The wind turbine farms are using IoT-driven predictive maintenance so that it can efficiently conduct a preliminary damage assessment.
FREMONT, CA: Predictive maintenance and IoT are the most important IT solutions necessary for the renewable energy sector as it has already offered various advantages. Here are ways in which predictive maintenance will be beneficial for wind energy.
Predictive Maintenance for Wind Farms: Common Use Cases
- Most of the wind farms operate by utilizing the SCADA data available from remote monitoring and management. It is possible to allow predictive maintenance to use the data sources, but there are two important restrictions.
- Operators have the ability to monitor a restricted amount of sensor data that is collected from the wind turbines. Failure is a common factor that can generate from the earlier unknown and untracked sources, due to which it can become difficult to identify and mitigate the actual cause of the failure remotely.
Incomplete visibility in the developing downtime can reduce the effectiveness of SCADA-based predictive maintenance. It is one of the primary reasons which the operators are actively trying to use IoT-driven wind turbine predictive maintenance.
Improved Maintenance Planning and On-Site Visits
The wind turbine farms are prone to natural hazards like extreme icing, earthquakes, lightning, and many more. Ensuing unplanned maintenance can stop operations for a long time because the technicians have to run diagnostics so that they can understand the impact.
IoT sensors can efficiently replace humans by conducting preliminary damage assessments and make on-site visits more productive. For example, if lightning strikes a turbine, the sensor can capture the data about the current that has passed through the blade. It can also evaluate the changes in performance and pass the data in real-time to the control system to alert the team.
It will become easy for the maintenance team to know exactly which site they n only stop have to visit, the impact the strike has caused, and which blade needs diagnosis. While visiting, the team will have to stop near the affected wind turbine instead of checking each one, which will reduce downtime.
The machine learning algorithms can also compare the present performance of the turbines with the previous records and scan through it so that it can estimate the current situation of the blades. It can also recommend the technicians on whether it requires immediate inspection or maintenance can be postponed. Such data will help to increase the efficiency of the maintenance schedule and enhance on-site planning visits.
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