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Snowtown II wind farm turbines turning big data into smart data

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article image Thanks to Siemens’ remote diagnostic centre, only 15 per cent of the problems require service technicians to go onsite and work on the affected wind turbines

Siemens Ltd is using the data provided by over 800 sensors strategically positioned on the wind turbines at Australia’s newest wind farm in its research to optimise performance and minimise the need for potentially costly maintenance.

Every rotation of the ninety 3.0MW turbines at Snowtown II wind farm is continually monitored offsite to develop key target values that can evaluate potential errors and correct them remotely. Siemens’ work is yet another example of the state-of-the-art technology incorporated into Australia’s latest renewable resource power plant and demonstrates the engineering expertise available in Australia.

According to Engineers Australia Chief Executive Officer Stephen Durkin, it represents the benefits of continually developing the engineering talent available in Australia so they can be part of the exciting work that is going on at a global level.

He added that the work of engineers on projects such as Snowtown II is being showcased and demonstrated at the ongoing Engineers Australia Convention 2014 at the Melbourne Convention Centre this week.

Siemens Ltd CEO Jeff Connolly said the project also represented a graphic illustration of the transformation of big data into smart data and the move towards a digitalised economy in preparation for the fourth industrial revolution.

He explained that the information from Snowtown II is fed to their monitoring centre in Denmark, which collates similar data from Siemens’ wind farms around the globe. The database, which contained 97 terabytes of data till last year is expected to grow to 268TB by 2015.

However, the challenge lies in turning that data into meaningful information or smart data to enhance productivity. He added that the Siemens wind power service centre was an excellent example of digitalisation for efficiency.

The huge amount of data collected from Siemens wind farms is used to remotely monitor tiny variations and identify potential defects long before any service work is required. In some cases, Siemens engineers can detect defective main shaft bearings up to a whole year before they have to be replaced.

Data from the Snowtown II site flows continually to the diagnostic centre of Siemens Wind Power Services in Brande in western Denmark. The facility collects and evaluates all the operating data from more than 7,500 Siemens wind turbines all over the world using the Supervisory Control and Data Acquisition (SCADA) and Turbine Condition Monitoring (TCM) systems from Siemens. While the SCADA system collects the turbines’ electronic and mechanical data as well as information about weather and power grids, TCM is a vibration recognition system.

Each wind turbine nacelle contains up to nine sensors that measure the vibrations of the turbines’ key components such as the transmission case, the generator, and the main shaft bearing at the rotor blades. Each turbine at Snowtown II is equipped with TCM, which monitors it 24 hours a day, 365 days a year.

The collected vibration data is transmitted to a reference database in Brande where it is automatically processed to create sample values for the normal operation of the various types of wind turbines; the target values are continuously compared with the current operating data of active turbines.

The vibration sensors can detect even tiny deviations that indicate a potential defect enabling the system to discover a damaged gear wheel, for example, long before the transmission breaks down. When needed, Siemens engineers can remotely switch off the affected wind turbine and arrange for repair work.

The centre measures more than 2,500 anomalies every week with the 100 analysts at the facility investigating these error messages and transmitting more than 100 early warnings to the service technicians every week. If a case is serious, the technicians go directly to the affected turbine to take care of the matter after the centre has provided them all the information about the turbine and its operating history.

The flow of extremely detailed data is the key to the system’s success because it enables diagnostics experts in Brande to precisely determine what kind of defect they are dealing with and whether or not a service team needs to be sent out to the affected turbine.

For example, if individual turbine components exceed or drop below temperature tolerance ranges, the wind turbine in question automatically shuts down. However, if the off-site technicians come to the conclusion that the anomaly is not serious, they can remotely restart the wind turbine as soon as the temperatures have returned to their normal values.

The performance data from the remote monitoring system allows Siemens’ engineers to remotely solve issues affecting 80 per cent of the stopped turbines within ten minutes. Another five per cent of the problems take somewhat longer to solve, but do not require technicians to be sent to the defective turbines. Only 15 per cent of the cases require service technicians to go onsite and work on the affected wind turbines.

A long-time advocate of smart data, Siemens Wind Power Services became one of the first companies in the world in 1998 to install sensors in its wind turbines as standard procedure.

Work on the $439 million Snowtown II project began in August 2012 with Siemens providing a full turnkey project solution for the wind farm including the associated 275kv high voltage substation. The 90 turbines at Snowtown II represent the largest installation of Siemens’ innovative ‘Direct Drive’ technology commissioned to date. The Direct Drive technology uses half the moving parts of a conventional geared turbine, resulting in reduced complexity and increased reliability.

Officially opened by South Australian Premier Jay Weatherill on November 2, Snowtown II will produce 989 gigawatt hours (GWh) annually, enough to provide clean, emission-free power for 180,000 homes.

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