Impact of Probabilistic Modelling of Wind Speed on Power System Voltage Profile and Voltage Stability Analysis

Abstract

Wind speed characteristics may have a significant impact on the power system operation, stability and dynamics. Hence, appropriate modelling and quantification of wind probability distribution function (PDF) are critical for probabilistic power system analysis. Weibull distribution is the most widely used PDF for power system (particularly power system stability) studies, which might not be a valid PDF for different wind regimes having high, average, and low wind speed data. In this study, twenty different wind speed datasets (each dataset is different based on the location, duration, and resolution) have been collected and analysed with eight PDFs to model the wind speed to replicate different wind speed characteristics. The PDFs include Rayleigh, Weibull, Gamma, Lognormal, Normal, Inverse Gaussian, Generalized Extreme Value, and Exponential distributions. Root mean square error (RMSE) and the coefficient of determination (R2) are applied as the measure of accuracy. The obtained results show that the locations, duration, and resolution have a notable impact on the selection of suitable PDFs. It was found that the Gamma distribution is the best-suited PDF for representing low wind speed data. In comparison, the Weibull distribution represents the best PDF for high wind speed data. Also, the selection of the Gamma PDF for low wind speed data and Weibull distribution for high wind speed data is further validated by presenting voltage profile and voltage stability analysis. Study outcomes would assist power system planners to select the appropriate PDF for power system stability studies based on the characteristics of the available wind speed dataset.