Can you trust your wind analytics provider?
Can You Trust Your Wind Analytics Provider?
Not all wind analytics deliver equally reliable results. The quality of an analytic depends on the data it uses, the domain expertise behind its development, and how thoroughly it has been validated under real operating conditions.
This case study compares two approaches to detecting yaw misalignment: OpenOA, an open-source method using 10-minute SCADA data, and Wind PulseSense, which combines high-frequency SCADA data with physics-based analytics and wind turbine domain expertise.
Testing the Analytics Against Field Evidence
The two methods were evaluated across three field-validation scenarios:
Four turbines with known LiDAR measurements and no yaw adjustments
Three turbines with multiple known yaw adjustments and corresponding LiDAR measurements
A 78-turbine wind plant with known yaw adjustments but no LiDAR measurements
Detailed results are available in the full case study.
How to Evaluate a Wind Analytics Provider
Before acting on analytic recommendations, owners and operators should ask:
How were the algorithms developed?
Does the development team combine wind turbine and data science expertise?
Have the analytics been validated against independent measurements?
How many turbines and operating scenarios were used for validation?
Are there documented examples of successful corrective action?
Can the provider explain how the results should be interpreted?
Incorrect yaw recommendations can reduce energy production and increase turbine loads. Reliable analytics must therefore do more than identify an apparent anomaly—they must provide field-validated insights that operators can confidently act upon.
Wind PulseSense combines high-frequency operational data, wind turbine domain expertise, and extensive field validation to accurately detect yaw misalignment and identify opportunities to improve wind farm output.
Download the complete case study to review the methodology and detailed results.
