Abstract

Ice detection of wind turbine and estimating the resultant production losses is challenging, but very important, as wind energy project decisions in cold regions are based on these estimated results. This paper describes the comparison of a statistical (T19IceLossMethod) and numerical (Computational Fluid Dynamics, CFD) case study of wind resource assessment and estimation of resultant Annual Energy Production (AEP) due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition (SCADA) data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. To better understand the wind flow physics and estimation of the wind turbine wake losses, Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.

Highlights

  • Ice detection of wind turbine and estimating the resultant production losses is challenging, but very important, as wind energy project decisions in cold regions are based on these estimated results

  • This paper describes the comparison of a statistical (T19IceLossMethod) and numerical (Computational Fluid Dynamics, CFD) case study of wind resource assessment 10 and estimation of resultant Annual Energy Production (AEP) due to ice of a wind park in ice prone cold region

  • Gravdahl et al (Davis et al, 2016) performed CFD analysis for wind resource assessment and wind park design layout optimization, where they found that design of a wind park layout based upon CFD simulations can help to 40 increase the wind energy production

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Summary

Introduction

Due to increasing demand of electrical power and needs of protecting the environment, there has been a growing need of rapid 20 expansion and better use of renewable energy resources to reduce the carbon emissions. 30 The International Energy Agency (IEA) T19 set out a statistical method to assess production losses due to icing based on standard SCADA data available from modern wind parks, named ‘T19IceLossMethod’ and developed by international expert group IEA Wind TCP Task 19. (IEA Wind Task 19, 2018) T19IceLossMethod is the standardized open source Python code model (IEA Wind Task 19, 2019) for assessment of wind power production loss (IEA Wind Task 19, 2018) and compared different sites with a systematic analysis method, as well as evaluate the effectiveness of various blade heating systems. (Bilal et al, 2015) This paper describes the statistical and numerical case study of wind resource assessment and wind park design layout effects on 55 energy production in icing climate. To better understand the wind turbine wake effects on flow behavior and resultant power production, Larsen wake model is used to calculate the AEP of each wind turbine.

Statistical Model
Numerical Model
Ice Detection
Wind Park Layout Optimization
Conclusion
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