Siemens opens new remote diagnostics center for wind turbines in Denmark

Siemens today officially opened the company's new remote diagnostics center for wind turbines, located at the company's global headquarters for wind service in Brande, Denmark. The facility offers some of the most advanced wind turbine remote diagnostic and monitoring capabilities in the world and has been designed to accommodate the continued and rapid growth of Siemens wind service. It currently hosts diagnostic operations and monitoring services for more than 7,500 installed Siemens wind turbines worldwide to proactively keep the units operating at their optimum levels of performance.

More than 130 experts closely monitor the entire Siemens global fleet on a 24/7/365 basis and can detect and often remotely solve potential issues before they occur. This proactive approach can lead to extended service intervals and increased uptime, helping to contribute to lowering the costs associated with wind energy.

Siemens draws from its extensive database and over 15 years of experience. Since 1998, Siemens' remote diagnostics team has been monitoring an ever growing number of wind power plants. Each day over 200 gigabytes of new data is collected from more than 7,500 turbines worldwide. Twenty four million turbine parameters are monitored on the fleet and investigated constantly to provide dynamic optimization of turbine operations with more than 300 million diagnostic calculation results currently being performed every week.

"This new state-of-the-art remote diagnostics center further reinforces our commitment to providing our customers with the very best in remote monitoring and diagnostics," said Tim Holt, CEO of Siemens Service Renewables. "We've been monitoring longer than anyone else in the industry and are leveraging the data into value-added decision-making tools that help our customers operate at maximum efficiency."

Since 1998 the company's expert team in Brande has continuously developed and improved the technology. Automatic Root Cause Identifier is a program that automatically detects and suggests the most likely parameter to cause deviation in turbine systems performance. Automated Surveillance based on Dynamic Linear Models is a system that constantly compares turbine systems performance with learnings and predictions of normal performance. These are just two examples of Siemens patented technologies and are part of Siemens' remote diagnostics portfolio designed to detect deviations, and react fast and proactively in order to avoid downtime and lost production.

Remote diagnostics is an important part of Siemens' portfolio of value-added services. Monitoring experts can solve and remotely fix more than 85 percent of all alarms remotely without a service team doing trouble-shooting onsite. Reduced downtime and fewer turbine visits result in a higher energy output. With vibration diagnostics Siemens can find even the smallest indicator that something may not be operating normally and follow up by recommending proactive solutions before the issue becomes serious.

Furthermore, Siemens can analyze the data collected to draw trends on individual turbines as well as the overall fleet. Experts can look to see how certain locations, operating conditions and length of service are impacting the turbines and make recommendations based on this and other data. The results contribute to enhanced turbine performance over the long term, thereby lowering the cost of energy. "What we are learning and seeing today, combined with historical data from the past, can provide valuable insights and play an important role in our future research and development," added Holt.

In establishing this new, state-of-the-art remote diagnostics center, Siemens has further advanced its monitoring and diagnostic capabilities, bundling expert knowledge in one place. The new building also includes a dedicated customer area where customers can meet with Siemens experts, observe the center's operations firsthand, and work together for a proactive approach to managing service and maintenance.

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