Wind Farm Performance
Clear, reliable, actionable
Performance is about monitoring operation, knowing when something changes, understanding why it changed, and acting with confidence. Reliable performance work separates external effects from turbine behaviour so decisions lead to measurable improvement.
Wind farm performance covers how assets produce energy over time, how losses evolve and how turbines respond to changing conditions. It becomes critical when production moves, expectations are not met or optimisation decisions must be made across turbines and months.
Why performance is so often misunderstood
Performance signals are influenced by operation, environment and measurement. Without structure, changes are easy to see but hard to interpret.
SCADA signals are designed for control and operation. Without filtering and structure, they mix operating states and hide real performance shifts.
- • SCADA is noisy, unstable and not designed for performance analysis
- • Short-term variations look like issues but are just conditions
- • Simple KPIs react to noise rather than causes
Wind measurements are not calibrated and evolve with rotor effects, configuration and environment, making raw comparisons unreliable.
- • Rotor effects bias the measured wind speed
- • Identical turbines can report different winds under the same conditions
- • Bias evolves with configuration, transfert functions and hardware changes
Direction, turbulence, density and wakes redefine what “normal” looks like from one period to another.
- • Wind conditions shift continuously
- • Wake exposure changes with direction
- • Environmental effects can outweigh turbine signals
Monitoring tools are useful for visibility: they show that something changed through trends and KPIs. Performance analysis explains what changed, why it changed and whether it matters, using signals that are comparable and interpretable rather than raw, mixed operation data.
What “decision-grade” performance looks like
Performance becomes actionable when results can be compared, explained and verified.
Performance must be computed from stable, comparable samples to avoid misleading conclusions.
- • Clear separation of operating states
- • Comparable conditions across time and turbines
- • Explicit confidence and coverage indicators
A deviation is useful only if you understand why it happens.
- • Issue-specific detection, not generic thresholds
- • Cross-checks across signals and models
- • Traceability across power-curve regions
After a change, gains must be confirmed and shown to persist.
- • Before/after comparison with normalisation
- • Impact quantified in performance and AEP
- • Ongoing tracking to confirm persistence
From monitoring to improvement
This is how ExpertWind moves performance from raw signals to validated improvement
Make the data physically consistent and separate operating states. Without this, KPIs are misleading.
- • Remove inconsistent and abnormal points
- • Classify normal, curtailed, stopped, abnormal and transitions
- • Validate pitch, rotor-speed and power coherence
Control what masks behaviour so comparisons become meaningful across turbines and time.
- • External conditions: air density, wind sector, turbulence
- • Operational regimes: limiting behaviour and control envelopes
- • Consistent tracking under comparable conditions
Create baselines for expected turbine behaviour to separate ‘wind’ from ‘turbine’.
- • Wind-speed prediction baselines using turbine and neighbours context
- • Power prediction baselines for expected output
- • Reference pitch and rotor envelopes for control regimes
Stabilise the wind input so performance is not driven by sensor drift or changing measurement bias.
- • Detect and correct sensor drift and faulty wind sensors
- • Capture transfer-function and sector-dependent biases
- • Handle rotor-induced wind-speed distortions
Diagnostics support action by identifying correctable causes and quantifying expected gains.
- • Issue-specific signatures across turbine operation
- • Quantified impact and interpretation
- • Designed for verification after corrective actions
What this enables in practice
A structured performance approach leads to earlier detection, better decisions and measurable improvement.
Some issues do not stand out in standard monitoring because they are masked by operating states and changing conditions.
- • Separate condition-driven variation from turbine-driven deviation
- • See deviations that stay consistent across time and turbines
Stable signals and cross-checks prevent chasing noise and reacting to artefacts.
- • Filter unstable periods and mixed operating states
- • Require agreement across independent indicators
- • Confidence and coverage built into results
Detection is useful only if it comes with interpretation and a way to verify improvements.
- • Issue-specific interpretation, not generic alerts
- • Quantified impact in performance and AEP
- • Clear before/after verification workflow
Rank issues by impact, persistence and fixability, so asset managers spend time where it pays back.
- • Impact-based ranking across turbines and months
- • Distinguish one-off effects from persistent losses
- • Turn results into a clear performance plan
How to work with ExpertWind
Choose the level of depth and involvement that fits your needs, from instant diagnostics to long-term performance improvement.
Turn performance signals into improvement
Whether you need a quick answer or long-term support, we help turn performance data into confident action.