Confidence intervals for annual wind power production

Bensoussan, Alain; Bertrand, Pierre; Brouste, Alexandre; Haouas, Nabiha; Fhima, Mehdi; Koulibaly, Daouda

doi:10.1051/proc/201444009

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1937). In (Alain Bensoussan et al 2014), confidence intervals for annual wind power productions defined whereas in (Breno Menezes.2014), confidence intervals for reservoir computing's wind power generation applied. The datasets recorded in the form of SCADA datasets are considered as noisy hence GP estimates of confidence do not include this, but the model does separately estimate the magnitude of the associated uncertainty.…”

Section: = ±mentioning

confidence: 99%

Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model

Pandit

Infield

2020

IJPHM

View full text Add to dashboard Cite

Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine.Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process developed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then was compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a standard issue with a wind turbine, which causes underperformance. Hence it is used as case study to test and validate the algorithm effectiveness.

show abstract

Section: = ±mentioning

confidence: 99%