Wind PulseSense
uncover wind farm performance losses hidden inside averaged data
Wind PulseSense uses high-resolution and averaged turbine data to detect turbine underperformance, diagnose operational inefficiencies, and recommend corrective actions that increase wind farm output.
Wind farms lose energy through small, persistent inefficiencies
Wind turbines can underperform for many reasons: yaw misalignment, control issues, curtailment behavior, sensor drift, software issues, and operating conditions that are not visible in standard reporting.
Many performance platforms rely primarily on 10-minute SCADA data. That level of data is useful for portfolio reporting and broad performance analysis, but it can miss important operating behavior that occurs between averages.
The result is that many wind farms leave recoverable production on the table.
Beyond
10-minute SCADA analytics
Wind PulseSense is designed to extract value from both high-resolution data collected every second and 10-minute average SCADA data. This higher-resolution view helps reveal short-duration events, control behavior, yaw-related losses, and other operating patterns that can be smoothed out in conventional 10-minute data.
By combining high-resolution analysis with wind-specific domain expertise, Wind PulseSense helps operators understand not only which turbines are underperforming, but why they are underperforming and what can be done about it.
Performance issues we help identify
Yaw and Pitch misalignment
Detect pitch and yaw–related performance losses with greater precision than conventional averaged‑data approaches, including subtle misalignment, rotor imbalance, and pitch control issues.
Power curve deviations
Identify turbines operating below expected performance across the power curve by tracking changes in key operating parameters.
Control and rated power related inefficiencies
Detect operating behavior that may indicate sub‑optimal control settings, rated‑power limitations, or unstable responses, using high‑frequency signals.
Curtailment, derating and availability patterns
Understand when lost output is related to farm-level curtailment, turbine derating, or frequent shutdown events to support commercial and operational decisions.
Thermal health and loading indicators
Monitor generator, gearbox, and main‑bearing temperatures to detect abnormal thermal loading early, enabling proactive maintenance and reduced risk of major failures.
Fleet-level benchmarking performance variation
Compare turbines, sites, and operating regimes using peer-based analytics across major operating parameters to prioritize the most impactful performance improvements.
Designed to find what conventional analytics can miss
Common performance analytics
Wind PulseSense
Primarily based on 10-minute averages
Uses high-resolution data captured every few seconds and 10-minute averages
Identifies broad underperformance
Diagnoses specific operating inefficiencies
Provides dashboards and alerts
Provides corrective recommendations
Often requires manual interpretation
Prioritizes actionable improvement opportunities
Focuses on reporting
Focuses on increasing output
Increase output from existing wind assets
Recover lost production
Identify and correct performance losses that have been hidden.
Prioritize high-value actions
Focus engineering and operations teams on the issues most likely to improve production.
Improve yaw alignment
Detect and address yaw-related losses that are difficult to identify accurately with averaged data alone.
Confirm improvements
Confirm if the implemented corrections have addressed the identified problem.