Denmark

Denmark

MHI Vestas unveils data and performance tools

DENMARK: MHI Vestas has launched a range of "smart" software packages to help wind farm operators increase performance, enhance turbine monitoring and real-time decision-making.

The new products will help customers with designing and operating projects (pic: MHI Vestas Offshore Wind)
The new products will help customers with designing and operating projects (pic: MHI Vestas Offshore Wind)

The new product suite will be used to help customers during the design phase of projects, as well as with carrying out operations and maintenance (O&M), MHI Vestas said.

The portfolio consists of four products. The first will perform intergrated load design simluations on turbine foundations to optimise design. 

A second comprises "dampers", which work individually or together to reduce dynamic movements of the turbine tower.

This should reduce fatigue and design loads. MHI Vestas believes it could also extend the use of monopile foundations to more challenging sites.

MHI Vestas has also launched a performance monitor for mobile devices. It is designed to feed real-time information on turbine performance, site production, and other operational metrics such as wind speed and direction, component temperature and pitch angle to handheld devices.

Finally, the offshore wind joint venture has launched a fast data sensor solution. It claimed this will send and receive data frequency 600 times faster than traditional SCADA (supervisory control and data acquisition) methods.

This should enhance diagnostic capability and optimise scheduled service, the manufacturer said.

High-frequency data can also be stored and analysed without affecting performance or communication with other data and control systems, according to MHI Vestas.

MHI Vestas’ head of product management, Henrik Bæk Jørgensen, previously told Windpower Monthly the fast data solution was capable of collecting data from approximately 1,000 sensors in a turbine "between one and 50 times per second", whereas traditional SCADA methods had "ten-minute average values to predict failures and to improve turbine performance".

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