Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Abstract

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

Wind Turbine Performance Decline with Age

“…A further SCADA-based study of performance decline with age is given in [13]: LiDAR and SCADA data are employed for the analysis of the behaviour of three Mitsubishi MWT-1000A wind turbines for the first four years of operation and the achieved estimate is an average decline of -0.52% per year, which is higher than the plausible expectations based on the reasoning of [6].…”

Data-driven characterization of performance trends in ageing wind turbines

“…In [12] and [13], two fleets of operating similar types of wind turbines are analyzed in order to estimate the variability of the ageing effects and it is observed that trends severe as in [10] regard a minority of the turbines. Further relevant studies about SCADA-based estimations of wind turbine performance decline are [14,15,16].…”

Estimation of Wind Turbine Performance Degradation with Deep Neural Networks

“…As the effect of power deficits are more prominent from cut-in to rated wind speeds, inclusion of data from this dynamic operating region of the turbine is essential for such analysis. Study by Kim and Kim (2021), based on the SCADA data, uses the turbine's power curve for performance comparison. As discussed in Veena, Mathew and Petra (2020), limitations of manufacturer's power curve in understanding the site specific dynamics of the velocitypower response of the turbines has been well established in several previous studies.…”