A Gaussian Process Based Approach to Estimate Wind Speed Using SCADA Measurements from a Wind Turbine

被引：3

作者：

Paiva, Eduardo B. R. F. ^{[1
,2
]}

Nguyen, Hoai-Nam ^{[1
]}

Lepreux, Olivier ^{[1
]}

Bresch-Pietri, Delphine ^{[2
]}

机构：

[1] IFP Energies Nouvelles, Solaize, France

[2] MINES ParisTech, Paris, France

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 20期

关键词：

Wind Speed Estimation; Wind Energy; Machine Learning; Gaussian Process Regression; Real-time; KALMAN FILTER; PITCH CONTROL; SYSTEM;

D O I：

10.1016/j.ifacol.2021.11.154

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a method for estimating in real-time the speed of the wind to which a turbine is subjected using its SCADA (Supervisory Control And Data Acquisition) measurements. The approach is fully data-driven. It is based on Gaussian Process Regression. We use real experimental SCADA data from an operating commercial 3-bladed horizontal axis wind turbine. The reference values for the wind speed are obtained from a nacelle LiDAR (Light Distancing and Ranging) sensor. The comparison of the obtained estimation results with the measurements provided by the LiDAR sensor emphasizes the performance of the proposed method and underlines its interests for control purposes. Assessing performance on a day of operation, we obtain median errors of less than 1%. A numerical comparison with a more traditional model-based approach is also provided. Copyright (C) 2021 The Authors.

引用

页码：65 / 71

页数：7

共 50 条

[1] Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model
Pandit, Ravi Kumar
Infield, David
INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT, 2018, 9 (01)
[2] SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes
Pandit, Ravi Kumar
Infield, David
IET RENEWABLE POWER GENERATION, 2018, 12 (11) : 1249 - 1255
[3] Estimation of the Ambient Wind Field From Wind Turbine Measurements Using Gaussian Process Regression
van der Hoek, Daan
Sinner, Michael
Simley, Eric
Pao, Lucy
van Wingerden, Jan-Willem
2021 AMERICAN CONTROL CONFERENCE (ACC), 2021, : 558 - 563
[4] Wind Turbine Load Mitigation using MPC with Gaussian Wind Speed Prediction
Liu, Yanhua
Patton, Ron J.
Shi, Shuo
2018 UKACC 12TH INTERNATIONAL CONFERENCE ON CONTROL (CONTROL), 2018, : 32 - 37
[5] Predictive control of an experimental wind turbine using preview wind speed measurements
Verwaal, N. W.
van der Veen, G. J.
van Wingerden, J. W.
WIND ENERGY, 2015, 18 (03) : 385 - 398
[6] A practical method to estimate wind turbine wake characteristics from turbine data and routine wind measurements
Magnusson, M.
Smedman, A.-S.
Wind Engineering, 1996, 20 (02): : 73 - 92
[7] The Influence of the Wind Speed Profile on Wind Turbine Performance Measurements
Wagner, Rozenn
Antoniou, Ioannis
Pedersen, Soren M.
Courtney, Michael S.
Jorgensen, Hans E.
WIND ENERGY, 2009, 12 (04) : 348 - 362
[8] Wind Turbine Gearbox Forecast Using Gaussian Process Model
Wang, Xueru
Zhou, Jin
Guo, Peng
26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC), 2014, : 2621 - 2625
[9] Modeling of Wind Turbine Power Curve Based on Gaussian Process
Zhou, Jin
Guo, Peng
Wang, Xue-Ru
PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2014, : 71 - 76
[10] Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data
Moynihan, Bridget
Tronci, Eleonora M.
Hughes, Michael C.
Moaveni, Babak
Hines, Eric
RENEWABLE ENERGY, 2024, 227

← 1 2 3 4 5 →