Failures of wind turbine components can be expensive both in terms of the repair itself but also long lead times where the turbine is inoperative and losing revenue. If the deteriorating health of a component is known before a failure occurs, then a replacement can be ordered well in advance of the repair thus preventing unnecessary downtime. Preventative repair also avoids the risk of a failure damaging other parts the turbine. SCADA-based Condition Monitoring (SCM) uses data already collected at the turbines to detect deteriorating component health and identify the increased risks of failures. Results are proving to be valuable to owners and operators.
DNV GL’s SCM service applies a physics-of-failure methodology to monitor component health using the known relationships between data channels. Following the creation of this service and positive feedback from customers, DNV GL has developed it further into a fully functional and automatic online customer-ready SCM service. The SCM system automatically applies analysis algorithms to live data from operational wind farms. A website allows customers to log in and review SCM health reports starting at a high-level dashboard and drilling down to detailed in-depth analysis of specific components. Additionally, a framework has been developed where new SCM algorithms will be added as part of continuous research efforts.
For wind farm owners and operators who need higher wind farm availability and lower maintenance costs, SCM is an analytical tool that can monitor the health of turbine components and predict impending failures allowing for preventative maintenance to be scheduled, avoiding long periods of unexpected downtime and high repair costs. SCM is unlike traditional condition monitoring as it uses existing data collected in the wind farm SCADA system meaning that it does not require expensive retrofits of additional sensors or equipment.