Integration of wind power into grids requires accurate forecasting, however, traditional wind power point forecast errors are unavoidable and they cannot be eliminated due to the highly volatile and uncertain in the chaotic time series of wind power. Unlike point prediction, which conveys no information about the prediction accuracy, probabilistic interval forecasts can provide a range, within which the target will lie with a certain probability, for estimating the potential impacts and risks facing the system operation. Most existing prediction interval (PI) construction methods are often placed after a deterministic forecasting model with or without prior assumptions, this paper propose a novel lower-upper bound estimation approach using extreme learning machine to directly construct PIs for wind power series. Based on the analysis of the interval forecasting error information in training dataset, a new problem formulation is developed in this method to get better PIs. In addition, in order to obtain the global optimal solution of the above model, a quantum bacterial foraging optimization algorithm is proposed by introducing the theory of quantum mechanics into bacteria foraging behavior. The testing results from two real wind farms with different confidence probability and optimization criterion demonstrate the excellent quality of PIs in terms of both reliability and sharpness, which provide a support for the steady operation of power system with wind power integration.