Onshore wind operations and maintenance

With competitive auctions suppressing energy prices in mature onshore wind markets, wind farm owners and operators are under constant pressure to reduce costs and increase revenue. In a mature market, where as much as 349 GW of global onshore wind capacity could be “out-of-warranty” by 2020 [1], benchmarking and the in-depth analysis of asset performance and equipment reliability data are becoming vital tools for reducing OPEX costs and optimising electricity generation.

Benchmarking for onshore wind O&M

Benchmarking performance across energy sectors, particularly within Oil & Gas, is well established. In the onshore wind sector, most benchmarking is undertaken at owner portfolio level, with very little benchmarking across competing portfolio asset managers.

Wind Energy Benchmarking Services (WEBS) is an independent performance benchmarking company. It applies a suite of web-based tools to assist wind farm owners and operators around the world to improve performance and reliability of their operational assets. By understanding where their wind farm rank in comparison to similar projects, both structurally and geographically, wind farm owners can give a meaningful context to maintenance strategies and investment decisions.

The benchmarking is focused on the operational efficiency and effectiveness of onshore wind farm assets. WEBS can benchmark more than 180 performance, availability, reliability and maintenance metrics across a range of dimensions. These include regional geography, turbine type, age and provide a detailed analysis of your current performance against your peers enabling identification for cost optimisation and increased productivity.

The service is available globally and the benchmarks are updated monthly, providing the most comprehensive and up to date view of wind farm operations KPIs on the planet. Key performance indicators (KPIs) associated with availability, performance and reliability are published monthly, allowing wind farm owners to track their performance relative to their competitors using robust data that is constantly updated. Adherence to international standards, both IEC and RDS-PP, ensures the data in WEBS is consistent, validated and verified. Through WEBS, clients are using benchmarks to gain a unique and objective understanding of their ongoing performance against their peers, identifying asset improvement opportunities.

WEBS New Energy Update analysis

In addition to this direct comparison, metrics from the WEBS database provide industry-level reliability data that can inform predictive analytics and help owner/operators to optimise their predictive maintenance strategy.

An analysis of failure rates from the WEBS database provides insight on: system and sub-system failure rates and associated downtime. The analysis can be further segmented by isolating more than two dozen key wind farm parameters such as: age, number of turbines, turbine rated power and turbine model. This granularity provides wind farm owners and operators with the ability to make a like-for-like comparison of their asset’s performance; helping to identify opportunities for value creation and ensuring that the improvement focus is targeted in the right areas.

The 2018 failure rate and performance analysis considers a subset of the available WEBS database consisting of approximately 60 wind farms in Europe. These wind farms have been quality checked for consistency and provide ongoing data, allowing for consistent analysis across populations and over time. The wind farms have been further divided into populations for detailed analysis.

The population break downs are summarised in Table 1. It should be noted, that historically in the onshore wind industry, the definition of a “failure” has captured events requiring at the least, a manual reset and minor repair. However, the results of the 2018 analysis consider any forced outage that results in a stoppage of the wind turbine generator. This includes those where the wind turbine is able to automatically reset the fault and restart without the intervention of a manual reset, either locally or remotely. This allows turbine alarm logs, which report all forced outages to be used in order to apply a more general methodology across the industry.

The consequence of this is that reported failure rates are higher than typically reported when considering only those failures that require repairs or manual intervention on the turbine, and the associated downtime is shorter. However, within the larger population, it is possible to consider subsets where major component repair or replacement takes place, providing further insights into the reliability and risk profile of your wind farm.

Table 1: Definitions of farm filters.

Failure rates and downtimes with age and turbine rated power

Considering the overall failure rate and distribution of failure rates with different turbine age populations shows some variation. At a population level, there is an overall downward trend in the observed number of failures, suggesting newer wind farms are increasingly reliable and benefiting from the maturing nature of the industry and stable wind farm platforms. However, for the most frequent failure systems, the classic bath tub failure rate can be observed with mid-life machines showing the lowest levels of failures.

Fig. 1: Failure rate by age population.

The only subsystem where newer machines are showing equivalent failure rates to old machines is in the control and protection system, suggesting that the increasing complexity of operating new turbines efficiently to maximise energy yield comes at a slight cost. This is further demonstrated by examining the relative contribution to number of outages where control and protection is a significantly larger proportion of the overall failures on new machines. Considering the mid-life population, the yaw subsystem failures are disproportionately high when compared to other turbine populations. However, they have a low associated downtime mitigating the impact of these failures on availability.

Fig. 2: Downtime per forced outage by age population.

Further investigation reveals that this population is heavily influenced by failures on a limited number of turbine technologies and sites, highlighting the importance of being able to benchmark performance against equivalent populations to accurately identify if there is an underlying performance issue or not.

Fig. 3: Availability by age population.

Providing independent benchmarking data and analysis for subscribers and advisory services across the onshore wind sector, while ensuring both data confidentiality and confidence in the quality of the results, is the goal of WEBS. Early adopters are already seeing the benefits through understanding what best in class performance looks like and being able to compare their assets against this.


[1] www.accenture.com/us-en/blogs/blogs-onshore-wind-driving-value-operations

Contact Aleksandra Sledzinska, New Energy Update, aleksandra@newenergyupdate.com


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