Mobile Sensing for Wind Field Estimation in Wind Farms: Preprint

Conference · Tue Aug 25 00:00:00 EDT 2020

DOI:https://doi.org/10.23919/ACC45564.2020.9147861· OSTI ID:1669501

Pasley, David J.; Nicotra, Marco M.; Pao, Lucy; King, Jennifer; Bay, Christopher

This paper introduces a novel approach for estimating the wind field over an entire wind farm using a mobile sensor to collect limited amounts of data. The proposed method estimates the boundary conditions of a simplified turbine wake model by computing the model sensitivity matrix and using a recursive least-squares algorithm to recover the model parameters from the wind field measurements. To address the fact that it is not practical to take measurements across the entire wind farm, the proposed method classifies each area on the map based on its sensitivity to parameter variations. This classification is then used to generate a suitable path for a mobile sensor, which is charged with collecting data for the recursive least-squares algorithm. The proposed framework can successfully estimate the model boundary conditions using just the measurements collected along the path of the mobile sensor. This preliminary result paves the way for using real-time wind field estimates for the coordinated control of all the turbines within a wind farm.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4W)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1669501
• Report Number(s): NREL/CP-5000-76133; MainId:6082; UUID:2b60002c-6a52-ea11-9c31-ac162d87dfe5; MainAdminID:14090
• Conference Information: Presented at the 2020 American Control Conference (ACC), 1-3 July 2020; 77743
• Country of Publication: United States
• Language: English

Related Subjects

estimation
mobile sensing
WIND ENERGY
wind farm control