Study On Short-term Prediction Method Of Wind Speed And Wind Power

With wide distribution and large reservation, wind as a environmentally friendly renewable energy is received more and more attention. In the development of wind power, the wind power grid is also brought challenge to power quality, the safe operation of the power system and power supply and demand balance. How to reduce the impact of wind power network to the power system, as well as enhance the competitiveness of wind power generation is a difficult problem of developing wind power technology, which is in need of solution. The power dispatching department can be guided by the accurate predictions of short-term wind power prediction to timely adjust scheduling plan, so as to reduce the reserve capacity and operation costs, which is an effective way to promote the development of wind power technology.In this paper, wind speed and wind power short-term prediction technology is studied, through the short-term forecast for wind speed, the wind power forecast is completed based on the wind power curve. Correct analysis of wind speed time series properties will be beneficial to build the short-term prediction model scientifically and rationally. First, phase space reconstruction of wind speed time series is taken. By comparing of the original C-C method, the improved C-C method has the characteristics of high reliability, fast calculation and can also estimate the embedding dimension and delay time at the same time, so it is chose to estimate the parameters of the reconstruction of wind speed time series. Then, the chaotic characteristics of wind speed time series is verified by the correlation dimension and maximum Lyapunov index, so chaotic phase space reconstruction can be introduced to the wind speed prediction and laid the foundation for the prediction. And two commonly used traditional chaotic time series prediction method, the weighted zero-order local low and the weighted one-order local law is used to predict the short-term wind speed. The prediction results show that using chaotic time series prediction method to predict the short-term wind speed is feasibility. Finally, a new type of recurrent neural networks—echo state network which has high precision of chaotic time series prediction is introduced, and the process of establish the network, chaotic time series based iterative prediction and direct prediction are analyzed in detail. Combining the wind speed chaotic time series and the echo state network, the forecasting model of single-step wind speed prediction and eight hours ahead wind speed short-term prediction are established, and the MATLAB simulation results show that the prediction method is valid. Compared to the traditional chaotic time series prediction method, the prediction accuracy is improved. On the basis of standard wind power curve, the prediction values of wind power are obtained by the prediction values of wind speed. The simulation results indicate that the standard power curve is not fully comply with the actual relationship between wind speed and wind power, so as to make wind power prediction error is greater than the wind speed prediction error, but the prediction accuracy is still satisfactory. The proposed prediction method has opened a new space for wind power prediction technology.