The Annals of Applied Statistics

Covariate matching methods for testing and quantifying wind turbine upgrades

Yei Eun Shin, Yu Ding, and Jianhua Z. Huang

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In the wind industry, engineers perform retrofitting upgrades on in-service wind turbines for the purpose of improving power production capabilities. Considering how costly an upgrade can be, people often wonder about the upgrade effect: whether it indeed improves turbine performances, and if so, how much. One cannot simply compare power outputs for the purpose of assessing a turbine’s improvement, as wind power generation is affected by an array of environmental covariates, including wind speed, wind direction, temperature, pressure as well as other atmosphere dynamics. For a fair comparison to discern the upgrade effect, it is critical to have these environmental effects controlled for while comparing power output differences. Most existing approaches rely on establishing a power curve model and let the model account for the environmental effects. In this paper, we propose a different approach, which is to devise a covariate matching method to ensure the environmental covariates to have comparable distribution profiles before and after an action of upgrade. Once the covariates are matched, paired $t$-tests can be applied to the power outputs for testing the significance of the upgrade effect. The relative increase in power production can also be quantified. The proposed approach is simple to use and relies on fewer assumptions than the power curve modeling approach.

Article information

Ann. Appl. Stat., Volume 12, Number 2 (2018), 1271-1292.

Received: January 2016
Revised: September 2017
First available in Project Euclid: 28 July 2018

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Causal inference Mahalanobis distance matching methods nearest neighbor matching observational study wind power curve


Shin, Yei Eun; Ding, Yu; Huang, Jianhua Z. Covariate matching methods for testing and quantifying wind turbine upgrades. Ann. Appl. Stat. 12 (2018), no. 2, 1271--1292. doi:10.1214/17-AOAS1109.

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