Exploring wind farm reliability

Wind farms have become emblematic of a shift toward renewable energy, capturing headlines worldwide for their graceful turbines and capacity to produce low-carbon electricity on land or offshore. Yet behind every spinning rotor lies an intricate web of components whose reliability can make the difference between cost-effective, green energy and frustrating downtime or maintenance backlogs. In a world increasingly shaped by climate change imperatives, attention to the reliability and fault modes of these wind assets has grown ever more urgent. Industry practitioners, energy analysts, and policymakers alike want to know how these systems fail, what drives downtime, and how best to manage or prevent disruptions. With wind energy now a key factor in global power generation, the subtle interplay of turbines, transformers, cables, and circuit breakers cannot be overlooked if sustainable power is to be delivered at scale.

Recent years have witnessed a sizable expansion in the offshore wind sector, with capacity and turbine dimensions leaping forward. But with growth comes new challenges, from higher mechanical loads in extreme ocean environments to the complex engineering required for HVDC or HVAC connections. Reliability stands as a key success factor, impacting both the immediate costs of unscheduled maintenance and the long-term perception of wind power as a dependable energy resource. Unreliable equipment can translate into a major drop in availability and revenue. On the flip side, overly rigid reliability demands can inflate capital expenses, so there is a fine balance to be struck. The key question: which components drive the most frequent or severe failures, and how can we glean enough data to take preemptive action? This has led to an increased reliance on operational data from tens of thousands of turbines worldwide.

Because so many factors influence failures—from material selection and weather to maintenance practices—engineers and analysts turn to statistical and reliability-based frameworks. Failure rates become a yardstick for how often components fail in a given time frame, while downtime measures how long the system is out of service each time it fails. In typical reliability engineering, time to failure often follows an exponential distribution, or in aging scenarios, a Weibull distribution. The assumptions simplify calculations and let experts approximate how many failures might happen per year or how many hours might be lost to downtime. Of course, these remain approximations, reliant on the existence of large databases or engineering experience. In some subfields, data is scarce, making it difficult to derive robust rates. But fortunately, multiple data collections have emerged internationally, generating what can be called a mosaic of reliability insights.

Within wind turbines, the usual suspects for failures are well known. Gearboxes, generators, electric converters, and hydraulic systems account for a significant portion of downtime. But do the exact ranks and shares of failures remain constant across Germany, Denmark, Sweden, the United States, or China? The data suggests not. Even within a single country, different turbine models and installations of different vintages can exhibit varying reliability patterns. The same variability applies to offshore sites, where the rigors of a marine environment can shift the top causes of disruption—particularly around cable connections and corrosion. By aggregating results from WMEP in Germany, LWK in Schleswig-Holstein, the Windstats newsletters, Swedish Vindstat data, and large multinational analyses like ReliaWind or CREW, it becomes possible to see that while some patterns stay consistent—electrical components tend to fail frequently, gearboxes cause large chunks of downtime—there is also no universal formula. Surprises lurk in each region’s or dataset’s breakdown.

For example, according to the German WMEP data from 1989 to 2006, it became clear that gearboxes were a major downtime driver, while the electrical system saw numerous minor but frequent fixes. Over in Sweden, Vindstat also highlighted high failure rates in electrical subsystems. Another source, the CREW database in the United States, covering 3000 turbines from 2011 to 2015, reached comparable conclusions about top drivers of downtime, though the grouping or categorization might differ. The Chinese Wind Energy Association studied 1311 to 640 turbines from 2010 to 2012, likewise finding converter issues among the top concerns. Sometimes small differences appear—some older turbines might have mechanical pitch systems, or else direct-drive technology might skip the gearbox—but in general, the big pictures converge enough to let reliability engineers identify critical subassemblies that consistently appear in the top slots.

While the turbine itself has gained the most research attention, wind farms also include transformers, cables, and circuit breakers whose performance can hamper or enable the entire system’s output. Transformers can fail due to bushing issues, tap-changer defects, or coil winding breakdowns. Databases from Japan, Brazil, the UK, and the CIGRE global surveys provide a glimpse into how transformers fare around the world, with average failure rates hovering around 0.09% to 0.66% depending on voltage level, design, and region. The presence of mechanical relay deterioration also shows up in many records, pointing to the risk of mechanical subcomponents. Meanwhile, cables can be AC or DC, land or submarine, extruded or fluid-filled. In marine settings, external damage from anchors or trawlers plays a major role, but internal faults from manufacturing or installation issues matter as well. CIGRE’s Working Groups have compiled data from 59 utilities in 19 countries, revealing that older self-contained oil-filled cables often fail at higher rates than modern XLPE extruded cables. DC cables, especially new extruded types, sometimes show lower failure rates, though sample sizes remain smaller.

Circuit breakers, too, are far from trivial, with nearly 27 utilities from 13 countries analyzing their vulnerabilities. Key trouble spots include auxiliary relays, coil issues, mechanical linkages, or density monitoring in SF6 breakers. Because circuit breakers see less failure data in many sites—there just aren’t as many installed as turbines or cables—some global surveys cannot produce a single aggregated failure rate. They do, however, highlight the most frequent failure points within the device. The main objective is to ensure minimal unplanned outages or at least reduce the severity of any forced downtime. In sum, a wind farm’s reliability cannot be fully understood by only focusing on the turbine. Each link in the chain must be accounted for, or the entire system can become bottlenecked by an external substation or cable interruption.

Because of the complexity, many reliability experts employ Fault Tree Analysis (FTA). This structured top-down approach identifies how individual failures in components combine to produce an undesirable event, such as a total wind farm outage. FTA clarifies whether the system is functionally “series” or “parallel,” how shared cause failures might propagate, and which subcomponents are the prime culprits. By systematically listing subassemblies as basic events, connecting them with AND/OR gates, and then analyzing minimal cut sets, reliability engineers can see which combos of failures lead to a top-level outage. It also helps highlight events that deserve the most stringent monitoring or redundancy solutions. FTA’s broad usage in the nuclear, aerospace, and automotive fields attests to its power, though it simplifies real systems by treating many events as binary.

In a wind farm context, an FTA can branch at the top to see major families of failures: wind turbines themselves, substation equipment including large transformers, cables (AC or DC) spanning from offshore to onshore, and circuit breakers. Each branch can subdivide further. The turbine FTA might branch into gearbox faults, generator faults, control system faults, pitch/hydraulic system faults, rotor or blade damage, yaw system faults, or power converter malfunctions. Each of these subsystems can spawn subtrees: the converter might fail from IGBT module issues, capacitor failures, terminal problems, or shorted PCBs. The gearbox might fail from lubrication exceptions, gear cracks, bearing wear, or alignment problems. The transformer can fail from bushing or winding breakdown, tank leakage, or a tap-changer fiasco. And so on.

This hierarchical approach fosters systematic thinking. Instead of a single monstrous tangle of basic events, each subsystem can be reasoned about. The data from reliability databases, gleaned from actual operational records, is then integrated. For instance, if each IGBT module has a certain exponential failure rate and each turbine has a certain number of modules, the converter sub-tree can produce a predicted converter failure rate. The same logic applies for the sub-assemblies in the turbine’s mechanical and electrical aspects. Summing up or combining these events via AND/OR gates yields the top event: total turbine failure. Then that can feed into the wind farm-level OR gate, signifying that one or more turbine or cable or substation or circuit breaker failure can reduce or stop the farm’s output.

Though the methodology works elegantly on paper, real data issues complicate matters. Many reliability surveys only gather high-level information or present partial sets that do not map perfectly onto a single taxonomy. Some data sets track failures monthly but do not specify which subcomponent is at fault. Others might separate “electrical system” in ways that lump converter, generator, and auxiliary controls together, making it tricky to incorporate into a refined fault tree. Also, uncertain independence assumptions, plus the possibility of common-cause events like severe storms affecting multiple turbines at once, can hamper the precision of FTA results. Another challenge is that new technology, from advanced converters to direct drive turbines or novel cable insulation, might not have enough track record to produce robust failure statistics. Despite these challenges, FTA remains a valuable tool for wind farm engineers. By focusing on probabilities, it can highlight the largest reliability risks so that owners and operators can allocate maintenance resources effectively. Suppose the analysis shows that gearbox downtime accounts for a disproportionate share of lost production. In that case, it might be wise to conduct more frequent inspections, adopt condition monitoring sensors, or design a quicker replacement protocol. If the biggest risk resides in substation transformers, then ensuring spare units or near-site maintenance becomes a priority. Over time, as more data accumulates, the fault tree can become more refined, with each parameter updated to reflect actual field performance.

This study’s aggregated perspective reveals that across wind turbines, certain subassemblies stand out: electrical components frequently fail but are quick to fix, while mechanical subassemblies like gearboxes, main bearings, or generators create extended downtimes. Substations, in turn, revolve around the reliability of large transformers, while cables face either internal or external damage. Circuit breakers seldom fail but can cause significant disruption if they do. Different surveys identify different ranks or exact numeric rates, but the consistency of these top failure areas is striking. Meanwhile, an FTA at the farm level can help unify these disparate insights.

A short numerical example can be performed to illustrate how this might work. If each wind turbine has an approximate average failure rate of 1–2 events per year, with each downtime of 10–15 hours, but cables have only 0.02 events per circuit-kilometer-year, one might guess the turbine is more likely to fail more frequently, yet the cable, if it fails, has a bigger production impact. Indeed, that cable might cause an entire cluster of turbines to lose their route to the substation. The FTA, combining the cables in an OR gate with the other substation gear, might reveal that while cable failures are rarer, the downtime can last weeks if it requires an expensive specialized vessel for submarine repairs. The results point to a stark difference between frequent small disruptions from turbines and infrequent but massive disruptions from grid interface equipment.

Because offshore wind is increasingly important, cables and transformers become more central to reliability planning. Storms, wave action, and salt corrosion pose extra hazards. HVDC solutions can help reduce reactive losses, but the converter stations become new reliability hotspots, given the presence of large, complex, full-scale converters and DC lines. Survey data, while still limited, indicates that HVDC lines can be quite robust if installed carefully, but the converter itself might bump up the failure rates. Operators who have historically run AC substations find themselves contending with new phenomena like DC cable partial discharge or converter control bugs. All these realities feed into the FTA, which in turn helps operators weigh various configurations and O&M strategies.

The big takeaway is that reliability in wind power is a multi-layered puzzle, with turbines, cables, transformers, and switchgear each presenting its own set of vulnerabilities. Tools like FTA bring clarity, letting us see how an entire farm might fail from a combination of smaller events. But these tools are only as good as the underlying data. The best solutions rely on regularly updated reliability surveys that track new technologies and operating environments. As wind power penetrates deeper into global grids and new offshore expansions hit the horizon, these reliability frameworks become indispensable. The synergy of data-driven insights, carefully structured fault trees, and thoughtful design improvements can help ensure that wind energy remains not just a symbol of a green future, but a truly consistent and dependable source of power.

Along these lines, the ongoing shift to bigger turbines, further offshore, and new HVDC architectures will demand further expansions in reliability data. The industry must share knowledge more freely, acknowledging that the collective learning curve can yield cost savings and better availability for all. New approaches, from advanced sensor-based condition monitoring to AI-driven predictive maintenance, also promise to refine the data inputs to reliability modeling. In short, wind farm reliability is a living research field, increasingly important as the globe transitions to sustainable energy. By continuing to study, compare, and refine the reliability metrics across subassemblies, wind farm developers can more confidently invest in the long-term operation of these assets. The reliability-centered insight gleaned from fault tree analyses and the aggregated knowledge from global databases ensure that as the scale and complexity of wind farms grow, our ability to keep them running reliably grows too.

Subject of Research: Reliability of Wind Energy Systems and Components
Article Title : Exploring Wind Farm Reliability: Key Concepts, Databases and Fault Trees
News Publication Date : April 2025
Article Doi References : 10.1016/j.rser.2024.115227
Image Credits : Scienmag
Keywords : Wind Farm, Wind Turbine, Reliability, Transformer, HVDC/HVAC Systems, Fault Tree Analysis, Renewable Energy, Downtime, Failure Rate, Power System