In wind energy operations, performance is everything, and performance starts with availability. While much attention is given to forecasts and production metrics, it’s equally important to understand the availability of wind turbines and the limitations they face.
This blog post explains the difference between turbine availability and limitations, outlines their causes and consequences, and shows how Enlitia’s Planner module gives asset managers the power to register these variables and improve the accuracy of 10-day-ahead forecasts.
Availability refers to the number of hours a wind turbine is technically capable of producing electricity, divided by the total number of hours in the period. In simpler terms, if a turbine could operate 24 hours in a day but was only online for 20 of them, its availability for that day would be around 83%.
Availability is a key performance indicator (KPI) for asset managers. It reflects the health and readiness of turbines and can be affected by both planned and unplanned events.
Even when a wind turbine is well-designed and properly installed, its availability can drop due to a wide range of internal and external issues. These factors are often unpredictable, making it essential for asset managers to understand, monitor, and register them in real-time. Let’s explore the most common causes of low availability in wind farms:
Despite preventive strategies, turbines are complex electromechanical systems and inevitably face unexpected failures:
These failures require immediate action, often leading to unplanned downtime and forcing the turbine offline, directly impacting availability metrics.
Modern wind turbines rely on a network of sensors (e.g., anemometers, temperature sensors, vibration monitors) and SCADA systems to monitor performance and trigger alarms. However:
These invisible or silent failures can degrade availability, and without accurate logging or validation, they can go unnoticed, corrupting operational data.
Sometimes, a turbine is fully functional, but still not allowed to produce energy. This occurs due to:
In these cases, turbines are technically available, but their output is artificially restricted, often without automatic logging in the SCADA system unless integrated with external signals. If not registered, curtailments can be wrongly interpreted as turbine faults or performance drops.
Wind turbines rely heavily on embedded software and remote control systems that handle:
Bugs, software crashes, firmware mismatches, or logic misconfigurations can lead to:
These issues may not leave physical traces, making it hard for asset managers to diagnose the root cause without access to control system logs.
If these causes of low availability are not tracked and registered accurately, the consequences ripple across the entire operation:
That’s why platforms like Enlitia’s include dedicated tools — like the Planner module — that allow asset managers to explicitly record these events, giving full visibility and enabling smarter, context-aware forecasting.
Limitations in wind turbines refer to any condition that allows the turbine to stay online but restricts it from operating at full capacity. These are often partial power losses rather than complete shutdowns. While availability is a measure of whether the turbine is producing energy at all, limitations describe how much the turbine’s output is being constrained, even when technically available.
Unlike availability issues, which result in downtime, limitations create a performance ceiling — the turbine is running, but not at its optimal production level.
These are among the most frequent and most overlooked causes of underperformance in wind turbines. The turbine stays technically operational, but internal components signal stress, degradation, or risk, prompting the control system to limit output. Examples include:
These limitations are turbine-initiated and automated, designed to preserve component health while keeping the turbine online.
Sometimes, the turbine is forced to limit output due to external commands from the grid operator, even though it’s physically capable of producing more:
These actions are strategic or regulatory, not mechanical, but they still produce underutilisation of available wind resource.
Turbines often self-limit based on environmental thresholds, especially to avoid damage or ensure compliance with site-specific safety rules:
These scenarios reflect intentional, limited operation, rather than full outages.
Limitations, especially internal equipment ones, are often invisible unless explicitly logged or modelled. If left untracked:
That’s why Enlitia’s AI Platform and Planner module allow asset managers to register and categorise limitations clearly, offering not just a binary status (on/off), but a nuanced view of partial restrictions.
Understanding and properly tracking both availability and limitations is not just a technical concern — it directly influences the financial performance, operational strategy, and decision-making accuracy of renewable energy producers. When these metrics are missing or misclassified, their impact cascades through multiple layers of the business. Here are the three most significant consequences:
At the operational level, KPIs like capacity factor, performance ratio, and availability score shape how assets are evaluated and compared. If a turbine experiences downtime or is operating under constraints, but this isn't registered correctly, these metrics become unreliable.
For instance:
When these distortions accumulate, they can affect how assets are prioritised for investment, maintenance, or repowering, and may lead asset managers to take corrective action based on a false signal.
Forecast models are only as accurate as the assumptions behind them, and one of the most critical assumptions is asset availability. If the system assumes turbines will operate at full capacity, but limitations are in place (whether grid-imposed or component-related), the model will consistently overpredict production.
This has several ripple effects:
By integrating availability and limitation data, forecasts can be made not only more accurate, but also more trustworthy, with deviations that are explainable and operationally justified.
Energy markets operate on precision — when wind producers submit bids, they commit to delivering a specific amount of power at a given time. If real-world generation falls short of that bid due to hidden limitations or untracked outages, producers can face imbalance penalties, especially in day-ahead or intraday markets.
Beyond penalties, poor data around availability and limitations:
Ultimately, when limitations and availabilities aren’t visible or accounted for, revenue leakage is not just possible — it’s guaranteed.
Tracking turbine availability and operational limitations is essential — but it’s only valuable if that information becomes actionable. That’s where Enlitia’s AI Platform stands out.
Rather than treating availability and limitations as static, back-office metrics, the platform allows asset managers to actively feed this operational knowledge into the forecasting process, unlocking a new level of control, transparency, and precision.
With Enlitia, asset managers gain the ability to enrich raw turbine data with contextual information, registering expected unavailability periods, performance-limiting conditions, and strategic constraints. This isn’t just about logging downtimes; it’s about giving the platform the same operational awareness that the team on the ground has.
By capturing this type of information in an intuitive interface, asset managers move beyond passive monitoring to active performance shaping — adjusting assumptions in real time, based on what they know is happening (or will happen) in the field.
One of the key differentiators of Enlitia’s platform is that this contextual information directly feeds into the platform’s AI-powered 10-day-ahead power forecast.
Rather than assuming every turbine is operating at full capacity 24/7, the forecast engine dynamically incorporates:
The result? Forecasts that reflect what’s truly possible, not just what historical patterns suggest — reducing overestimation, increasing reliability, and providing a more robust foundation for trading and O&M planning.
Forecasting power is only part of the value. Enlitia’s platform also offers a second layer of insight: revenue forecasting.
Within the same Planner module, asset managers can access a revenue forecast view, which translates power estimates (in MW/MWh) into projected revenues (€), accounting for:
This empowers energy producers to not only understand how much power they’ll generate, but also how much value they can extract from it — a crucial shift for revenue optimisation and market strategy.
Every time a limitation is registered or an availability window is adjusted, Enlitia’s algorithms learn. This closed feedback loop ensures that the power and revenue forecasts aren’t static — they continuously evolve with operational behaviour and improve in accuracy over time.
By aligning human knowledge (asset manager input) with machine intelligence (AI-powered forecasting), Enlitia enables a more responsive, intelligent, and financially aware approach to asset management.
And the best part? All of this is achieved without any additional hardware. Enlitia’s AI Platform is 100% software-based, making it easy to deploy across existing wind assets — no retrofitting, no sensor upgrades, just smarter insights from the data you already have.
All of this is achieved without any additional hardware. Enlitia’s AI Platform is 100% software-based, which means you can start making smarter, more profitable decisions using the data you already have — no need for sensor upgrades, field installations, or infrastructure changes. It's a seamless layer of intelligence built to elevate your wind asset performance.
Whether you’re looking to improve forecast accuracy, reduce revenue volatility, or simply understand what’s holding your turbines back, our team is here to help.
Schedule a Q&A session with us and discover how Enlitia can turn your operational challenges into forecasting and revenue opportunities. We’ll walk you through real use cases, answer your specific questions, and show you how to take full control of your data, without the hardware.
Enlitia is an innovative provider of asset performance management & monitoring solutions, empowering the energy industry to thrive in the digital era. With our pioneering AI Platform, we deliver transformative results, driving growth, performance, and value for our clients. Unlock the power of data with Enlitia today.
