scholarly journals Wind Turbine Power Curve Upgrades: Part II

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1503 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Driven ◽  
Power Curve ◽  
Yaw Control ◽  
Production Improvement ◽  
The Difference ◽  
Management Optimization

Wind turbine power upgrades have recently become a debated topic in wind energy research. Their assessment poses some challenges and calls for devoted techniques: some reasons are the stochastic nature of the wind and the multivariate dependency of wind turbine power. In this work, two test cases were studied. The former is the yaw management optimization on a 2 MW wind turbine; the latter is a comprehensive control upgrade (pitch, yaw, and cut-out) for 850 kW wind turbines. The upgrade impact was estimated by analyzing the difference between the post-upgrade power and a data-driven simulation of the power if the upgrade did not take place. Therefore, a reliable model for the pre-upgrade power of the wind turbines of interest was needed and, in this work, a principal component regression was employed. The yaw control optimization was shown to provide a 1.3% of production improvement and the control re-powering provided 2.5%. Another qualifying point was that, for the 850 kW wind turbine re-powering, the data quality was sufficient for an upgrade estimate based on power curve analysis and a good agreement with the model result was obtained. Summarizing, evidence of the profitability of wind turbine power upgrades was collected and data-driven methods were elaborated for power upgrade assessment and, in general, for wind turbine performance control and monitoring.

scholarly journals Wind Turbine Yaw Control Optimization and Its Impact on Performance

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 41 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Energy Conversion Efficiency ◽  
Test Case ◽  
Yaw Control ◽  
Control Optimization ◽  
Technology Improvement

The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

scholarly journals Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 100
Author(s):  
Davide Astolfi
Keyword(s):  
Data Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Data Driven ◽  
Power Curve ◽  
Research Perspectives ◽  
Analysis Methods ◽  
Curve Modeling ◽  
Working Parameters ◽  
Data Analysis Methods

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

Planetary Gear for Counter-Rotating Wind Turbines

2014 ◽  
Vol 658 ◽  
pp. 135-140 ◽  
Author(s):  
Radu Saulescu ◽  
Codruta Jaliu ◽  
Olimpiu Munteanu ◽  
Oliver Climescu
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Rotation Speed ◽  
Planetary Gear ◽  
Electrical Energy ◽  
Turbine Rotor ◽  
Higher Power ◽  
Electrical Generator ◽  
High Rotation Speed ◽  
The Difference

A specific problem of the wind turbines refers to the difference between the low rotation speed of the wind turbine rotor and the high rotation speed needed for the electrical generator. Usually, the adaptation between the speed of the turbine rotor and the electrical generator speed is achieved by means of a speed increaser. A recent alternative relates to the use of coaxial counter-rotating wind turbines, which can achieve higher power and improve the conversion efficiency of the wind energy into electrical energy (up to 25%) with a reduced cost of approx. 20-30% compared to similar single rotor turbines. Conceptually, the counter-rotating wind turbine systems can integrate a particular generator wherein the rotor is coupled to a row of blades and the stator with another row of blades, or a commonly generator, coupled to a differential planetary gear, that allows the summation of the blades motions.The paper describes and analyzes kinematic and dynamic aspects of a system consisting of two coaxial counter-rotating turbines and a generator, interconnected by a planetary gear with two inputs (the two turbines) and an output (the generator). The algorithm is based on the property of the differential planetary gear of adding two input motions into one output motion. The kinematic and dynamic parameters of the planetary gear are established in the paper, and a case study is further presented: a small wind turbine equipped with a transmission enabling input speed multiplication.

scholarly journals Effect of Ambient Reynolds Number on Small Wind Turbine Subjected to Low Wind Speed Conditions

2021 ◽  
Vol 12 (2) ◽  
pp. 223-231
Author(s):  
Joel Mbwiga ◽  
Cuthbert Z Kimambo ◽  
Joseph Kihedu
Keyword(s):  
Reynolds Number ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Reynolds Numbers ◽  
Design Parameters ◽  
Power Performance ◽  
Airfoil Surface ◽  
Small Wind Turbine ◽  
Low Wind Speed ◽  
The Difference

Wind flow over the airfoil surface is adversely affected by the differences between the design and ambient values of a dimensionless quantity called Reynolds number. Wind turbine designed for high Reynolds Number shows lower maximum power performance when installed in low-speed wind regime. Tanzanian experience shows that some imported modern wind turbines depict lower power performance compared to the drag-type locally manufactured wind turbines. The most probable reason is the difference between design and local ambient Reynolds numbers. The turbine design parameters have their properties restricted to the range of Reynolds numbers for which the turbine was designed for. When a wind turbine designed for a certain range of Reynolds numbers is made to operate in the Reynolds number out of that range, it behaves differently from the embodied design specifications. The small wind turbine of higher Reynolds number will suffer low lift forces with probably occasional stalls.  

Yawed Effect on Wind Turbine Near Wake

2014 ◽  
Author(s):  
Yuntian Ge ◽  
Xiuling Wang
Keyword(s):  
Wind Turbine ◽  
Flow Pattern ◽  
Wind Turbines ◽  
Air Flow ◽  
Aerodynamic Performance ◽  
Wake Flow ◽  
Near Wake ◽  
Rotation Plane ◽  
The Difference ◽  
Ansys Workbench

Wind turbines rotation was motivated by the force of wind. In reality, wind doesn’t moving vertically to the wind turbine rotation plane, but in random directions instead. Therefore, the yawed effect has to be taken into consideration when study wind turbine aerodynamic performance. The purpose of this study is to compare the difference between the wind turbine near wake flow with yawed effect and without yawed effect aerodynamically. The research uses CFD technology to simulate the rotation movement and air flow pattern, which is completed in software Ansys Workbench.

Influence of Wind Turbine Aero-Elastic Load on Dynamic Response of Floating Platform

2012 ◽  
Vol 608-609 ◽  
pp. 649-652
Author(s):  
Fa Suo Yan ◽  
Hong Wei Wang ◽  
Jun Zhang ◽  
Da Gang Zhang
Keyword(s):  
Dynamic Response ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Numerical Code ◽  
Floating Body ◽  
Response Analysis ◽  
Floating Platform ◽  
Floating Base ◽  
Wind Turbine System ◽  
The Difference

A numerical code, known as COUPLE, which has been developed to perform hydrodynamic analysis of floating body with a mooring system, is extended to collaborate with FAST to evaluate the interactions between wind turbine and its floating base. FAST is developed by National Renewable Energy Lab (NREL) for aeroelastic simulation of wind turbines. A dynamic response analysis of a spar type floating wind turbine system is carried out by the method. Two types of simulation of wind load are used in the analysis. One type is a constant steady force and the other is a six-component dynamic load from a turbulent wind model. Numerical results of related platform motions under random sea conditions are presented in time and frequency domain. Comparison of results is performed to explain the difference of two analyses. The conclusions derived in this study may provide reference for the design of offshore floating wind turbines.

scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Power Curve ◽  
Power Performance ◽  
Mean Wind Speed ◽  
Set Up ◽  
Power Curves

Abstract. The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.

Yaw Control of Small Wind Turbines With a New Passive Tail Device: Part 1 — Steady-State Case

Solar Energy ◽  
2003 ◽  
Author(s):  
Eduardo Rinco´n Meji´a ◽  
Jesu´s Tovar Salazar ◽  
Jo´zef Wo´jcik Filipek
Keyword(s):  
Steady State ◽  
Wind Turbine ◽  
Field Test ◽  
Wind Turbines ◽  
Strong Wind ◽  
Test Results ◽  
Yaw Control ◽  
Small Wind Turbines ◽  
Horizontal Axis Wind Turbines ◽  
State Case

This paper describes the behavior of a new tail device to yaw smoothly small wind turbine rotors out of the wind during strong wind or gusts. The passive tail device consists of a rigid short tail, an aerodynamic rotating vane, a tail bumper, and a spring. This passive tail device reduces gyroscopic loads, is easy to adjust, can be manufactured in smaller sizes, and is much stronger than conventional vanes used in small wind machines. Besides, the energy collected with it is greater. Field test results indicate that its behavior agrees very well with simulations, and that the regulator can be advantageously utilized, as compared with conventional vanes and other mechanical or electromechanical means, in horizontal-axis wind turbines with diameters of 12 m or smaller. Here the steady-state case (quasi-steady wind velocity is assumed) is analyzed, showing the technical viability of the regulator proposed.

Variation modes of vehicle interior roominess

2019 ◽  
Vol 234 (1) ◽  
pp. 212-227
Author(s):  
Sang-Don Lee ◽  
Seung-Lye Kim
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Shape Variation ◽  
Key Factors ◽  
Vehicle Interior ◽  
Plan View ◽  
Passenger Vehicles ◽  
Length Width ◽  
The Difference ◽  
To Receive

Vehicle interior roominess is one of the key factors for buyers’ satisfaction. Principal component analysis, principal component regression, and correspondence analysis are applied to understand the modes of variation for interior roominess among 15 dimensions from 76 passenger vehicles, to predict buyers’ satisfaction and to identify dimensions with unique proportions. Three variation modes are found: (1) the interior plan-view variation mode (plan-view = vehicle length × width) shows the largest difference between small and large vehicles, (2) the vehicle height variation mode exposes the difference between the short and tall vehicles, and (3) the greenhouse-shape variation model reveals the difference between sleek and boxy vehicles. Domestic brands are found to receive lower satisfaction even though they are similar in the 15 dimensions. The knee clearance for the second-row shows the largest difference in proportion followed by the diagonal head clearances for the first and the second-rows.

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