Goldwind was the first company in China to develop and test a grid-connected prototype, and mass produce wind turbine towers using a hybrid steel and concrete design. This technology offers stable power generation, low power consumption, a low equipment fault rate, low spare part costs, and more environmentally-friendly tower foundations. At present, we have successfully developed hybrid towers of different heights, including 100m, 120m, and 140m. Our first prototype was connected to the Dabancheng grid in June 2013. By the end of 2016, we had connected about 70 commercial turbines to the grid. This product has already received CGC design certification and spare part type certifications. It won second prize at the 2016 Power Construction Science and Technology Progress Awards.
Goldwind was the first company in China to develop and test a grid-connected prototype for two years, and mass produce flexible towers. Its unique speed avoidance control technology and increased tower resistance technology effectively reduce the base fatigue load and maximum load on the tower base. This significantly reduces the weight of the flexible tower compared to pure steel towers. Our first prototype was connected to the Hami grid in February 2015. Our first 32 commercial units stood out from the many products of international competitors. In June 2016, we completed construction and grid connection for a project in Thailand. The 121/2500-120 flexible tower has already received DNV-GL (type A) type certification.
The POWERNESTTM system is a wind farm-level control system that integrates various turbine performance optimization function modules. It provides a series of advanced control technologies, including intelligent yaw wind rectification for optimized unit output, environmental perception and adaptation control capabilities, real-time load detection, service life status estimates, and sector management. This system can improve the average annual generation capacity of a wind farm by 2-5%. As of June 2017, this system had been deployed on projects with a total capacity of over 2 million kW and provided an average generation increase in excess of 2%.
Traditional wind direction and speed measurement instruments are affected by the wake created by blade rotation, which means the measured values and actual values deviate. Our newly-developed front-mounted LiDAR wind measurement technology uses LiDAR units mounted on the turbine to capture the dynamic wind flow in front of the turbine. This information is then input into the turbine's real-time control system for use in advanced control algorithms that optimize the turbine performance, load, and other characteristics. This significantly reduces the unit's load, while improving its generation capacity.
This front-mounted LiDAR unit was developed and tested over two years, giving us significant simulation analysis and field testing experience. It allowed us to make a series of important technical breakthroughs in dynamic environmental adaptation, intelligent optimal gain, extreme gust ride-through, and feedforward control. These breakthroughs laid a solid foundation for the decrease of Goldwind turbine loads and promotion of wind zones.
Blade extension technology uses longer blades to increase the swept area and take full advantage of excessive loads to directly increase power generation. On a Goldwind 1.5MW turbine, this technology increases the annual power generation of the unit by about 5.5%. This technology is widely applicable to projects where the wind resource parameters are less than the turbine design parameters.
By applying Goldwind smart self-testing technology, farm cluster control technology, new sensor technology, and other sensing technologies, we have increased the turbine fault detection rate to over 90% and reduced the critical fault false-positive rate to less than 5%. At the same time, we use the smart sensing data platform's vibration fatigue analysis and load inversion technologies to monitor the turbine damage caused by uneven wind resources in real time. This allows us to predict a unit's fatigue life and dynamically adjust power generation to balance the fatigue life and generation capacity. Ultimately, this improves the overall service life and reliability of the wind farm.
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