Liu Xin, Yan Shu, Chen Xinming, Mu Yanfei, Shi Shaoping

Huaneng Clean Energy Research Institute

Wake Conference 2017, June 1st, Visby, Sweden

The largest power generation company

in the world by installed capacity

By the end of 2016, the Company had

total installed capacity of 160GW,

including wind power capacity of 18GW

The first Chinese power producer to join

the ranks of Fortune 500 Companies,

ranking 217th in 2016

Founded in 2011

Institute directly under the Head Office of China Huaneng Group

Institution focused on the frontier technology research and development

of clean energy

Main business: technology research, development, transfer and service,

the manufacture of key facilities and project implement of a wide range

area including clean coal-based power generation and conversion,

renewable energy power generation, and technology on emissions

reduction of contaminations and greenhouse gases, etc.

Established on January 2017

Consulting Services – Overview

• Wind farm measurement technology（LiDAR）

• Wind resource evaluation

• Wake research

• Market Analysis

• Wind turbine performance measurements

• Post evaluation of wind farm project

Looking forward to international cooperation

CFD and Experimental Studies on Wind Turbines in Complex Terrain by Improved Actuator Disk Method

The classic AD method

is to simply the turbine rotor as an actuator disk with uniform thrust. The

rotor is modelled by porous cells （pressure drop） instead of the swept

area of the rotor.

21

2T

Tp C u

A

The improved AD method

defines the blades aerodynamically as two-dimensional airfoil data. The

local angles of attack and the lift and drag coefficients, CL and CD, are

used to compute the forces acting on the blade

21f ,

2r Lel L D DcB C eU C e

The rotor sink terms are subtracted from the momentum sources by ANSYS

Fluent UDF (User Defined Function)

Located on a complex terrain

in moderate mountainous

area of southwest China

Altitude range: 2400~2749 m

above the sea

Number of turbines: 33

Rated Power: 1.5 MW

Rotor diameter: 82 m

Hub height: 65 m

Rated wind speed: 10.5 m/s

Contour map of the wind farm

Wind energy distribution is concentrated (around 7 m/s)

Turbulence intensity is small

Wind resources meet the requirement of exploitation

Complex terrain area

Doppler Wind LiDAR Equipment

PERFORMANCES

Measurement range 80m ~ 4000m

Laser wavelength 1550nm, Eye safety Class 1M, Compliant with IEC 60825-1

Data update rate Max 4Hz (configurable)

Wind speed range 0-70m/s

Wind speed accuracy ≤0.1m/s (radial velocity); ≤0.2m/s (wind profile)

Wind direction accuracy <3°(wind speed > 2m/s)

Radial range resolution 30m/Configurable, no less than 160 layers

Scanning servo accuracy ±0.1°

Scanning features PPI: 0~360° RHI: 0~180° Pointing accuracy: 0.1° Max scanning speed: 55°(Configurable) Scanning mode: PPI/RHI/DBS

Phase I: comparison and

calibration of LiDAR and the

meteorological tower.

Phase II: power curve

measurements on different

manufacturer turbines

respectively.

Phase III (On going): wake

characteristics measurements

and wake optimization.

Front LiDAR Distance: 3D in prevailing wind direction

Rear LiDAR Distance: 4D

A small rectangle region which is 3

kilometers long, 2 kilometers wide

#18 and #20 (same altitude)

Quadrilateral mesh of the ground

surface:5 m by 5 m

First layer height of 5 meters and 10

layers in height direction with 1.2

growth ratio

Shear Stress Transport (SST k-ω)

turbulence model

Pressure-outlet

Symmetry conditions at sides and

top

No slip condition with the

roughness height of 0.2 m at the

bottom

Neutral Atmosphere Condition

ABL Height = 500m

The inlet velocity, which is

assumed to be a logarithmic

profile, was iteratively adjusted to

match this target speed

An average wind speed in the period of one hour

during a relatively steady wind flow was

selected from the massive measurement

database of the front lidar

Local velocity and pressure distribution in complex terrain highly depend

upon the terrain features

The wake in complex terrain changed alone with the flow direction affected

by mountains downstream.

The wake tended to go around the mountain and pass through the valley

between two mountains.

CFD method was able to predict the wake flow direction along the terrain

change.

The possible reason of such difference between calculation and

measurement (error is around 12°) might be that the terrain model in

CFD was not enough accurate and lost some details of the ground.

168

°

Front LiDAR Rear LiDAR

Highlight climate effects is difficult to define: orography, wakes, turbulence,

wind speed, atmospheric stability.

Wind flow is extremely turbulent and results are recorded by means of time

averaging method.

The measurement results are carefully selected from a relatively steady

wind flow.

Improved AD slightly over-predicted the velocity deficit after the rotor, but

the shape of the profile was very similar with the measurement results

The negative slope of the velocity profile at the front lidar location was

different from the commonly known logarithmic profile. This is due to the

location of the lidar and specific terrain shape. In this case, the front lidar

was placed at the upwind side of the mountain, where the up-coming

wind will be accelerated near the ground.

The improved AD slightly over-predicted the velocity deficit after the

rotor but captured the velocity profile much better comparing to the

classic AD.

Moreover the terrain will change the wake developing direction which

makes the behaviour of wake more complex.

Generally, the improved AD model is a powerful and high-efficient tool

for investigating the global wind farm aerodynamic behavior.

Questions? Dr. Xin Liu, Deputy Manager

Email: xin-liu@foxmail.com

Acknowledgments

This work was supported by China Huaneng Group Science and

Technology Fund (HNKJ15-H17).