The AI-driven industrial analytics can help the wind farms decrease spare parts expense by increasing asset life.
FREMONT, CA: Operation and maintenance can be related to up to 30 percent of the Levelized cost per kWh produced over a turbine's lifetime.
Unplanned downtime of a wind turbine will last a week or more each year and considerably longer in some instances. Since wind turbines are generally built-in remote areas, transporting heavy replacements is also costly. In the case of a generator, it can take more time for transport than for actual repair. About 70 percent of wind turbine downtime is projected to be due to significant maintenance. The high O&M expense for operators is an advantage for incremental revenue generated, which drives the interest in IIoT Predictive Maintenance.
The Limitations of Current Maintenance Techniques
The default system used today in Wind Farms is Predictive Maintenance (PdM) using SCADA data. It is a monitor-based sensor data from turbines. Operators are notified if manually set control limits are violated.
There are two significant drawbacks. Firstly, it is only possible to track only a small amount of sensor data. The root cause of computer failure is from unknown sources, in some instances. With conventional PdM, unless one of the sensors selected has triggered the root cause, it will not be identified.
The second restriction is based on how the SCADA data is tracked. Alerts are created if control levels are breached. However, in certain situations, it is already too late to stop system failure once the control level has been broken. The lack of insight into changing downtime decreases the productivity of PdM.
What is the Economic Benefit of IIoT Predictive Maintenance?
IIoT Predictive Maintenance's objective is to include asset degradation and emerging fault warnings early enough to avoid unscheduled downtime. When operators are notified to malfunction, parts are ordered, they may minimize workloads. Reactive repair, on the other hand, is both costly and more time-consuming.
Lower O&M expenses and higher yield rates are the ultimate objectives of IIoT Predictive Maintenance. Here are some examples. For Germany, the UK, and Denmark, the approximate O&M costs are between EUR 1.2 c and EUR 1.5 c per kWh of wind power generated. Around 50 percent of this is expected to be assigned to insurance, operating costs, and other overhead expenses. One can experience a reduction in replacement and labor expenses utilizing IIoT Predictive Maintenance.