The atmospheric stability and ground topography play an important role in shaping wind-speed profiles. However, the commonly used power-law wind-speed extrapolation method is usually applied, ignoring atmospheric stability effects. In the present work, a new power-law wind-speed extrapolation method based on atmospheric stability classification is proposed and evaluated for flows over different types of terrain. The method uses the wind shear exponent estimated in different stability conditions rather than its average value in all stability conditions. Four stability classification methods, namely the Richardson Gradient (RG) method, the Wind Direction Standard Deviation (WDSD) method, the Wind Speed Ratio (WSR) method and the Monin–Obukhov (MO) method are applied in the wind speed extrapolation method for three different types of terrain. Tapplicability is analyzed by comparing the errors between the measured data and the calculated results at the hub height. It is indicated that the WSR classification method is effective for all the terrains investigated while the WDSD method is more suitable in plain areas. Moreover, the RG and MO methods perform better in complex terrains than the other methods, if two-level temperature data are available.

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Monin–Obukhov similarity theory (MOST) overestimates the mean vertical velocity gradient in some atmospheric stable conditions, i.e., Richardson number R f < 0 . 25 . To obtain a given hub-height inflow velocity for a certain roughness length, this overestimated velocity gradient underpredicts the friction wind speed and the turbulence intensity, potentially influencing wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we first show that MOST predicts a larger velocity gradient for the atmospheric stability parameter ζ > 0 . 1 and proposes new flow similarity functions without constraining R f to limit the overestimated velocity gradient. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier–Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in a larger wake deficit and a slower wake recovery rate with a root-mean-squared error (RSME) of wake deficit in the range of 0.07 to 0.20. This overestimated wake effect is improved by applying the new similarity functions, and the RSME of wake deficit is reduced by 0.05 on average.

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid dispatching and grid planning. To address the problem of the low accuracy of traditional wind power interval prediction, a new interval prediction method of wind power is proposed based on PSR-BLS-QR with adaptive rolling error correction. First, one-dimensional wind power data are mapped to high-dimensional space by phase space reconstruction (PSR) to achieve data reconstruction and the input and output of the broad learning system (BLS) model are constructed. Second, the training set and the test set are divided according to the input and output data. The BLS model is trained by the training set and the initial power interval of training data is constructed by quantile regression (QR). Then, the error distribution of nonparametric kernel density estimation is constructed at different power interval segments of the interval upper and lower boundaries, respectively, and the corresponding error-corrected power is found. Next, the optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries. Finally, a test set is used for testing the performance of the proposed method. Three wind power datasets from different regions are used to prove that the proposed method can improve the average prediction accuracy by about 6–14% with the narrower interval width compared with the traditional interval prediction methods.

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