Based on EnOS™, the Ensight Wind application adopts data mining and machine learning technologies to help wind power asset owners identify, diagnose, and pre-warn the sub-health status of critical devices, reducing significant risks of failure and maintenance costs, thereby reduces productivity loss due to maintenance of large components. The application helps monitor the power generation performance of wind turbines in real time and provides early warning against anomalies of wind turbines, helping asset owners promote generation efficiency of turbines and avoid power loss.
· Identifies anomalies of critical large components, control systems, and sensors by using the data collected by SCADA, without adding more sensors.
· Supports over 40 types of early warnings and provides possible causes and overhaul suggestions.
· Provides a packaged common technical improvement scheme and integrates voice information for early warning of blade failures.