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Title: Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches

Journal Article · · Wind Energy
DOI:https://doi.org/10.1002/we.2722· OSTI ID:1846064
ORCiD logo [1]; ORCiD logo [2];  [3];  [3];  [4];  [4]
  1. Department of Industrial and Systems Engineering Texas A&,M University College Station Texas 77840 USA, Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember Surabaya East Java Indonesia
  2. Department of Industrial and Systems Engineering Texas A&,M University College Station Texas 77840 USA
  3. National Renewable Energy Laboratory Golden Colorado 80401 USA
  4. Wind Energy Institute of Canada Tignish PEI C0B 2B0 Canada

Abstract Wind power production is driven by, and varies with, the stochastic yet uncontrollable wind and environmental inputs. To compare a wind turbine's performance, a direct comparison on power outputs is always confounded by the stochastic effect of weather inputs. It is therefore crucial to control for the weather and environmental influence. Toward that objective, our study proposes an energy decomposition approach. We start with comparing the change in the total energy production and refer to the change in total energy as delta energy. On this delta energy, we apply our decomposition method, which is to separate the portion of energy change due to weather effects from that due to the turbine itself. We derive a set of mathematical relationships allowing us to perform this decomposition and examine the credibility and robustness of the proposed decomposition approach through extensive cross‐validation and case studies. We then apply the decomposition approach to Supervisory Control and Data Acquisition data associated with several wind turbines to which leading‐edge protection was carried out. Our study shows that the leading‐edge protection applied on blades may cause a small decline to the power production efficiency in the short term, although we expect the leading‐edge protection to benefit the blade's reliability in the long term.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; National Science Foundation (NSF)
Grant/Contract Number:
DE‐AC36‐08GO28308; AC36-08GO28308; IIS-1741173 I; CCF-1934904
OSTI ID:
1846064
Alternate ID(s):
OSTI ID: 1846066; OSTI ID: 1855375
Report Number(s):
NREL/JA-5000-81389
Journal Information:
Wind Energy, Journal Name: Wind Energy Vol. 25 Journal Issue: 7; ISSN 1095-4244
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

References (17)

The increasing importance of leading edge erosion and a review of existing protection solutions journal November 2019
Gaussian Process-Aided Function Comparison Using Noisy Scattered Data journal April 2021
A kernel plus method for quantifying wind turbine performance upgrades: Kernel plus method journal April 2014
How does wind farm performance decline with age? journal June 2014
A case study of space-time performance comparison of wind turbines on a wind farm journal June 2021
Modeling wind-turbine power curve: A data partitioning and mining approach journal March 2017
Gaussian Process Operational Curves for Wind Turbine Condition Monitoring journal June 2018
Wind turbine power curve modeling for reliable power prediction using monotonic regression journal March 2020
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms journal January 2015
Performance analysis of a small wind turbine equipped with flexible blades journal March 2019
Data-driven multivariate power curve modeling of offshore wind turbines journal October 2016
Effects of Leading-Edge Protection Tape on Wind Turbine Blade Performance journal October 2012
Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression journal April 2020
Covariate matching methods for testing and quantifying wind turbine upgrades journal June 2018
Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique journal November 2019
Causal inference in statistics: An overview journal January 2009
Intelligent analysis of wind turbine power curve models conference December 2014

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