8 results ·
● Live web index
listicle
D
dnv.com
article
https://www.dnv.com/training/a-guide-to-wind-farm-performance-optimization
Wind farm monitoring and data analysis to maximize energy capture. DNV has reviewed the performance of over 1000 wind farms and 50,000 turbines worldwide, including IEC-based power performance testing, availability audits, operational energy assessments, performance monitoring and benchmarking. The course will illustrate how to better monitor availability, power performance, and component health. To complement the warranties and formal procedures (see companion course “Wind Turbine Power Performance IEC 61400-12-1”), wind farm performance may be optimized and tracked through live operations monitoring and periodic detailed analysis. The SCADA data continuously recorded by each turbine and met masts on the wind farm can be interrogated to track performance and monitor the health of turbine components. The course will explain the value of SCADA data and how to leverage this to proactively manage performance. Explore the maritime training courses. ### Energy Academy. Explore the energy training courses. Explore the business assurance training courses. Explore the software training courses.
L
link.springer.com
article
https://link.springer.com/rwe/10.1007/978-3-030-31307-4_61
Optimizing wind farm layouts is to optimize the designable variables in a wind farm, during the farm planning before the wind farm construction. Based on the best knowledge of the planners on the wind farm, i.e. the wind farm modelling, the wind farm daily operation is simulated. The task of optimizing wind farm layout is implemented by establishing the optimization problem and solving it with proper methods. This chapter gives a short overview of the wind farm layout optimization in the field of wind energy. After briefly introducing the basic concepts and guidelines, the problem formulation of the wind farm layout optimization is presented, consisting of wind farm modelling, objective function, constraints and computational complexity. Different methods used for automated wind farm optimization are also presented, as the reference solutions to the task, including the calculus based methods, the heuristic optimization algorithms and the hybrid approaches. Additionally, the research needs and trends in wind farm optimizations, and the commercial software for optimizing the wind farm layout are provided in this chapter.
N
nrel.gov
official
https://www.nrel.gov/docs/fy19osti/72935.pdf
Results for case study 1 show that the best optimal wind farm layouts in this study were achieved by participants who used gradient-based optimization methods. For the first case study, in which the goal was to isolate variability in the optimization method, we pre-coded a representative wake model as a control variable and permitted participants to use any optimization strategy to alter turbine locations that would deliver the best annual energy production (AEP) for the farm. 3. Supplied Code To enable participation in this case study, we provided a link to a GitHub repository,∗which included files that had: • Turbine characteristics, wind frequency, and wind speed in IEA 37’s .yaml schema • Example turbine layouts for each farm size (in .yaml format) • Python parsers of the .yaml schema ∗https://github.com/byuflowlab/iea37-wflo-casestudies 3 This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
D
digitalrepository.unm.edu
research
https://digitalrepository.unm.edu/me_etds/263
UNM Digital Repository University of New Mexico UNM Digital Repository. # WIND FARM OPTIMIZATION AND ANALYSIS OF COMPLEX WAKE STRUCTURES. ## Author. A high-fidelity computational solver was developed with direct numerical simulation (DNS), large eddy simulation (LES), and Concurrent Precursor Method (CPM) capabilities. Extensive validation ensured the solver's accuracy in modeling fluid dynamics, including the wake characteristics of both utility and miniature-scale wind turbines. The study examined the influence of ground clearance on turbine performance, comparing two scenarios with different hub heights. Finally, the study uses a nine-turbine array model to explore wind farm behavior under varying wind conditions and yaw angles. This analysis highlighted that wind speed variations and yaw adjustments impact wake characteristics and power output, with implications for optimizing wind farm efficiency. Large-Eddy Simulation, Wind-Farm Optimization, Variability of Wind Speed and Yaw Angle, Ground Clearance of Wind Turbine. ## Degree Name. ## Level of Degree. ## First Committee Member (Chair). ## Second Committee Member. ## Third Committee Member. ## Fourth Committee Member. ## Author Corner.
D
diva-portal.org
article
https://www.diva-portal.org/smash/get/diva2:1150284/FULLTEXT01.pdf
NOMENCLATURE V Abdul Mouez Khatab Performance Analysis of Operating Wind Farms NOMENCLATURE AEP (Annual Energy Production) EIA (Energy Information Administration) EU (European Union) IEC (International Electrotechnical Commission) IRR (Internal Rate of Return) KPI (Key Performance Indicator) NPV (Net Present Value) NTF (Nacelle Transfer Function) O&M (Operation and Maintenance) PCWG (Power Curve Working Group) PEP (Potential Energy Production) RIX (Ruggedness Index) RSD (Relative Standard Deviation) SCADA (Supervisory Control And Data Acquisition) WRA (Wind Resource Assessment) WRF (Weather Research and Forecast) WTG (Wind Turbine Generator) T# (Turbines Number in a Wind Farm) TABLE OF CONTENTS VI Abdul Mouez Khatab Performance Analysis of Operating Wind Farms TABLE OF CONTENTS ABSTRACT ...................................................................................................................... Main category Sub-category Possible causes Loss Share [%] Offline losses Alarm Code Cause not specified by the alarm code 67.7 Down Time Maintenance, icing 15.5 Faulty/missing SCADA data (NaN) Icing on the wind sensors; other unknown causes 10.5 Icing Ice formation on the blades 4 Online losses Underperformance Incorrect control parameters; yaw misalignment; incorrect NTF; high wind shear and/or turbulence; other unknown causes 2.3 RESULTS, DISCUSSION, AND ANALYSIS 54 Abdul Mouez Khatab Performance Analysis of Operating Wind Farms Figure 26: Production loss of each wind turbine in 2014 divided into different categories Analyzing performance per year, the four analyzed years showed two distinctive features.
S
sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S1364032120303385
by F Azlan · 2021 · Cited by 110 — Focusing on optimising the micro-position of the turbines within the wind farm to minimise the wake effects can maximise the expected power output. There are
W
wes.copernicus.org
article
https://wes.copernicus.org/articles/8/865/2023
Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly." name="citation\_abstract">. # A comparison of eight optimization methods applied to a wind farm layout optimization problem. A comparison of eight optimization methods applied to a wind farm layout optimization problem A comparison of eight optimization methods applied to a wind farm layout optimization problem Jared J. Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly. P. J., Holt, W., and Ning, A.: A comparison of eight optimization methods applied to a wind farm layout optimization problem, Wind Energ.
N
nationaloffshorewind.org
article
https://nationaloffshorewind.org/wp-content/uploads/NREL-147506_Final_Report.pdf
Final Report – D5 Wind Farm Control and Layout Optimization for U.S. Offshore Wind Farms Prepared for: National Offshore Wind Research and Development Consortium Christine Sloan, Project Manager Julian Fraize, Project Manager Prepared by: National Renewable Energy Laboratory (NREL) Golden, CO 80401 Paul Fleming Principal Investigator David Dunn NOWRDC Project Coordinator Patrick Duffy, Christopher Bay, Matthew Churchfield Rebecca Barthelmie, Sara Pryor Report Agreement # 147506 FIA-19-16408 February 2023 2 Acronyms and Abbreviations AEP annual energy production BOEM Bureau of Ocean Energy Management CC cumulative curl FLORIS FLOw Redirection and Induction in Steady State GCH Gauss curl hybrid GW gigawatt IEA International Energy Agency LCOE levelized cost of energy MW megawatt nm nautical mile NREL National Renewable Energy Laboratory ORBIT Offshore Renewables Balance-of-System and Installation Tool SCADA supervisory control and data acquisition SOWFA Simulator fOr Wind Farm Applications TI turbulence intensity WRF Weather Research and Forecasting 3 Notice This report was prepared by Paul Fleming in the course of performing work contracted for and sponsored by the National Offshore Wind Research and Consortium (NOWRDC), New York State Energy Research and Development Authority (NYSERDA), and the U.S. Department of Energy (hereafter the “Sponsors”).