Data-driven yaw misalignment correction for utility-scale wind turbines

TL;DR

This paper introduces a cost-effective, data-driven framework using SCADA data and machine learning to detect and correct yaw misalignments in wind turbines, significantly improving power output and reducing loads without additional costly instruments.

Contribution

It presents a novel SCADA-based method for static and dynamic yaw error correction applicable to large-scale wind farms, eliminating the need for expensive ground truth instruments.

Findings

Hybrid model reduces yaw error by up to 85%.

Method is transferable to detect various yaw errors.

Significantly improves turbine performance and load management.

Abstract

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as LiDAR to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine SCADA data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.