Machine Learning Based Analysis and Quantification of Potential Power Gain from Passive Device Installation

Abstract

Passive device installation on wind turbine generators (WTGs) can potentially improve the power generation of WTGs. Yet, how much impact the installation will make is unclear because conducting controlled experiments is impossible due to ever-changing wind and weather that affect the power generation significantly. In addition, the potential improvement is believed to be in a small scale, such as 1-5%, which is less than a typical 3-8% variation level observed in wind data. This article proposes an adaptive kernel-based method and builds a surrogate model to reduce the level of unexplained variation in wind data. In addition, to establish experimental environments that are similar to a controlled situation, this article develops an analysis framework that utilizes two other nearby WTGs without any passive device installation.