Evaluating wind plant performance: Key metrics for success

In the dynamic landscape of renewable energy, the operational efficiency and the performance of wind plants are paramount factors in ensuring sustainable energy generation. As stakeholders and industry experts navigate the intricacies of wind energy, the need for precise metrics to evaluate wind plant performance becomes increasingly evident.

This article delves into the specific key metrics that drive success in the evaluation of wind plant performance. By focusing on these metrics, industry professionals can make data-driven decisions to enhance the reliability, efficiency, and overall success of wind energy projects.

Actual production (energy yield)

Actual production (energy yield) is a direct measure of the total energy output of a wind plant over a specific period. Consistent monitoring and analysis of actual production is crucial for identifying underperforming turbines and optimizing overall plant efficiency.

Production SCADA

Represents the electricity production data as monitored and recorded by Supervisory Control and Data Acquisition (SCADA) systems. SCADA systems are crucial for data acquisition, real-time monitoring, and control in power plants.

Production grid

This corresponds to the actual amount of electricity injected into the electrical grid and delivered to
end-users. It accounts for losses (also called “energy balance” or “energy discrepancy”) incurred during the transmission and distribution of electricity. It is important to note that such energy losses are due to several factors, including resistance in power lines, transformer losses, and other inefficiencies in the grid infrastructure. These losses are a natural part of the electricity delivery process and are typically expressed as a percentage of the total electricity generated.

In GPM Horizon, users can see an overview of their production (SCADA, Grid, and fluctuation between them) and the revenues generated based on market prices.

Production SCADA, production grid, and revenue information

Capacity factor

Capacity Factor represents the ratio of the power plant’s actual energy output to its maximum potential output if it were continuously operating at full capacity.

Capacity factor = (Actual energy produced / Maximum potential energy generation) × 100

A low-capacity factor may result from factors such as maintenance downtime, variations in availability or operational issues.


A wind turbine with 2MW installed capacity could theoretically produce per year:

2 MW * 24hrs * 365 days = 2 MW * 8’760hrs = 17’520 MWh

But let us say the turbine produced only 6’500 MWh in that year.

The resulting capacity factor in this case would be: 6’500 MWh/ 17’520 MWh = 0.371 = 37.1%

Full load hours

Full load hours are calculated by determining the total number of hours the power plant operates at its maximum rated capacity.

FLH = (Actual production for a period / Rated power)

A high FLH value indicates that the power plant consistently operates at or near its maximum capacity, showing reliability and stability in electricity production. Power plants with a high FLH are considered more efficient and economically viable, as they maximize the utilization of their installed capacity.


A wind turbine with 2 MW installed capacity produces 6’500 MWh/ year.

The FLH is then calculated as: 6’500 MWh / 2MW = 3’250 hrs.

It’s easy to see that the capacity factor and the full load hours go hand in hand. The higher the capacity factor, the higher the FLH, and vice versa.


Efficiency is calculated as a percentage of wind energy that is converted into electricity under given wind conditions. Here, we apply the actual wind data to a power curve (provided by OEM or historically calculated), to determine if the asset is operating efficiently.

Efficiency = (Actual energy produced / Theoretical production) × 100


Reliability metrics, including the mean time between failures (MTBF) and mean time to repair (MTTR), provide insights into the robustness of the turbines. A high MTBF and a low MTTR are indicative of reliable and easily maintainable turbine systems.

GPM Horizon offers comprehensive dashboards to analyze the KPIs over selected time periods, and to benchmark assets to quickly identify the best and worst performers.

Benchmark any KPIs in GPM Horizon
Benchmark any KPIs in GPM Horizon


Availability is the percentage of time that a wind turbine is available to generate electricity. A high availability indicates that a wind turbine is not out of service due to maintenance or other issues. 

Availability provides insights into how consistently an asset is operational and is available to generate power. It is a critical factor in ensuring a reliable and efficient energy supply. 

Time-based availability and energy-based availability are two distinct approaches to measure the operational performance of a power plant, to provide insights into its reliability and efficiency. 

Time-based availability 

Time-based availability (TBA) is a metric that assesses the operational status of a power plant over a specified period. It is a measure of the percentage of time the plant is available to produce electricity. 

TBA = (Actual operational time / Total time) × 100 

Time-based availability focuses on the temporal aspect of plant performance, indicating the percentage of time a plant is available for operation. It is essential for assessing the plant’s ability to meet demand, consistently. 

Energy-based availability 

Energy-based availability (EBA) considers the energy output. It considers the actual energy produced in relation to the maximum potential energy that could be generated. 

EBA = (Actual energy produced / Maximum possible energy generation) × 100 

By considering both time and energy aspects, stakeholders can assess not only how often the plant is operational, but also how effectively it utilizes its capacity during operational periods. 

GPM Horizon provides complete flexibility to create availability calculations. Users can employ their own formulas to calculate contractual and custom-defined availabilities, define them at turbine, park- and portfolio-levels, set targets, and have as many availabilities (TBA and EBA) as needed to perform their analysis and reporting. 

Tracking contractual availability over the entire year
Editing contractual availability formula
Editing contractual availability formula
Adding new custom availability formula
Adding new custom availability formula


Downtime refers to the periods when the power plant is not operational due to planned maintenance, unplanned outages, and other issues.

Maximizing availability while minimizing downtime is critical for ensuring a reliable and consistent power supply. Promptly monitoring and addressing downtime issues can have a significant impact on the overall performance of the wind plant.

GPM Horizon offers a robust classification of downtime and underperformance based on the IEC TS 61400-26 standard. The logic automatically assigns Time Classes (T-Classes) to every second of turbine operation, by analyzing both efficiency and turbine alarms. Users can also amend the results if needed and provide additional information from the field for comprehensive reporting.

All relevant information with T-Class classification
All relevant information with T-Class classification
Waterfall losses chart: displaying all losses by category
Waterfall losses chart: displaying all losses by category
Logbook daily view: T-Classes displayed and editing capabilities
Logbook daily view: T-Classes displayed and editing capabilities

Outage factor

The outage factor (OF) measures the impact of outages on a power plant’s operational performance. It quantifies the percentage of time that a power plant is unavailable or non-operational due to planned or unplanned outages during a specific period. The OF is crucial for assessing the reliability, maintenance effectiveness, and overall operational efficiency of a power generation facility.  

OF = (Total outage time / Total time) x 100 

Planned outages are scheduled maintenance periods during which the power plant is intentionally taken offline for inspections, repairs, or upgrades. 

Unplanned outages result from unexpected events such as equipment failures, malfunctions, or other operational issues. 

In GPM Horizon, users can benchmark the outage factor within their portfolio and clearly understand the main causes of relevant outages. 

Benchmarking Outage Factor within the portfolio
Benchmarking Outage Factor within the portfolio
Dashboard analyzing alarms for a given period
Dashboard analyzing alarms for a given period

Wind data

There are several important variables to monitor when working with the actual wind data. Among other things, wind performance curves (power curves), wind speed, and wind distribution are discussed.

GPM Horizon offers multiple visualizations, including wind rose, plotting power curves, scatter plots with colored points which are classified by the relevant status of the turbine, etc.

Power curve analysis with filters
Power curve analysis with filters
Wind distribution chart
Wind distribution chart

Tons of CO2 avoided

This metric represents the reduction in CO2 emissions that would have occurred if the electricity generated by wind turbines had been produced from conventional fossil fuel sources. This metric is often calculated by first estimating the amount of electrical power produced by the wind turbines and then comparing it to the amount of electricity that, in the specific country where the renewable assets are located, would have been generated by fossil fuels.

The difference in emissions between the two scenarios represents the tons of CO2 avoided. By tracking and reporting on this metric, wind plant operators and owners can determine the environmental advantages of their projects and support larger efforts to address climate change.

C02 avoided among KPIs list
C02 avoided among KPIs list

Join us on this exciting journey!

As the global push for renewable energy intensifies, the evaluation of wind plant performance becomes increasingly vital.  Continuous monitoring, data analysis, and advancements in technology will play crucial roles in optimizing wind plant efficiency and contributing to a more sustainable future. By focusing on the key metrics, stakeholders can drive innovation, improve reliability, and further accelerate the transition towards a cleaner and a more resilient energy infrastructure.

As we conclude this exploration of key metrics for wind plant performance, the takeaway is clear: precision in evaluation begets triumph in execution!

The winds of success, harnessed through data-driven decisions and strategic optimizations, carry us forward into a future where wind energy stands not only as a symbol of sustainability but also as a testament to the relentless pursuit of excellence in performance.

Article by :
Sucharita Banerjee, Alexey Bakulin, Dirk Oehlmann and Francisco Laucirica.

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