AIST FREA

産総研トップへ

Wind Power Team

Advanced Technology for Wind Power Generation

Over View

In order to accelerate the introduction of wind power generation and achieve grid parity it is necessary to reduce the cost of wind power generation. This requires the continuous development of the hardware aspect of wind turbine technology, and the software aspect of technologies for the selection of suitable sites and management of wind farms.


Research Target

The team aims to establish elemental technologies for a high-performance wind turbine and its control strategies, and wind assessment technologies for the site selection and operation of wind farms, in cooperation with the domestic wind power industry.
These technologies will help reduce the cost of wind power generation, stimulate the domestic market, and improve the international competitiveness of the wind power industry in Japan.
The team has set the following goals:


  • 1. Improvement of the power output by 5% or more and the lifetime of wind turbines by 5‒10% or more by developing elemental technologies for improving the performance of wind turbines and entire wind farms.
  • 2. Advancement of assessment technologies for accurate wind measurements with errors of less than ±5% in annual wind speed and the reduction of assessment costs by 20‒30%.
Line-of-sight wind speed measured by a nacelle-mounted LIDAR installed on a research wind turbine at an upwind distance of 75‒300 m in front of the turbine.

Line-of-sight wind speed measured by a nacelle-mounted LIDAR installed on a research wind turbine at an upwind distance of 75‒300 m in front of the turbine.

* LIDAR: Light Detection and Ranging
(Device for remotely measuring wind speed and direction by laser light)


Research Outline

1. Elemental technology for a high-performance wind turbine

The team has demonstrated a prototype of a nacelle-mounted LIDAR as a new technology for measuring the wind speed and direction upwind of the turbine rotor plane. LIDAR measurement flow and turbine feed-forward control can be combined to optimize the turbine cyclic blade pitch and rotor speed to alleviate fatigue effects on the tower, gear and blades coming from wind gusts and wind shear during daily operation. By adopting the fast feed-forward strategy, the lifetime of the wind turbine can be prolonged and the power performance is improved.

Numerical meteorological model

Numerical meteorological model

2. Offshore wind resource assessment using a numerical meteorological model and satellite remote sensing

To select a site and design a wind farm, the wind speed and direction are the key factors. Wind measurement using a meteorological mast is very expensive, especially at an offshore site. To accurately measure the wind speed and direction, new technologies using a numerical meteorological model and satellite remote sensing have been developed. These technologies are also expected to help reduce the measurement costs.

Wind resource assessment using satellite remote sensing
Wind resource assessment using satellite remote sensing

Main Research Facilities

Prototype of the nacelle-mounted LIDAR Research wind turbine
Komaihaltec Inc. KWT300
Prototype of the nacelle-mounted LIDAR Research wind turbine Komaihaltec Inc. KWT300
The forward-looking wind LIDAR measures the approaching inflow in front of the research wind turbine with nine sampling directions. Rated power output: 300 kW, rotor diameter: 33 m, hub height: 41.5 m.
The wind turbine is designed to withstand severe climate conditions in Japan (e.g. a highly turbulent flow from the complex terrain) through the collaborative research of AIST and Komaihaltec Inc.
Ground-based LIDAR Satellite and meteorological data processing system Search device for acoustic sources
Ground-based LIDAR Satellite and meteorological data processing system Search device for acoustic sources
The LIDAR system measures the wind speed and direction remotely at the height of 50‒200 m above ground. The system provides about 1 petabyte of storage for large-scale satellite and meteorological data, and it also processes the data. The system surveys acoustic sources using 30 acoustic microphones and transducers.

Activities and Achievements

1)Field demonstration results of the nacelle-mounted LIDAR【Fig. 1】

The team succeeded in remotely measuring the wind speed distribution on the upstream side of a wind turbine using a high-performance nacelle-mounted LIDAR. The team found that the wind power could be increased by up to about 6% by reducing the appearance frequency of yaw misalignment larger than ±10° based on the information about the wind direction in front of the wind turbine obtained with the nacelle-mounted LIDAR.

【Fig. 1】Histogram of yaw misalignment (error in the wind turbine direction against the inflow wind direction)
【Fig. 1】Histogram of yaw misalignment (error in the wind turbine direction against the inflow wind direction)

2) Advanced assessment technique (Numerical meteorological model)【Fig. 2】

The team developed a simulation environment for improving the spatial resolution of the numerical meteorological model by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data obtained from the Ministry of Economy, Trade, and Industry. The team also developed the high-resolution sea-surface temperature dataset Modis-based Sea Surface Temperature (MOSST) (Shimada et al., 2015), thus significantly improving the reproducibility of atmospheric stability near the sea surface.


【Fig. 2】Comparison between various sea-surface temperature datasets and measured data (Osaka Bay)
【Fig. 2】Comparison between various sea-surface temperature datasets and measured data (Osaka Bay)

3) Advanced assessment technology (Satellite remote sensing)【Fig. 3】

The team developed a method for retrieving sea-surface wind speed by using a satelliteborne synthetic aperture radar (SAR) in consideration of atmospheric stability. Moreover, it was clarified that the relationship between fetch and retrieval errors during offshore winds is remarkably different from that during onshore winds due to the land effect.

【Fig. 3】Difference between measured value from an ocean observation tower (1 km offshore) and retrieved SAR wind speed (Hiratsuka)

【Fig. 3】Difference between measured value from an ocean observation tower (1 km offshore) and retrieved SAR wind speed (Hiratsuka)

Team Member

Title Name
Leader Tetsuya Kogaki
Researcher Susumu Shimada
Researcher Hirokazu Kawabata