Essential KPIs in Wind Asset Management

Reducing unscheduled downtime is the most important aspect of wind asset management. This is because wind turbines are rather complicated to manage. They require constant monitoring, ad-hoc on-site inspections for accurate pictures of their status, and a keen understanding of the interaction between wind flow, the weather, and the assets themselves.

Unscheduled downtime for wind turbines can have a cascading effect to logistics and cost. Wind turbines are more challenging to get to and move parts for than photovoltaic panels. This is even more so for offshore wind turbines. Parts are also harder to acquire at a moment’s notice as they are not heterogeneous and tend to be manufacturer specific.

Here are some KPIs to help build resistance against unscheduled downtime for your onshore or offshore wind turbine portfolio. They are categorised into three sections: performance, reliability, and maintenance. This article will go through several useful KPIs and briefly discuss their respective limitations.


Time-based availability (TBA%)

TBA is the ratio between the total time a wind turbine is operational and the total period of time. Reducing unscheduled downtime results in the increase of this metric. This indicator however does not provide information about the power generation efficiency of a wind farm. TBA does not take into account that the operating time of a wind turbine is influenced by wind conditions.

Energy-based availability (EBA%)

EBA is the ratio between actual and expected energy production. This metric directly provides the power generation efficiency of the wind farm. However, the difficulty lies in the estimation of the expected energy production.


Mean Time To Repair (MTTR)

The average time it takes to restore a wind turbine to operational capacity. The indicator is calculated by dividing the total time of restoration by the number of failures. It is however difficult to use this metric to compare between wind turbines or wind farms. This is due to a lack of a standard definition for wind turbine taxonomy. The reciprocal of this metric is the repair rate.

Mean Time Between Failures (MTBF)

MTBF is a good indicator of the reliability of a wind turbine. It is derived by dividing the total operational hours by the number of failures for the wind turbine. The same comparison limitations due to a standard wind turbine taxonomy definition apply to this indicator as well. The reciprocal of this metric is the failure rate.

Mean Time To Failure (MTTF)

This is similar to MTTR, but measures the average time until a failure that is non-repairable occurs. A non-repairable wind turbine has to be completely replaced as there are no possible maintenance actions that can be implemented to restore operational capacity. The challenge with MTTF as with the other two reliability KPIs is that the metric is only reliable when comparing wind turbines of the exact same build.


Interventions per wind turbine

An intervention is any field work done to maintain operational capacity of a wind turbine. This metric indicates the success of the implemented O&M strategy. Higher wind turbine reliability results in less interventions needed. This metric however does not account for a lot of information, such as the duration of the interventions, and their related costs. There are also issues with comparability between wind farms, but this can be mitigated by normalising the number of interventions by the number of turbines in a farm.

Corrective Maintenance (%)

The ratio between corrective interventions and the total of all interventions expressed in hours. Corrective interventions also tend to be costlier than other interventions. This making up for some limitations of the Interventions per wind turbine metric. It however does not differentiate between immediate and non-immediate corrective intervention activities.

Schedule Compliance (%)

The ratio between scheduled maintenance tasks completed on time and the total number of tasks. It is important to note that the total number of tasks includes unscheduled tasks. This metric is to assess the efficiency and accuracy of maintenance planning and execution. The more unscheduled tasks there are, the smaller the percentage. It does not completely distinguish between scheduled and unscheduled tasks.

Overtime jobs (%)

The ratio between overtime working hours and planned working hours. This is normalised by the working hours per worker and per size of the workforce. It assesses the effectiveness of maintenance planning, worker health, and ideal work force size. It however does not differentiate internal and external man hours.

Total Maintenance Cost vs. Annual Maintenance Budget (%)

This metric is the key definer of the quality of maintenance planning and is the most immediately relevant maintenance indicator to stakeholders. The most desirable outcome is for the total maintenance cost be less than the annual maintenance budget. Most importantly, this metric can be compared across different wind farms. This metric does not break down the ratio of labour to parts cost.

Optimise the management of your wind portfolios

If you were not previously aware of these KPIs, we hope they will help you improve the effectiveness and efficiency of your management of onshore and offshore wind farms.

You can further improve and optimise your asset management process with the use of asset management software such as Arbox Hap. Hap consolidates commercial, technical, and financial asset management into a single platform. The integrated centralised data repository makes monitoring of these interconnected KPIs quick and accessible through automation.

Reference: Gonzalez, E., Nanos, E. M., Seyr, H., Valldecabres, L., Yürüşen, N. Y., Smolka, U., Muskulis, M., & Melero, J. J., (2017). Key performance indicators for wind farm operation and maintenance. Energy Procedia, 137, 559-570. doi: 10.1016/j.egypro.2017.10.385

Learn about Arbox Hap

Contact us today to see how HAP® can help you manage your activities efficiently and boost your bottom line.