How do you measure and improve the performance and reliability of wind turbines?

To understand where problems lie but also to identify where there is potential for improvement, we need data! Comprehensive data grants us the full picture to see exactly which adjustments we can still make.

As all people in this thread are writing performance data from wind turbines are a key driver to improved performance. However one thing owners are often forgetting is to ensure the right to SCADA data and high resolution data from the wind turbines contractually and to store it.

There is no doubt - as Operator and / or Owner of wind turbines you need access to data. Ten years ago this was even difficult - but luckely the times have changed and OEMs, component supplier and Operators have seen the benefit of sharing data. And also the discussion who is owning the data of WTG is widely accepted. The data belongs to the Owner of the Windturbine. These days you need to invest into your data strategy - and "collecting everything for ever" is not a strategy - it's more the absence of strategy. DATA have to be transformed into INFORMATION (Data in /with context) and lnformation must lead to ACTION. There is no (business) case just to collect and store data - beside for the Cloud or Datacenter operator ;-)

Most companies focus on normal operations, in the Lean world that would be normal flow. However, they tend to neglect abnormalities and often pay a heavy price for it. Absolutely, you need data but which ones? The answer for that is provided through comprehensive DFMEA and PFMEA which highlight the abnormalities. That’s where the focus needs to shift. Hence the steps would be to capture data of these abnormal and normal conditions, then baseline their current performance, determine and implement smart solutions and keep monitoring the effectiveness of these improvements.

Data-driven wind turbine management is game-changing. Sensors, SCADA, and ML unlock: Peak efficiency through tailored operation Predictive maintenance for fewer outages Extended lifespan for maximized ROI Agreed, data quality and domain expertise are crucial. Plus, industry collaboration will turbocharge advancements.

From our perspective at Fraunhofer IWES with offshore wind farms, it really helps to directly look into time series approaches. You can do this by AI, but in the end there are usually very practical reasons for anomalies. This might not only be related to the turbine, but also to maintenance strategies, which, if not optimal, might lead to bad performances.

From my expirience high-resolution wind sensors unlock deeper insights in wind turbine analysis. Capture detailed wind speed & direction data to: * Understand: Analyze performance, reliability, & external factors' impact. * Predict: Identify faults & optimize power generation with machine learning plus human expert. * Compare: to WTGs SCADA and wind-mast or LiDARs on-site. This data-driven approach leads to better decision-making & improved wind energy production.

Data analysis can also help to evaluate the effectiveness of maintenance activities on the performance and reliability of wind turbine components. In addition, this step can help in optimizing the performance and efficiency of the components.

In my experience if you start with a clear problem statement you will find the data very easy which is needed to analyse your problem ie how to detect or anticipate a gearbox problem. You will also identify the need for additional sensors - examples are CMS retrofits or Ice detection ,..... The bigger your fleet - the bigger your data samples - the easier it is to find comparable data. Comparabel data or normalized data is important to to do cross fleet analytics. Comparable data leads also to comparable KPIs - I heard about a story years ago when the responsible fleet operators sit together in a monthly jour fixe with their board member and reported each of them was the monthly Top Performer - based on their individual KPIs .

In my EU wind farm experience, smart data analysis was key. We used machine learning to uncover subtle performance patterns, revealing how microclimates impacted turbine efficiency. A big win? We found peripheral turbines underperforming. Adjusting pitch control based on this insight boosted farm output by 2.5%. Cross-site analysis helped standardize best practices and pinpoint site-specific tweaks. Remember: in wind energy, good analysis doesn't just find problems—it uncovers hidden opportunities. What surprising insights has your wind farm data revealed?

In our EU wind farms, smart visualization was key. Our dashboard became a virtual control room, offering instant insights. A game-changer? Heat maps overlaying performance on terrain data, instantly showing how landscape affected output. Custom KPI visuals combining capacity, availability, and MTBF helped balance performance and maintenance, boosting efficiency by 4%. These visuals weren't just for techs—they helped us communicate complex data to investors and regulators. Good visualization in wind energy doesn't just show data—it tells a story and drives action. How has visualization improved your wind operations?

Maintenance planning must always be followed by regular inspections to identify physical wear, damage to blades, corrosion and other structural problems that may occur before scheduled maintenance dates. Some technologies such as drones with thermal cameras help with visual inspection and detection of anomalies.

Planning wind turbine maintenance activities based on data analysis and visualization enables a proactive and efficient approach to ensuring the reliability and efficiency of wind turbines. To carry out this planning you need: - Identify patterns in operational data to anticipate problems and optimize operations; - Base maintenance scheduling on predictive models to reduce downtime and costs. Schedule maintenance before failures occur based on performance predictions. - Prioritize maintenance activities based on component criticality and probability of failure; - Direct maintenance resources efficiently based on failure predictions and turbine operating conditions; -Maintain a detailed record of all maintenance activities carried out.

Performance improvement or the avoidance of bigger outages should be the aim of your data strategy. when the windturbine is erected the Operator is operating in a box: Lifetime contribution = uptime * available capacity * margin (Income-costs). These are your levels over the lifetime of 25- 35 years - an d of course you need to define every year again how do you optimise the product of this box

When looking to optimize wind turbines you should not only be applying analytics to each wind turbine separately, but in addition you should be doing fleet analysis across the entire wind farm because the turbine operations affect each other so to optimize energy output you will want to optimize the output of the entire farm instead of at individual turbine level.

The current trend is to use bigger wind turbines (18MW or even 22MW) to reach a maximum planting capacity. But to be honest, I am not sure if that is a right way. Bigger turbines means bigger vessels for Transportation and Installation are necessary. That’s exactly the offshore industry is lacking (the official announced reason why ?rsted has to step down from the investment in USA). Some turbine manufacturers are calling for an efficient utilization of the 15MW turbine. From my perspective, it not only lock the capacity of the wind turbine manufacturers to match the global offshore target, but also facilitates the associated value chain to focus on their current portfolio. In a nutshell, the whole industry will reach its maximum efficiency

Optimizing wind turbine performance using data analytics and visualization involves a systematic approach to monitoring, analyzing and continually improving operations. This can be done using sensors installed in each turbine that monitor vibrations, temperature, wind speed and energy production. Weather data is collected from a local weather station; Machine learning algorithms analyze data to predict imminent failures in specific components and recommend maintenance actions. Production data is analyzed to identify efficiency patterns and trends; Real-time dashboards show energy production, maintenance status and weather conditions. Visual indicators alert operators to any anomalies or problems.

There are different methods and indicators when it comes to measuring the performance and reliability of wind turbines. These include: 1. Capacity factor or the ratio of the actual energy out of a wind turbine to its potential energy output over a period of time. 2. Availability or the percentage of time that a wind turbine is able to generate power over a period of time. 3. Failure rate or the number of failures or breakdowns a wind turbine or its components record per unit of time. 4. Down time or the amount of time that a wind turbine does not produce power due to failures or maintenance over a period of time.

It's essential to consider the influence of site-specific factors and environmental conditions on turbine operation. Factors such as terrain, topography, wind shear, turbulence, and climate variations can significantly impact turbine performance and reliability. Therefore, conducting thorough site assessments and micrositing studies prior to turbine installation is crucial for optimizing performance and mitigating potential challenges. Additionally, ongoing monitoring and analysis of site-specific conditions enable adaptive management strategies, allowing operators to adjust turbine operations and maintenance practices to suit prevailing environmental conditions effectively.

Improving wind turbine reliability using data analysis and visualization involves several steps and techniques that help monitor, predict and optimize turbine performance, such as using SCADA systems to collect operational and environmental data from turbines, including wind speed, wind direction, energy production and weather conditions; use machine learning techniques to analyze large volumes of historical data and identify patterns that can predict future failures; create dashboards that display real-time operational data, allowing operators to monitor turbine health and make quick decisions; use insights derived from data analytics to optimize maintenance scheduling, minimizing downtime and maximizing turbine availability.