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

Abstract

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