Introduction
Kavaken's platform specializes in leveraging advanced analytics, including machine learning and artificial intelligence, to optimize the operational efficiency of wind turbines. This case study highlights how Kavaken recognized inefficiencies in sector management. As a result, the warnings in the application prevented possible structural damage and increased the production of the turbines.
Client
Our client operates a geographically challenging wind farm equipped with turbines employing sector management strategies. Prior to implementing Kavaken's solution, the client faced challenges related to inaccurate sector cut-out applications leading to unnecessary production losses and potential long-term structural damage to turbines.
Challenges
Sector management is applied to prevent damage to turbines. The wind direction angles at which the turbine will be stopped are determined after detailed studies before the power plant is put into operation. Our customer's wind turbines experienced problems due to incorrect setting of the sector cut-outs.
After the power plant starts operation, it can easily be seen from Scada that the turbine has stopped due to sector management. However, confirming that these postures are applied at the correct angles and noticing when there is a wrong application is a very troublesome and difficult task.
If the turbine stops 10 degrees early, this means it starts working again 10 degrees early. Therefore, when turbulent wind comes, it continues to work even though it should stop. This may cause the turbine to experience alignment and similar structural problems. Our customer's challenges highlighted the critical need for Kavaken's comprehensive and data-driven approach to wind turbine management.
Kavaken's Solution
Our machine learning algorithms detected unexpected amount of losses during periods of sector cut-out application.
Our product's algorithm swiftly identified the issue, raising a warning of excessive sector cut-outs. It pinpointed sensor misalignment as a root cause and recommended specific action: recalibration with a focus on Northing alignment. This targeted approach ensured that the root cause was addressed effectively, enabling the client to rectify the problem and optimize turbine performance.
Action
Following the root causes and recommended actions provided by Kavaken's application, our customer quickly conducted a sensor recalibration. The service team confirmed a 10-degree difference between neighboring turbines, validating the algorithm's findings. This recalibration effectively addressed the sensor misalignment issue.
As a result, unnecessary sector cut-outs was prevented, ensuring uninterrupted turbine operation and maximizing energy production. Additionally, costly structural damage that could have occurred as a result of this situation was prevented. Subsequently, Kavaken's product algorithms confirmed that the issue had been successfully resolved
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Results
The impact of Kavaken’s insights was significant, preventing an unnecessary production loss of annually. Additionally, a remarkable 0.7% increase in production per turbine was achieved, highlighting the tangible benefits of resolving the sensor misalignment issue.
This case underscores Kavaken's commitment to optimizing wind turbine operations and maximizing asset performance. Through proactive identification and resolution of operational challenges, Kavaken empowers clients like the utility company to achieve higher availability, increased efficiency, and sustainable green energy production.
