In a wind farm running hundreds of wind turbines, the hydraulic systems play an important part in running the turbine smoothly. Hydraulic systems optimize the turbine by controlling the blade pitch to maximize the amount of power generated under unpredictable wind conditions. Performance takes a hit, when there’s a sudden drop in pressure. In the worst case scenario, a complete loss in pressure can destroy components, resulting in significant financial losses.
Behind the scenes of these wind farms is the field monitoring equipment (manned by operators looking at every crevice of the turbine). Data sets from sensors report on the operating status of individual turbine units and control points in the system. These control points vary in their oil level, voltage, humidity, bearing, coupling, particle counters, accelerometers, gearbox, and more.
So, when an issue occurs, the field operators will have to look at the Operational and Maintenance schedules, standard operating procedures, filed monitoring data, and parts reference data to come up with a cause and a solution.
A drop in hydraulic pressure can be caused by several factors, and the fault may not always point to the hydraulic systems. To isolate this issue, field operators will need to bring down the turbine, send out the field personnel with test equipment, and check with the hydraulic system’s supplier for a solution. This could mean days if not weeks before a resolution is found.
End-to-end operational analytics
With all the data sets out there, wind farms can gain real-time visibility into operations and component-level behavior through the analysis of streaming data captured by the electro-mechanical heavy equipment, sensors, and controllers.
Wind farms can identify specific units and/or components, which could need further inspection in the future and can completely eliminate unplanned activities between scheduled maintenance processes.
Let’s ask ourselves – Would it not be great if these activities could occur:
- Specific units and/or components that could need further inspection in the future and potential maintenance could get triggered as alerts from an asset knowledgebase?
- Wind farms were equipped with a solution to predict the supplier’s (in this case, hydraulic systems’) performance over the life-cycle of all the installed turbines?
- Wind farms are in a position to procure only the most efficient configurations of the hydraulic system that impact production the least?
- We can quantify how a component’s performance is impacting the financial performance on a daily basis?
Brand new approach
This highly fragmented data requires complex analysis and correlation across the different types of datasets
The aim is to augment intuitive decision making with statistically-based technical support and operating procedures. Combine that with visualization that is so richly detailed as to provide a full 360 degree view of all the assets in wind farm.
Glassbeam’s Hyper scale IoT platform SCALAR can ingest structured, unstructured or multi-structured data from any type of machine and prepare it for analysis. Combined with our machine learning / Predictive analytics layer built on top of Apache Spark and you have a fast, scalable analytics solution for processing large-scale IoT machine data at your fingertips. With our rich set of APIs and an easy to use drag and drop type of dashboard builder, creating custom solutions has never been this easy.
The future prism
An IoT analytics platform for the Wind Energy industry can perform the following tasks:
- Trigger the related events that deviate from domain-specific machine learning models’ outcomes
- Grab part-level performance metrics and predict trends based on pre-determined baseline data
- Set up watch lists for configuration changes and correlate that with the supplier’s part performance statistics
- Measure the status of the components’ attributes and visually correlate over the asset base and their lifecycle
- Support out-of-the-box reporting to procure replacement parts or schedule maintenance activities based on prescriptive analytics
- Provide cognitive intelligence to front-line operators, analysts, and business planners for guidance and advice, ahead of impending machine/component failures
- Ensure maintenance contract negotiations are based on the prescriptive data of the lifespan of a part/system
Building a wind energy operation analytics platform requires a flexible and adaptable technology framework and Glassbeam’s next generation platform is ideally suited for this purpose. Our RESOURCES PAGE has numerous case studies, white papers and analyst reports that articulate this capability in more detail.