arXiv subjects

C. P. Connaughton

Publications and source records attributed to C. P. Connaughton.

3 recordsLinked to original sources

arXiv2026

Rician Distribution as a Physically Interpretable Model for Wind-Speed Statistics

The statistics of atmospheric wind variations are commonly modeled using Gaussian or Weibull forms, which often trade physical interpretability against statistical accuracy, especially in the distribution tails. Here we derive a Rician distribution for wind speed from a simple physical model based on two orthogonal Gaussian velocity components with a non-zero mean in the preferred direction. Using wind-speed records from four geographically distinct wind farms, we show that the Rician model consistently outperforms the Gaussian model and remains competitive with the Weibull model. The same behavior persists when the data are partitioned into monthly windows, where the Rician parameters also provide a transparent description of seasonal and geographic variability, compared to Weibull parameters. In addition, the model naturally connects Gaussian-like and Weibull-like regimes through the Rician parameter ratio $μ/σ$, making the Rician distribution a compact and physically interpretable two-parameter model for wind-speed statistics.

physics.data-an↗

arXiv2026

A Two-Regime Statistical Framework for Wind-Power Distributions: From Wind-Speed Fluctuations to Turbine Control

Wind-power variability is a major challenge for the reliable integration of utility-scale wind energy into modern power systems. Although wind-speed statistics are often described by simple parametric distributions, translating these statistics into turbine-level power fluctuations is nontrivial because the relationship between wind speed and power is highly nonlinear and changes across different turbine operating regimes. Here, we develop a two-regime statistical framework for wind-power distributions. In the aerodynamic operating regime, between the cut-in and rated speeds, the turbine power follows an approximate cubic dependence on wind speed. Starting from a physically motivated Rician model for the wind-speed magnitude, we derive an analytical expression for the corresponding wind-power distribution using a nonlinear change of variables. In the control-dominated near-rated regime, where active blade-pitch and generator control regulate the turbine output, the aerodynamic transformation is no longer applicable. Instead, we characterize the power deficit relative to the rated power and show empirically that its continuous tail is well described by a bounded stretched-exponential distribution for both individual turbines and wind-farm ensembles. These results provide a physically interpretable statistical description of wind-power fluctuations across the full operational range of utility-scale wind turbines.

physics.data-an↗

arXiv2015

Grid-scale Fluctuations and Forecast Error in Wind Power

The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_τ$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_ζ$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_τ$) and the scaling error ($e_ζ$).

physics.data-an↗