy. The installed capacity and single capacity increase quickly. The volatility of wind power influences the safety and stability of power grid. In order to reduce the impact of wind power to power grid and have the rea-sonable scheduling wind energy, wind power prediction is very important. Involved in wind speed prediction about medium and long term, the scholars have made ex-tensive research, and achieved good effect. But super short term and short-term wind speed has strong randomness and instability, and its prediction effect is not very ideal.The following studies are carried out around some key technical issues of randomness and non-stationary characteristics for wind speed time series:(1) The choice of multi-step prediction strategies. In the level of the prediction strategy, studying short-term wind speed prediction and analyzing the characteris-tics of a variety of traditional prediction strategies have been done. According to the limitations of traditional prediction strategies for short-term wind speed predic-tion, error compensation strategy is proposed. It is combined with DirMO strategy to get ComDirMO strategy. ComDirMO strategy effectively improves the mul-ti-step prediction accuracy.(2) The trend term extraction of time series for short-term wind speed. The basic principles of wavelet decomposition and empirical mode decomposition are introduced in detail. In order to improve multi-step prediction accuracy, trend term extraction is very effective. According to the characteristics of short-term wind speed, focusing on the trend term extraction method of wavelet decomposition and empirical mode decomposition, it studies high-frequency and low-frequency com-ponent prediction, part-high-frequency and low-frequency component prediction and low-frequency component prediction. Wavelet decomposition and empirical mode decomposition are analyzed in the short-term wind speed prediction. It is concluded that empirical mode decomposition theory is more suitable for the trend term extraction of short-term wind speed time series.(3) The chaotic characteristic and phase space reconstruction for short-term wind speed time series. Due to the strong randomness and instability characteristic of the short-term wind speed, it has the chaos characteristic by chaos theory. Based on the phase space reconstruction, embedding dimension m and delay time τ are determined to have the input vector. It can improve the prediction accuracy. (4) The combination prediction based on the combination of weights for short-term wind speed. The combination theory is put forward to solve the problem of BP neural network’s hidden node, and the inconsistent problem of embedding dimension results in phase space reconstruction. The linear combination method and the nonlinear combination method are studied. Combined with the empirical mode decomposition theory, it improves the prediction accuracy.(5) The combination prediction based on the optimal prediction model for short-term wind speed. Based on the theory of multi-step wind speed prediction, a multiple attribute decision making model is put forward. In the view of "cost", multiple performance indicators are considered, which include the prediction per-formance analysis of the historical data and the attributes of the future prediction information. The predictive value of a prediction model is determined as the com-bination prediction result to improve the prediction accuracy.(6) Expert system of wind speed prediction. Wind speed time series of differ-ent times for different sites have different characteristics. Combination prediction model and expert system theory are studied in this paper, the expert system of wind speed prediction is established, which takes the combination prediction theory as the framework.(7) The numerical simulation based on geometric Brownian motion for short-term wind speed. This method uses ITO theorem to simulate geometric Brownian motion. The calculation model is simple and easy application. By the chaotic characteristic and the power spectrum test, it shows that the numerical sim-ulation is in accord with the basic characteristics of short-term wind speed time se-...