Wind Farms : the Challenges of their Efficiency Assessment and Maintenance

A Wind Powered World

It is no longer possible to speak of the energy transition without mentioning wind power. As a large-scale, decarbonised energy source, it has experienced spectacular growth over the past few decades. Global installed wind power capacity surpassed the historic milestone of 1299 GW by the end of 2025 (1). In that year alone, the industry recorded a record-breaking 165 GW worldwide, representing a 40% increase compared to the previous year (1).

A total of 138 countries have now integrated this electricity into their grids to stabilise their energy costs and reduce their carbon footprint. This development is also partially explained by the increasing size of new wind turbines, designed for greater efficiency. We are seeing a shift from 5-6 MW turbines from a decade ago to commercial models that can exceed 20 MW (2), enabling the development of increasingly productive wind farms. However, this rapid expansion presents a new challenge: optimising the long-term management of complex assets to maximise operational and operational efficiency. We will first examine the challenges around the performance assessment of a wind farm, and then analyse different inspection and maintenance methods of wind turbine blades. We will thus see how longevity and smart maintenance are now becoming key drivers of the performance, reliability, and economic viability of modern wind farms.

Performance Evaluation

The first obstacle to optimising a wind farm lies in the difficulty of continuously assessing its actual performance. In theory, the principle is simple: the stronger the wind blows, the more electricity the wind turbine is supposed to generate, according to a precise theoretical model (3)(4). In practice, the actual operating environment is chaotic and unpredictable. Performance evaluation suffers from a high level of uncertainty, primarily due to the stochastic nature of the wind and the complexity of aerodynamic interactions between the machines. Quantifying production variances in a commercial wind farm remains a significant challenge due to the lack of universal and robust verification methods. To measure wind resources and orient the turbines, standard onboard technologies are installed directly on the structures, at the rear of the rotors. However, this configuration creates a major bias: the sensors are located at the heart of massive aerodynamic disturbances generated by the very movement of the blades (5). There, the airflows are turbulent and distorted, which structurally skews the collected data.

To bypass this problem and estimate the overall performance of a site, engineers rely heavily on mathematical models to simulate airflows. Yet, these theoretical projections tend to drift over the years (6). Changes in the local microclimate, wake interactions between rows and the mechanical ageing of the equipment cause the initial models to lose their original accuracy. Operators then find themselves facing statistical blind spots.

Other techniques seek to measure the wind upstream, independently of the turbine, using ground-based infrastructure or large meteorological measurement masts. While these approaches provide more reliable data, they encounter major economic and geographical barriers (6). The spatial variability of the wind is such that an accurate measurement taken at a fixed point is often only usable for one or two adjacent turbines. Multiplying these physical measurement infrastructures proves financially prohibitive over the entire lifespan of a project, extremely complex from a logistical standpoint, and intrusive to the environment, particularly for offshore wind farms where anchoring fixed measurement structures is an engineering challenge in itself.

In addition, with only a few data collection points, it is difficult to isolate the source of the highlighted issue and properly understand whether it comes from a machine imperfection, wind instability or imprecise measurement.

Without reliable indicators to guide decisions, infrastructure management becomes reactive rather than preventive. This blind management directly amplifies a second major challenge: the maintenance of the equipment.

Wind Farms' Health and Maintenance

The second obstacle is not one single component, but relates to the structural and mechanical integrity of the turbine as a whole: the maintenance of the system plays a crucial role in ensuring the efficiency and lifespan of the wind turbine. A modern wind turbine is a highly complex industrial infrastructure, housing critical mechanical and electrical components within its nacelle, such as the gearbox, generator, yaw systems, and main bearings. These massive parts, as well as the composite blades that are subjected to the shear forces of the wind, are continuously exposed to extreme dynamic loads and severe environmental stresses: sudden temperature variations, lightning, humidity, and salt corrosion. This environment then drives the need for rigorous and continuous maintenance, because even minor external constraints eventually accelerate the degradation of internal moving parts. 

A prime example of this vulnerability is the gearbox, whose failures are the leading cause of wind turbine downtime (7), driven by silent micro-mechanical degradations like gear scuffing or bearing overheating (8). To eliminate this major vulnerability at sea, the offshore industry is increasingly turning to Direct Drive technology (9), a gearless system where the generator is driven directly by the low-speed rotation of the rotor, thereby removing the high-maintenance gearbox entirely. 

However, while removing the gearbox reduces maintenance, it replaces one challenge with another: these gearless generators require massive amounts of copper and rare earths, increasing environmental and geopolitical dependencies during manufacture (9).

As shown by this specific case, maintaining these assets represents a significant logistical and financial challenge that weighs heavily on the profitability of the sector. Operations and maintenance costs end up absorbing between 20% and 35% of the levelised cost of energy over the useful life of a turbine (10).

The limit of traditional maintenance strategies is primarily its intermittent or reactive nature. The industry relies heavily on scheduled physical inspections, conducted either by technicians checking the internal systems within the nacelle, or technicians examining by rope access the external condition of the structures (11). This traditional approach is not only slow and costly, but it also carries inherent risks to the safety of operators working in isolated environments or at great heights (12). Furthermore, these ad-hoc interventions fail to detect internal micro-failures before they cause a widespread breakdown.

When a major mechanical anomaly or structural degradation is identified only at the time of failure, the financial impact is multiplied. There is a ratio of 1 to 15 between preventive action and curative repair (13). For example, early intervention on a bearing or an initial crack costs approximately $30,000 on a land-based turbine, while a major failure requiring the replacement of a heavy component or the entire rotor increases the bill to $500,000 on land (13), and frequently exceeds one million dollars at sea.

Next-Generation Diagnostics

Recognition of these challenges has already prompted early efforts to adapt through emerging solutions. For example, SkyVisor, Perceptual Robotics, and SkySpecs offer automated drone inspections, combined with asset management software. This allows for the detection of blade defects in mere minutes and the consequent optimisation of maintenance plans. In addition, other companies like Aerones deploy climbing robots to physically repair leading-edge erosion on site. Moreover, digital twins are used by GreenWITS to simulate turbine fatigue. Finally, the DLR's drones fleet manages to map 3D wind turbulence, maximising global power output.

All these innovations highlight the sector’s commitment to transitioning towards smarter operations and actively reviewing maintenance and efficiency.

Conclusion

The ambition to build a low-carbon future has made wind power a cornerstone of the global energy transition, and this rapid growth has brought the sector to a stage of maturity where long-term operational efficiency is now the absolute priority.

Optimising wind turbine efficiency remains a challenge: on the one hand, significant data gaps caused by complex aerodynamic interactions that distort and complicate performance monitoring; on the other hand, the burden of exorbitant costs and the inertia of reactive maintenance, which directly threaten its sustainability.

Ultimately, the future of wind energy will not rest solely on the manufacture of larger blades, but also on mastering their long-term management. To continue reconciling ambitious climate targets with operational realities, a profound transformation of the sector’s practices is essential. The wind energy sector must transition from reactive management to proactive defect detection and maintenance. This shift is achieved by combining major technological advances, like drone-based inspections, with cutting-edge predictive analytics such as improved wind turbulence modeling. This ensures the economic viability and sustainability of wind energy for future generations.

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1-   GLOBAL WIND REPORT: https://www.gwec.net/reports/globalwindreport

2-   Advancing Offshore Wind Capacity Through Turbine Size Scaling: https://www.mdpi.com/1996-1073/19/7/1625

3-   L'énergie éolienne, du point de vue de la physique: https://www.refletsdelaphysique.fr/articles/refdp/pdf/2024/01/refdp202477p67.pdf

4-   Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements: https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1050342/full

5-   Effect of turbine nacelle and tower on the near wake of a utility-scale wind turbine: https://arxiv.org/pdf/1903.03167

6-   Planning and Development of Wind Farms: Wind Resource Assessment and Siting: https://backend.orbit.dtu.dk/ws/portalfiles/portal/7901631/Ris_I_3272_ed.2_EN_.pdf

7-   Statistics Show Bearing Problems Cause the Majority of Wind Turbine Gearbox Failures: https://www.energy.gov/cmei/systems/articles/statistics-show-bearing-problems-cause-majority-wind-turbine-gearbox-failures

8-   Gearbox Reliability Collaborative Gearbox 1 Failure Analysis Report: https://digital.library.unt.edu/ark:/67531/metadc830838/m2/1/high_res_d/1036039.pdf

9-   A prospective life cycle assessment of drivetrain technologies in offshore wind: https://publications.tno.nl/publication/34645360/ArVNdO51/Dighe-2025-Prospective.pdf

10-   6 Biggest Challenges of Wind Turbine Maintenance: https://worktrek.com/blog/wind-turbine-maintenance-challenges/

11-   Wind Turbine Maintenance: Reduce Downtime, Boost Output: https://safetyculture.com/topics/wind-turbine-maintenance

12-   Occupational safety and health in the wind energy sector: https://research.unl.pt/ws/portalfiles/portal/5518661/OSH_in_Wind_energy_sector.pdf

13-                   https://onyxinsight.com/resources-support/articles/cut-turbine-maintenance-costs-90-percent/

a- https://genwind.fr/?page_id=112

b-https://www.energierecrute.com/actualites-energie-environnement/7142-le-metier-de-technicien-de-maintenance-eolien-un-pilier-de-la-transition-energetique

c-https://www.resco.net/blog/the-turbulent-business-of-wind-turbine-maintenance/

d-https://www.abot.fr/secteurs/energie-industrie/