Sozen, Mehmet

Standridge, Charlie

Zeitler, Dave

English

Scholarworks@GVSU

Grand Valley State UniversityScholarWorks@GVSU2013 Presentations Big Data Conference4-26-2013Reflections on Lake Michigan Wind: A Study ofMotion Compensated Laser Pulse TechnologyUsing a Mobile Buoy PlatformDave ZeitlerGrand Valley State University, zeitlerd@gvsu.eduMehmet SozenGrand Valley State University, sozenm@gvsu.eduCharlie StandridgeGrand Valley State University, standric@gvsu.eduFollow this and additional works at: http://scholarworks.gvsu.edu/bigdata_conference2013This Article is brought to you for free and open access by the Big Data Conference at ScholarWorks@GVSU. It has been accepted for inclusion in 2013Presentations by an authorized administrator of ScholarWorks@GVSU. For more information, please contact scholarworks@gvsu.edu.Recommended CitationZeitler, Dave; Sozen, Mehmet; and Standridge, Charlie, "Reflections on Lake Michigan Wind: A Study of Motion Compensated LaserPulse Technology Using a Mobile Buoy Platform" (2013). 2013 Presentations. Paper 18.http://scholarworks.gvsu.edu/bigdata_conference2013/18GVSU Big Data Conference April 26, 2013 Reflections on Lake Michigan  Wind :   A Study of Motion Compensated Laser Pulse Technology Using a Mobile Buoy Platform  Dave Zeitler Mehmet Sozen Charlie Standridge Project Goals • To collect and analyze wind data essential to the consideration of future wind industry development on the Great Lakes • To develop real-time / hub-height offshore wind data collection capabilities called for by prior Great Lakes wind assessment studies  • To validate the use of laser pulse technology on a mobile platform as a viable wind assessment technology Summer 2012: Mid-Lake Plateau • May 8 through December 17 – 224 Days • Measurements once per second • Wind speed and direction • 6 range gates and on deck • 19,353,600 seconds • 135,475,200 wind speeds observations How Effective is the Laser Wind Sensor? • Noise at one-second requires aggregation of data • 10 minutes (traditional) • 30 seconds to 10 minutes depending on application • Is data valid at all heights (75, 90, 105, 125, 150, 175 m)? Explaining Variation • Does wind speed vary by: • Height (marine boundary layer) • Time of year • Location in lake  • November-December 2011 deployment • Summer 2013 deployment Modeling the One-Second Data  David Zeitler  2011/2012 Yeni Nieves, Biostatistics PSM  2012/2013 Daniel Hodges, Biostatistics PSM Andrew Borgman, Biostatistics PSM  2013/2014 Tyson Spoelma, Statistics Major Aaron Clark, Biostatistics PSM Modeling the One-Second Data  Challenges • We currently have about 55Gb of data, more coming. • Data comes from multiple sources (Laser Wind Sensor, Meteorological Package, Water Quality Sensor, NOAA bouy). • Data rates and recording times vary across sources from 1 second to 1 hour. • Data has highly variable quality. Modeling the One-Second Data  Data Quality Issues Modeling the One-Second Data  Missing data imputation Modeling the One-Second Data  Forecasting Modeling the One-Second Data  Wind profiling Generalization: u/ur = (z/zr)α  u -> wind speed @ z ur -> wind speed @ zr z -> height one zr -> height two α -> exponent  Modeling for Power Estimation  Objectives  • Accurate power estimation from wind speed and wind direction data • Integration for estimating total energy generation • Developing a model for a wind turbine with dynamic yaw control for more accurate energy estimation than traditional models with no yaw control Faculty members:   Mehmet Sözen  and Azizur Rahman Graduate Assistant:  Md Nahid Pervez Modeling for Power Estimation  010002000300040005000600070008000Available energy Betz model One-two-threeequationPolynomialmodelINL wind energy Dynamic modelEstimated energy (MWh) Estimated energy by different models Modeling for Power Estimation  Power curve for Gamesa Eolica G58-850 kW wind turbine  Modeling for Power Estimation  0200040006000800010000120001400016000Available energy Betz model one-two-threeequationPolynomialmodelINL wind energy Dynamic modelEnergy estimated (MWh) Lagerwey LW72 2000kW Gamesa Eolica G52 850kWEnergy estimated by different wind turbine models Modeling for Power Estimation  Range Gate Altitude (m) Energy output (MWh) Nameplate capacity (kW) Capacity factor RG1 75 1707.732224 850 37.9% RG2 90 1786.540252 850 39.3% RG3 105 1805.26632 850 39.7% RG4 125 1754.083151 850 38.5%  Modeling for Power Estimation  Wind rose of averaged speed per direction bin Modeling for Power Estimation  Wind rose of fraction time per direction bin Modeling for Power Estimation  Frequency distribution of RG 1 

Reflections on Lake Michigan Wind: A Study of Motion Compensated Laser Pulse Technology Using a Mobile Buoy Platform

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Reflections on Lake Michigan Wind: A Study of Motion Compensated Laser Pulse Technology Using a Mobile Buoy Platform

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