As renewable energy advances, improving the efficiency of existing wind assets has become crucial. Turbine control upgrades offer a cost-effective way to boost responsiveness, increase energy yield, and extend turbine life, particularly for older models. This case study highlights how a control system upgrade improved the yaw responsiveness of the Senvion MM92/2050 turbine fleet in Portugal, leading to significant performance gains. Using Windfit's high-frequency monitoring, the client captured detailed data to validate the upgrade’s impact, paving the way for further optimisations across their wind assets
Challenge: Enhancing Turbine Responsiveness to Wind Direction
Maximizing the energy yield from their Portuguese wind farm was a priority for a client, who identified an opportunity to enhance the responsiveness of their Senvion MM92/2050 turbines to shifts in wind direction. Among them, turbine WTG 13 had shown the lowest reactivity to wind changes, directly impacting power production. To address this, the client decided to trial a control system upgrade to improve the turbine’s yaw response. Given that these aging models often lack high-frequency (HF) data or sufficient sensors, our role as a monitoring solution becomes crucial.
The Windfit Approach
To ensure accurate measurement of the upgrade’s impact, the client installed Windfit systems on four turbines in the wind farm several months before the upgrade, enabling detailed performance data collection. The control system upgrade was applied exclusively to WTG 13, allowing for a direct performance comparison with the other monitored turbines. Windfit’s high-frequency data and advanced monitoring capabilities reinforced the insufficient SCADA data enabling comprehensive tracking of crucial metrics like yaw movement, alignment, vibration levels, and power output pre- and post-upgrade.
Real Results, Real Impact
Enhanced Yaw Responsiveness: WTG 13 adjusted to wind changes more frequently, with smaller, more precise yaw movements, enhancing overall efficiency.
Increased Power Output: Power curve analysis, adjusted for air density, showed a 1.2% increase in power output for WTG 13.
Consistent Performance Across Metrics: Cross-comparisons with other turbines validated the 1.2% AEP (Annual Energy Production) gain, confirming the upgrade’s positive impact.
Scaling Success to Other Farms
Satisfied with these results, the client extended the upgrade across four additional wind farms to adapt all turbines to local conditions. Each site was equipped with Windfit to monitor and verify the upgrade’s effectiveness over time. This control was necessary to ensure that the turbine control upgrade was adapted to the local wind conditions of each wind farm. The results from these trials mirrored the initial success, with each turbine demonstrating improved yaw responsiveness, reduced dynamic yaw error, and an average AEP increase of 1.2%.
Conclusion: Driving Efficiency Through Data-Driven Upgrades
This case study underscores the substantial value of integrating Windfit’s independent, high-frequency data monitoring into the assessment and validation of turbine control upgrades. With Windfit’s granular data, the client not only confirmed the effectiveness of the upgrade in enhancing yaw responsiveness but also demonstrated a significant increase in energy output. This data-driven approach enabled the client to make informed, performance-based decisions, ultimately leading to efficiency gains across their wind portfolio.
The success of this strategic upgrade exemplifies how targeted interventions, supported by reliable data, can maximize energy potential, improve long-term performance, and support sustainable growth in wind energy.
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