**1. Introduction**

Wind turbine generator and drive train technology has developed rapidly over the last decade as utility-scale wind turbines have increased in size and contribute to a greater share of the electricity market [1–3]. This fundamental shift in the energy mix requires wind turbines to cope with greater flexibility in generation, with wind farms now operating more like traditional power plants to reach increased demand that meets current electricity grid conditions. As the generator and wider drive train configuration has adapted to meet changing grid requirements, wind turbine developers and operators have also been challenged to lower the overall LCOE by reducing the OPEX. The response to this challenge has been to find opportunities to maximise availability, increase system reliability, decrease the cost of repairs, reduce downtime and minimise lost production over the lifetime of a

**Citation:** Turnbull, A.; McKinnon, C.; Carrol, J.; McDonald, A. On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market. *Energies* **2022**, *15*, 3180. https://doi.org/10.3390/en15093180

Academic Editor: Paweł Lig ˛eza

Received: 8 March 2022 Accepted: 21 April 2022 Published: 27 April 2022

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site due to unplanned maintenance activities [4]. In recent years, system reliability issues have also been addressed by simplifying and reducing the number of potential points of failure, with some OEMs taking the strategic decision to remove the gearbox and focus on direct-drive technology.

## *1.1. Problem Statement*

In the offshore environment, where turbines are now increasing to 14 MW rated power, not only is access at a premium, but lost production is also very expensive to operators, with research recently suggesting that O&M could contribute up to 30–40% of the total LCOE [5]. This scenario provides developers with extra motivation to increase overall wind turbine reliability. This has lead some OEMs to focus on eradicating components that have conventionally caused expensive repairs and high amounts of downtime relative to other elements of the drive train. Examples of this behaviour would be in removing the gearbox for a direct-drive machine, or removing the high-speed stage of the gearbox for a medium-speed machine. For wind energy to continue to be financially viable, the wind industry must continue to adapt and improve technology; however, it must also ensure knowledge gained from older systems is understood and transferred where possible to ensure any lessons learned are appropriately recorded and applied.

When a new technology or wind turbine model is deployed, there is little or no operational data and maintenance records to understand reliability at a system-wide level. The transition to larger turbines with direct-drive train technology therefore poses an interesting hurdle to asset owners and operators that are looking to scale and optimise maintenance activities; in the context of wind turbine reliability and condition monitoring, how much insight can be drawn from data gathered on older technology and applied to modern direct-drive machines?

#### *1.2. Motivation, Paper Structure and Novelty*

Literature surrounding wind farm O&M commonly falls into several distinct categories; reliability analysis, performance optimisation, fault diagnostics, failure prognostics and maintenance optimisation. To date, reliability analysis has been used to determine critical components in order to focus efforts on fault detection, failure prediction and maintenance optimisation to minimise both OPEX and lost production, a process which has been identified as a key driver to achieve higher wind farm availability. Papers published to date have proposed a range of fault diagnostic methods primarily focusing on using SCADA data to detect anomalies across the wind turbine gearbox [6–8], blades [9], generator [10,11], pitch [12] and yaw [13] systems and main bearing [14]. Earlier approaches such as [15] used a linear auto-regressive model model to detect generator bearing failure by modelling bearing temperature and [16], which developed higher-order polynomial models of drive train temperatures. More recently, nonlinear auto-regressive neural networks with exogenous inputs (NARX) models have been used in [17,18] to detect gearbox issues. Several review papers [19–22] have also been published over the last several years providing a comprehensive overview. That being said, no study to date has attempted to review and evaluate previous work with an emphasis on highlighting the potential of transfer learning between direct-drive and geared machines.

Section 2 of this paper aims to provide a comprehensive overview of geared and direct-drive train taxonomy, with an emphasis on highlighting the key differences and similarities at assembly and component level that can be used to gather and transfer information. In addition, this section will take a look at real-world SCADA data to evaluate what information is typically gathered across different monitoring systems for each drive train configuration. This initial assessment is a vital step to determine which failure modes are likely to be common across both configurations and what associated monitoring data are readily available.

Using standardised research methods observed in the literature, Section 3 will present new results from a reliability study of 617 wind turbines across Europe and South America with a combined total of 217 operational years. Wind turbine stoppage rates and downtime related to a range of components are assessed, with the study encompassing both directdrive and gear-driven systems all under 3.2 MW rated power. Initial results are then compared to results from other reliability studies found in the literature.

Section 4 builds on previous sections and presents a framework to assess existing literature in the area of fault diagnostics and prognostics. The aim of this review is to provide an overview of existing techniques and case studies that are most applicable to direct-drive machines, making use of existing datasets made up of mainly gear-driven wind turbines. Not only does this allow for some immediate insight into the direct-drive machines, this study also opens up opportunities in areas such as transfer learning and reinforcement learning to adapt models as more data and information are made available for modern, larger, direct-drive machines. Finally, drive train components and failure rates common to both configurations are brought together to assess which components are most critical for future research into direct-drive diagnostics and prognostics. In the context of existing literature in this area, the contribution of this work is to:


#### **2. Drive Train Configuration Trends**

When describing a wind turbine drive train configuration, it is typically expressed as a series of assemblies and components required to convert the kinetic energy in the rotor to electrical energy needed for a stable grid connection. In modern utility-scale wind turbines, there are four major categories as described in [4,23]. Note configuration type A and B from [4] have been excluded due to a focus on current utility-scale technology applicable to the offshore environment that meets modern grid requirements [24,25].

Full details of each configuration type along with schematic diagrams can be found in [4,23]; however, a brief overview of the important configurations used in this work will be provided. Configuration one is the doubly-fed induction generator (DFIG). A partial power converter is used to control the electrical current in the generator's rotor. Configuration two has a full-power converter which enables the decoupling of the generator and grid frequency. This means that the frequency on the generator side can be fully controlled allowing for enhanced grid services and the use of a gearbox can be avoided. A synchronous electrical generator (which can be either an electrically excited synchronous generator (EESG) or a permanent magne<sup>t</sup> synchronous generator (PMSG)) is directly coupled to the main shaft of the rotor. Configuration three is a gearbox-equipped wind turbine with a full-power converter and medium/high-speed synchronous generator, which can be EESG or PMSG. In this arrangement, it is possible to choose between a relatively small gearbox (with moderate gear ratios) at the expense of using a large medium-speed synchronous generator. On the other hand, it is possible to assemble a gearbox with a higher gear ratio in order to reduce the size of the generator (high-speed configuration with synchronous generator). Configuration four is a gearbox-equipped wind turbine with a full-power converter; however, it has a high-speed asynchronous generator. As the fullpower converter enables the speed to be controlled by modifying the operating frequency, a squirrel cage induction generator (SCIG) is generally employed in this configuration. In the context of offshore wind energy more broadly, configuration three corresponds to a geared multistage high-speed wind turbine, configuration two is a direct-drive machine,

while type three and four are hybrid models. In relation to the work completed in this paper, configuration two makes up the direct-drive wind turbine category, while configurations one, three and four are grouped together into the gear-driven wind turbine category.

According to the JRC Wind Energy Database, in terms of market share, geared turbines have dominated the global onshore market, with the vast proportion of these turbines onshore made up of a DFIG arrangemen<sup>t</sup> below 3 MW rated power output. This is particularly true across Europe, Asia and North America. Further analysis presented in [1] shows the evolution of configuration types with geographical location, with configuration four more prevalent in North America and configurations two and three having more market share in Europe and Asia. If we look offshore across Europe, DFIG models dominated the early market predominately close to shore. This has vastly changed over the last 5–7 years, with direct-drive and hybrid models now making up a significant proportion. PMSGs have seen an explosion in the Asian market in particular, while EESGs are typically more common in European waters. Conversely, PMSGs have been gaining more traction in Europe as turbines increase beyond 5–6 MW [2,23,26–29]. The technological shift towards direct-drive PMSGs over other types of generators is predominately due to the perceived increase in reliability that can be achieved with fewer components and importantly, no gearbox. Whether the reliability of the system as a whole does in fact increase is still up for debate, with further evidence required in order to conclusively state either way. This topic will be discussed in greater detail throughout the reliability analysis section of this paper.

Looking offshore, this shift is even more apparent, with direct-drive machines starting to dominate the UK market over the last 5 years. In fact, from the 1725 wind turbines currently installed or under development in UK waters since 2016, 70.1% (or 1221) have direct-drive PMSG technology. This accounts for an installed capacity of just over 11.1 GW since 2016, 73.4% of total capacity either installed or under development.

Figure 1 shows the technology shift in the UK and wider European market from the first offshore wind farm to all current wind farms either operational or currently in development (due to be commissioned by 2026). These plots were developed by the authors using open source information found in [30]. In Figure 1a, each circle represents a wind farm, with the size of each circle scaled with the number of turbines that make up the site. Direct-drive configurations are shown in red, while gear-driven wind turbines are represented in blue. The y-axis displays individual wind turbine rated power of each site which, as shown on the plot, has increased significantly over the last 20 years along with the average site size. Figure 1b shows these same technology trends but split into each European country for comparison. Observed trends are similar, with turbine rating and total site capacity getting larger, with a large number of direct-drive machines entering the market over the last 5 years. Looking more closely at wind turbine manufacturers, Figure 2 shows a breakdown of OEM market share across Europe. SGRE currently has the largest share, with over 58% of installed capacity, followed by Vestas (28%) and GE Renewable Energy (11%).

Each wind turbine drive train configuration can be broken down into a common series of major components, each assigned a unique set of failure categories, which will be discussed extensively throughout the next section. Looking specifically at SCADA data related to the generator and gearbox, Figure 3 shows the average number of data channels recorded for both direct-drive and geared machines. Three examples of each configuration type were used, each ranging from 1 to 3 MW rated power. This information has been included to showcase the key differences when it comes to which sensors are available to create features for fault detection. This approach will later be used to assess model transferability. Based on these examples, direct-drive models have on average fewer channels and sensors than their geared counterparts. With regards to the generator specifically, temperature readings are typically available for the bearings, stator and rotor across both configurations, along with generator shaft speed and electrical current, voltage and power measurements.

**Figure 1.** Overview of offshore wind turbines drive train technology trends in Europe. (**a**) Comparison of geared and direct-drive offshore wind turbine installed capacity in Europe; (**b**) installed capacity of different drives by country.

**Figure 2.** European offshore wind installed (or planned) capacity by drive train type and OEM.

**Figure 3.** Number of SCADA channels assigned to each drive train category and component (showing only generator and gearbox).

#### **3. Wind Turbine Stop Rate Analysis**

Wind turbine reliability rates cover a range of metrics regarding wind turbine reliability. These can be wind turbine failure rates, stop rates, downtimes, or possibly lost production. For the purposes of this paper, the reliability rates examined are the stop rates and downtimes for wind farms in Europe and South America.

Wind turbine stoppages can occur for a range of reasons, from manual stops relayed from the owners or operators of the turbine to faults in the turbine components. Different metrics have been used in the past to assess the reliability of wind turbines, and their components. These metrics provide different levels of information, such as turbine availability percentage, stop rates, or failure rates. Wind turbine failures differ from wind turbine stops, and therefore cannot be directly compared. However, the general trends may still be examined and discussed, as stop rates will also include any failures, or faults, that caused the turbine to stop.

Previous studies have investigated the reliability of wind turbines, typically focusing on failure rates of wind turbine components. These are usually presented as number of failures per turbine per year, and are presented alongside the number of hours of downtime per turbine per year.

Several studies have collated reliability results from papers that have investigated wind turbine datasets. These studies typically present failures by component group, and these are usually similar with some slight variations in category definitions. The results presented in this section will be compared against the various past studies. Several of these studies also present the downtime per turbine per year in hours for the datasets. The studies cover wind turbines from across Europe, Asia, and the USA. The majority of these studies are focused on onshore turbines, and typically gear-driven turbines with some direct drives. In particular, a study from S. Ozturk et al. [31] has compared sub-1 MW geared and direct-drive wind turbines from the WMEP dataset in Germany. The papers collated in these reviews are described in Table 1.

The data provided for this study consisted of 39 farms, located in either South America or Europe. The majority of turbines assessed were located in South America, approximately 470, and the others, approximately 150, located in Europe. The European turbines are from either around the Mediterranean sea, or the British and Irish Isles.

Three of the farms investigated consisted of direct-drive turbines, approximately 50 turbines in total, with all of these located in Europe. The turbines rated between 1.5 and 3.5 MW, and aged between 2 and 13 years. The majority of turbines were operating for less than 7 years, with a small minority in operation longer. The data provided cover the life of the turbines investigated from when they were first operational until the beginning of 2020. A factor that is of interest in the wind energy community is how wind turbine reliability rates, such as stoppages, scale with turbine size. This link is unfortunately beyond the scope of this paper, as the dataset is limited to turbines of similar rating. Future work, utilising a dataset with more varied turbine rating, could examine the change in reliability rates between small and large rated turbines.



47


**Table 1.** *Cont.*

The categories used here were defined based on a combination of two reliability studies [5,49], which was done to allow for ease of comparison with previous work. The first set of categories were taken from Figure 8 in [49], with the addition of Grid from [5], and finally Nacelle, Shafts and Bearings categories were added.

Stop rate was calculated by taking the number of stops in each category and dividing through by the number of turbines and years of operation in each farm, this gave a number of stops per turbine per year.

$$S = \frac{N\_s}{N\_T \* T} \tag{1}$$

where *S* is the stop rate, *Ns* is the number of stoppages recorded for that category, *NT* is the number of turbines in that farm, and *T* is the years in operation for that farm. Downtime was calculated, for each farm, by dividing the total days of downtime per category by the number of turbines in the farm and the number of years in operation.

$$DT = \frac{T\_D}{N\_T \* T} \tag{2}$$

where *DT* is the downtime, *TD* is the total days of downtime for recorded, *NT* is the number of turbines in the farm, and *T* is the years in operation for that farm. These two values allow for fair comparison across all farms and turbines, as it negates the effect of farm size or age.

Figure 4 shows the average stop rates and downtimes for the direct-drive and geardriven farms respectively. As can be seen, the generator has roughly double the stop rate in direct-drive turbines, and there is also a much greater average downtime for direct-drive generators. The top three stoppage and downtime categories for each turbine type are shown in Table 2. There are some similarities between both turbine types; however, from Figure 4, it can be seen that direct-drive turbines seem to have higher overall downtimes. Even for components that are assumed to be similar between turbine configurations, there are quite large differences in stop rate and downtimes. For example, the sensors and pitch systems both have differences in stop rate and downtime, whilst being components that should not differ too much between configuration. It is possible that the pitch system could be hydraulic or electric, and this could bring about some of the change. There may also be a difference in turbine operation that could account for these differences.

**Figure 4.** Comparison of the Geared and Direct Drive turbines within the dataset based on average stop rates and downtimes for each configuration.

Before any comparisons are made between the results presented here and in other studies, it is important to note that stoppages are not the same as failures, therefore a direct comparison cannot be made. Stoppages can include failures; however, they also include stoppages due to alarms. These alarms can be for any reason, such as temporary overheating of a component. It is possible that these stops are indicators of failure; however, developing a model to represent this link is beyond the scope of this paper.


**Table 2.** Top three stoppage and downtime categories per turbine type.

#### *Comparison with Previous Studies*

Table 1 presents the details of previous failure rate and downtime studies examined. The table outlines the different databases used by each paper, the number of turbines contained, the time period examined, and the country of origin for each database. The last two columns of the database present the top three turbine components by failure rate, and downtime per failure respectively. From this table, two studies examined the reliability data for direct-drive turbines in Europe. The first, from M. Reder et al. [50], found that the top three, in descending order, components by failure rate were the Controller, Met Station, and Yaw systems, and by downtime were the Generator, Blades, and Controller. The second study, from S. Ozturk et al. [31] found that the top three components by failure rate were Controls, Electric Systems, then the Generator and Hub were tied for third. The top three by downtime were the Rotor Blades, Parts/Housing, and then the Drivetrain. When this is compared against Figure 4, it can be seen that there are some differences. The top three categories are shown in Table 2; however, these are for stoppages rather than failures. So it may not be appropriate to make a direct comparison.

The frequency of which each component is featured within the top 3 failures or downtimes is plotted in Figure 5. This bar chart plots the number of times each component was featured in the top three of either metric in Table 1. This chart can then be used to find out the overall top three components for both failure rates and downtimes. For failure rates, the top three components were the Controller, Pitch, and Generator, with the Controller, Gearbox, and Generator coming in at top three for downtime.

**Figure 5.** Frequency of each component being within studies top three failures or downtimes from Table 1.

One review from Crabtree et al. [5] presented the stop rate for the Egmond aan Zee offshore wind farm situated in the Netherlands. This farm consisted of 36 turbines with 3 years of operational data. These were geared 3 MW turbines, and unlike other studies presented stop figures instead of fault data. The control system, yaw system, and service stop categories were the top three, with the Gearbox, Generator, and Blades stops being the top three categories for downtime. This review is of particular interest as it presents stop rates, which can be directly compared against the results presented here.

Compared with Figure 4, the geared turbines from this study have relatively low stops due to the yaw and control systems, and low service stops on average with quite extreme outliers. For the downtime, the Generator, Gearbox, and Blades were relatively low.

Stop rates are explicitly different from failure rates in several ways—for example stop rates are caused by an wind turbine stoppage, whereas failure rates are due to an unscheduled, or unplanned, failure of the turbine due to some fault or malfunction. Therefore, several categories are found in stop data that would not be found for failure data, such as scheduled service as these are known in advance, or grid failures which are outwith the operator control. Stoppages are also typically more frequent and should have lower downtimes on average per stoppage as they are usually less severe than turbine failures. Within the stop data there will be failure examples, as these are examples of wind turbine stoppages; however, they will be less frequent.

#### **4. Framework for Assessing the Transferability of Diagnostic Techniques between Drive Trains**
