How to Integrate Design and Operations to Guarantee the P50 in Wind Farms

The P50 is one of the most critical metrics in evaluating wind projects. It defines the expected annual production with a 50% probability of being reached or exceeded and, therefore, serves as a reference both for financiers and for operations and maintenance teams.

In theory, it seems simple: if the design is well executed and the turbine operates, the wind farm should reach the P50. In practice, however, many projects fall short of this target. As Anna Rivera Jové, CEO of YouWind, explained: “Uncertainties don’t disappear once the turbines are built. A solid design means reducing those uncertainties as much as possible, so the baseline handed over to operations is realistic.”

The good news is that there are already tools and strategies capable of reducing uncertainties from the design phase through to operations, intelligently connecting data to turn performance into predictability.

What is the P50 and Why is it So Important

In simple terms, it represents the amount of energy the plant has a 50% probability of reaching or exceeding in a year. This number is crucial because it serves as a dividing line between expectation and reality: for banks and investors, it is one of the foundations for defining the financial viability of the project; for O&M teams, it is the daily performance target.

When a wind farm fails to reach the P50, negative impacts of different kinds are triggered. Return on investment is compromised, lenders’ confidence decreases, and the operation is forced to deal with constant pressure to recover losses. In some cases, power supply contracts are broken due to failure to meet production targets, or regulatory issues arise for wind farms that consistently fail to achieve their production goals. That is why, more than a statistical indicator, the P50 is a strategic benchmark that must be treated seriously, from the project design stage all the way to field operations practices.

This perspective is reinforced by research such as Lee & Fields (2020): An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties, which highlights how bias, unaccounted-for losses, and poor uncertainty management can undermine the accuracy of production estimates.

The Role of Design in P50 Reliability

One of the central factors in ensuring that the P50 is realistic lies in the design phase of the wind farm. It is at this stage that decisions are made that will impact the asset throughout its entire lifetime. Factors such as the distance to the grid connection point, turbine placement in relation to topography, and the choice of wake effect model directly influence the estimated energy output, as well as external factors such as exposure to curtailments due to grid limitations.

In Brazil, for example, a project in the Northeast may face external curtailments of up to 5%. If this loss is not considered during planning, the result will be an overestimated P50 that is virtually unattainable during operation.

Furthermore, the way data is used makes all the difference. Modeling that integrates high-quality wind resource data with sensitivity analyses allows developers to test different scenarios and better understand the uncertainties involved. A solid design must also include financial indicators such as CAPEX, OPEX, and LCOE, ensuring that the technical outlook is aligned with the economic sustainability of the project. In this sense, platforms like YouWind demonstrate how robust analyses can reduce uncertainty and deliver a reliable baseline for operations.

The Challenges of Operation: When the P50 Falls Short

Even when the design is well structured, operational reality can be very different. Many wind farms continue to fall below the P50 not because of flaws in the wind resource — an issue that modern modeling tools have largely overcome — but due to losses associated with equipment downtime, adverse environmental conditions, and external dispatch restrictions.

When these factors are not closely monitored, operations quickly turn into a cycle of firefighting, with overworked teams and little financial predictability. Nathianne Andrade, Head of Performance Engineering at Delfos, summarized the challenge: “The problem today isn’t the wind resource anymore — it’s the underestimated losses. Availability, curtailments, and environmental conditions can derail performance if they are not continuously tracked and managed.”

This is the reality for many operations: the data is available, but without the right tools it becomes a maze of information that does not translate into actionable decisions. Operating without clarity on where the biggest losses are is like flying a wind farm blind. This is where digital intelligence applied to monitoring and performance management becomes essential.

Delfos’ Contribution: Turning Data into Strategy

This is exactly where Delfos Energy comes in, offering a monitoring and predictive analysis platform that connects data from multiple sources and transforms them into actionable insights. The company’s value proposition is clear: to enable managers to precisely understand where performance deviations are occurring and to give them time to act before problems escalate into critical failures.

The journey begins with identifying performance gaps, in other words, detecting which categories of loss are deviating from expectations in relation to the P50. The platform then applies artificial intelligence and machine learning to prioritize alarms, filter irrelevant information, and highlight only what really matters for decision-making. With this solid foundation, it becomes possible to build proactive maintenance plans that anticipate risks and avoid reliance on a purely reactive operation.

Among the tools available, the Energy Losses module provides an intuitive visualization of wind farm performance, comparing projected energy with delivered energy and highlighting critical losses in red. The APM (Asset Performance Management) module enables detailed investigation of turbine performance, analyzing the power curve and subsystems such as pitch, generator, and oil, making it easier to detect underperformance. The Failure Prediction module can forecast failures in critical components, such as main bearings, up to eleven months in advance, using machine learning models that compare historical behavior with real-time data. This level of anticipation gives O&M teams the ability to plan maintenance without compromising availability or the financial health of the asset.

Bridging the Gap Between Design and Operations

The main lesson is that achieving the P50 does not depend on a single stage of the wind farm lifecycle, but on the integration between robust design and intelligent operations. A reliable baseline built during development ensures that expectations are realistic and achievable. Predictive monitoring and artificial intelligence applied during operation, in turn, ensure that these goals are not lost to unforeseen failures or delayed decisions.

This integration closes the loop and creates an environment in which financial risks are reduced, asset reliability increases, and investor confidence remains solid. By combining precise modeling with advanced monitoring, uncertainty is transformed into competitive advantage, preparing the wind sector for a more predictable and sustainable future.

Have Your Assets Reached the P50?

Falling short of the P50 should not be seen as an inevitable outcome, but rather as a signal that there are gaps to be corrected in design or operation. With today’s technology, these failures can be minimized, bringing actual production closer to planned expectations. The key is to view the P50 not as a single static number, but as the outcome of monitoring and controlling multiple sources of loss that must be managed through intelligent decision-making at every stage of the project.

In partnership with YouWind, a reference in digital solutions for wind farms, Delfos hosted a webinar showcasing tools for operating onshore wind assets.

🎥 Want to dive into all the insights shared during the event? Click here to access the full recording.

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