Adaptive detection and correction method for anomalous wind speed

To improve the accuracy and availability of the data acquisition system from wind farms, this study proposes to use the adaptive detection methods for effective detection abnormal wind speed. In allusion to characterization of the abnormal value was not obvious, choose Auto Regressive Integrated Moving Average to predict wind value of the current moment to obtain residual sequence. In order to reduce the interference of systematic errors, Using Empirical Mode Decomposition method gets the residual sequence of gross error characteristic information. With dual stochastic process by using Hidden Markov Mode of adaptive detection and removed abnormal value, to avoid the shortcomings of traditional threshold identification methods. Finally, using the cubic spline interpolation correcting abnormal data to get a complete wind speed sequence. RBF forecast results show that paper method to be better than traditional wavelet method and can be improved forecasting accuracy of short-term in wind-speed and power.