Improvement in output power assessment by wind turbine power curve modeling based on data mining

The accurate assessment of wind turbine output power is crucial in the process sizing farms. Typically, this based on manufacturer’s characteristic curve, which relates speed to output. However, curve often an idealized representation that may not accurately reflect actual under real-world conditions. To address limitation, various techniques have been employed develop more precise curves, including fitting, artificial intelligence, probabilistic models, and Gaussian processes. This paper introduces a novel method for modeling takes into account specific conditions at turbine’s location. involves transforming data graph resembles phase space commonly used statistical mechanics. By applying k-means algorithm space, clusters speeds can be identified. Furthermore, corresponding determined identified clusters. These provide valuable information constructing using adjustment function. utilizing method, authors demonstrate significant improvement accuracy estimation compared relying solely curve. proposed approach considers unique characteristics incorporates them process, resulting reliable advancement represents step forward optimizing farms ensuring their efficient operation.