ERG and Onyx Insight Make It Possible to Analyze the European Wind Portfolio In-House
ERG, an Italian clean energy provider, has agreed to a five-year agreement with ONYX Insight to leverage their cloud-based advanced analytics fleetMONITOR for the improvement of component timeframes and save operations and maintenance-related costs.
The software has been installed on over 250 wind generators in Italy, Germany, as well as France, with the goal of assisting ERG in optimizing fleet operations.
According to ERG, the power producer has leveraged the software’s data analysis to completely change data acquisition from its historical condition tracking system in order to improve in-house examinations.
Reports have also stated how the energy producer has enabled ERG to properly self-monitor the status of its turbine across its mixed fleet while only using a singular platform.
ONYX Insight, a big data, and engineering company has provided ERG with assistance in the configuration and deployment of the fleetMONITOR technology and is continuing to help with in-depth coaching.
ONYX Insight’s European sales director, Sven Thiesen, stated that the company is looking forward to assisting more wind farm operators throughout Europe in extracting additional value from their assets while optimizing their operations.
The software subscription model is rapidly transforming the face of wind energy O&M, therefore independent service providers must remain adaptable in order to satisfy customer expectations.
Europe’s wind market is growing more diverse, with mixed-asset portfolios incorporating numerous turbine models of varying ages. According to ONYX, this has put established O&M techniques to the test.
The usage of old CMS, which offers insufficient data and ineffective failure detection, has further complicated matters for wind farm management. This is especially true when they have plans of expanding and scaling up their predictive maintenance programs.
According to ONYX, good analytics necessitates high-quality information and malfunction detection in order to offer the most accurate findings, which are frequently lacking in antiquated CMS.