FAILURES AND RELIABILITY IN HAWTs OPERATING IN MOUNTAINOUS AREAS, OPEN SEA AND IN HARSCH CLIMATE SITES

Recently, offshore wind plants, as Horn’s Riv one, have reached a remarkable interest and development. In these sites, HAWT’s blades experiment corrosion, erosion and fouling and, at higher latitudes, icing conditions too, as in mountainous areas and in harsch climate sites. The operative conditions may influence the machine damages and they may occur as consequence of erosion, corrosion, fouling, icing, exfoliation caused by the above mentioned environmental conditions. The paper reports about data collection about failures occurred to HAWTS operating in offshore, mountainous and harsch climate sites in last ten years. Particularly, the damage occurred to blades, gears and bearings have been examined and the consequences analysed. The occurred failures are examined and the reliability of WTGs is assessed by appropriate models and analysis. The results are com pared to literature data and they may be very useful in programmable maintenance and in predictable one. Reliability analysis may be also useful to increase energy production.


Introduction
Wind is one of the most important renewable energy sources and at the present time there are many wind farms in onshore and offshore sites.
planning of preventive maintenance suggesting when is suitable to execute the maintenance operation. This is possible by analyzing what happens when a particular transition between two different states may occur.
Evaluations about WTGs failure show that modern onshore WTs (Win d Turbine) in Europe achieve a high availability of 95-99% [6]. However, despite WT technology progress, in terms of economy and performance, WT reliability seem to be declined with growing turbine size.
Electrical and electronic subassemblies, in particular, fail more frequently, leading to higher failure rates for WTs of higher complexity. An increasing number of failures cause unplanned downtimes up to 10 times per turbine per year, resulting in high maintenance effort and production loss [5].
While the performance and the efficiency of wind turbines and their energy yields have been improved with time, their reliability still needs improvement, particularly when considering their deployment offshore [9]. Many factors may influence the WTGs failure rate, such as wind speed, wind turbine design and climate conditions: these aspects should be part of every reliability analysis and assessment [8].
In the Tab. 1 data about these aspects have been collected.
The shown data have been collected in different way: some have been collected, directly by the Author, on machines or wind farms, during mainteinance operation in several Italian sites as Collarmele or Tocco da Casauria (in Abruzzi Region) and Frosolone (in Molise Region) and ever in mountainous areas, others have been collected by mainteinance journal of WTG in German sites or during specific inspection, while only for a group of German wind farms the data have been produced by a previous paper [4].
The modelling of reliability structure of WTG has been developed by analysing many WTG layout. Table 1. Climate conditions and type of site from [8] Particularly, it seems that WTG design complexity may show an higher failure rate than others and this is more evident in electric systems, electronic control, sensors, yaw systems, rotor blades, generator and drive train [2].
The specific climate site conditions may be very useful in understanding the failure causes [8].

Reliability of hawts
Reliability is defined [2] as the ability to perform as required, without failure, for a given time interval under given conditions, whereas availability is defined as the ability to be in a state to perform as required in the International Electrotechnical Commission (IEC) 60050 standards. Particularly, reliability is the probability of a device performing its purpose adequately for the period of time intended under the operating conditions encountered, while availability is the probability of finding a system in the operating state at some time into the future as in [4]. [5] and [7].
Under the mathematical point of view, reliability may be modelled as in the following equations as in [4] and [7]. Reliability function, from the MTBF point of view, may be: where R(t) is reliability as function of time (t) and m is MBTF (it depends from failure rate), assessed by Ireson's criteria [7]; from a global point of view, reliability may be assessed as in the following eqs. (2) and (3): where [4] µ is the repairing rate, λ is the failure rate, T the time duration of the cycle, D the rate of operative running time vs. starting up numbers and P S the probability of a failure during the start up and, at last, P the conditioned unavailability.
Therefore reliability evaluation is represented by R(t) * = 1-P (3) To assess reliability of a system, the system structure function may be very useful. This structure has been analysed in the Figs.1, 2, 6, 7 and 8 where every block is a component or a subsystem.
Taking into account the system shown in Fig. 1 (system structure of an HAWT) the corresponding reliability may be modelled as: Φ is the reliability function (expressed as structure system function) related to the structure and X j is the generic value of the component "j" reliability. In this way reliability may be assessed at first on the basis of machine structure or rather the axis line of the machine.
Obviously, X j contributes in different way if the component is "in series" (in succession) or "in parallel" (in redundancy) and the corresponding contribute X j to the Φ function is:  To analyse, in a correct way, the structure of an HAWT, it is very important to describe the components and the subsystems. They are collected in the Table 2 Table 2. Subsystems and components of HAWT from [8] Every failure may produce a downtime with a resulting lost in energy production or a lower power generation vs. rated power. The lost in power generation have a corresponding lost in energy productions and this may be modelled as in [8] by the equation: where LEP is the lost energy production (kWh), CF the capacity factor, P WTG the WTG rated power (kW), DTF the downtime per failure (h) and AFR the annual failure rate. From this point of view the analysis of downtime may be very important in assessing lost energy production from several wind farms. The first stage of the present work has been a filed study carried out measuring the reliability of existing WTGs at several operational wind farms in Germany and Italy, using WTGs data as: 10-Minute average SCADA DATA, fault/alarm logs, work orders/service reports and O&M contractor reports. As shown in Figs. 3-4-5-6-7-8, some structure schemes may be very useful in analysing the above mentioned data, but to analyse system reliability in the better way possible we ought to remember the structure of systems as showed in Fig. 6 and the following formula by [10] about the average failure rate for sub-assemblies that has been calculated over the entire recording period according to: where i is index counting the number of sub-assemblies failures, n is the number of sub-assembly failures, T is the total length of the recording period, k is the index counting the total number of downtimes and, obviously, D is downtime.   The impacts of climatic conditions on wind turbine failures have been investigated in many studies as in [4] and [8]. Low temperatures could lead to lubricant freezing and brittleness in the components while temperature variations could cause expansions and contractions. Generally, high wind speed, turbulence and gust can produce lower reliability of wind turbine blade, pitch and mechanical drive train, whereas temperature and humidity affect electrical components rather than mechanical ones. The external factors such as lightning, icing and high winds increase the failure rate of wind turbines by 1.713 times. For the effects of weather conditions on wind turbine failures, winter is the season in which failure frequencies are increased and wind speed did not show any impact on failure occurrences. About 30% of the blade damage cases are caused by thunderstorms, followed by heavy rainfall with 28%. Climatic conditions can not only have an impact on failure rates, but also affect the repair times of any failures, thus eventually causing variation in the resulting downtime. It is intuitive that repair time for a wind turbine in a snowy region when there is a heavy snowfall is not as the same as a region with no environmental obstacles for repair time. Particularly, we have observed as the failure rates and downtime values, based on different turbine types and aspects, show that direct-drive wind turbines failure rates in electrical and electronical components are greater than geared-drive wind turbines where gearbox failures cause the most downtime. Therefore, there is a need to determine the criticalities in these two different types of wind turbine. Really the impact may be very high in harsch climate sites as shown in following Tabs. Furtherly, the reliability of HAWTs may analysed using Second-Order Semi-Markov Chain as in [3] and for structural failure may be useful the procedure in [1] and also the other in [9].

Failure data analysis
Starting by the preliminary study as in [4], as previously exposed, we have collected data about failure rates, MTBF and downtime values for several WTGs in differente sites in Germany, North Europe Countries and in Italy, onshore and offshore, in mountainous areas and also in harsch climate sites.
The above mentioned data are shown in the following Tabs. Table 3. Data concerning of hawts taken into account in [4] Table 4. Data about WTGs in Germany in [8] and [10] 13 E3S Web of Conferences 312, 11010 (2021) https://doi.org/10.1051/e3sconf/202131211010 76° Italian National Congress ATI Table 5. Downtimes in German Wind Farms from [8] and [5] Table 6. Downtimes in hawts operating in harsch climate sites from [4] and [8] The data shown in Tab. 3 concern of wind turbines failures in the analysed Italian wind farms, while data in Tabs. 4 and 5 concern wind turbines in German sites. The Tab. 6 shows data about the downtimes detected in German and Italian sites with the same wind turbine type (Micon, Nordex and Vestas about 2 MW in rated power).
On the contrary, in Tab. 6 recent data (2015-2018) about Italian and German sites are exposed.
They show as the larger downtime is due to mechanical systems thet in mountainous areas may have a particular failure sensitivity. In the Tab. 7 recent data (2017-2019) about some Italian wind farms in mountainous sites are exposed: according to them the drive train failures and the yaw systems ones are the larger and 14 E3S Web of Conferences 312, 11010 (2021) https://doi.org/10.1051/e3sconf/202131211010 76° Italian National Congress ATI by analysis od SCADA system data the main cause appears as the gusts suffered during winter storms and the grid failures during the same period. Gearbox failures have caused great damages to drive train and, particularly, to the electrical generator increasing the global downtime as yet. Table 7. Recent data about downtimes in Italian wind farms The annual failure rate (AFR) is defined as the average number of failures per year and the corresponding data are shown in Tab. 8. Table 8. AFR data concerning of wind farms analysed in [5] and [8] 15 E3S Web of Conferences 312, 11010 (2021) https://doi.org/10.1051/e3sconf/202131211010 76° Italian National Congress ATI Starting from downtimes data, it's possible to assess reliability values for subsystems and components, as in Tab. 9, and, then, from equation (3), (4), (5) and (6) analyse the HAWT reliability.
The resulting value for the HAWT global reliability is shown in Tab. 10 where the reliability value for every site-type results from the analysis of collected data about mainteinance of several wind farms. Table 9. Reliability values assessment for hawt subsystems and components Table 10. HAWTs reliability values assessment In Tab. 10 reliability assessment has been proposed as result of eq. (4) and referring to the wind turbine structures in Fig. 8.
These values represent the functional objective that mainteinance should assure for wind turbines in the corresponding sites and the related environmental conditions. 16 E3S Web of Conferences 312, 11010 (2021) https://doi.org/10.1051/e3sconf/202131211010 76° Italian National Congress ATI

Conclusions
The climate conditions sites and the type of HAWT drive train affects machine reliability in a remarkable way, and, generally, the best values are reached by gearless wind turbine.
In several mainteinance actions the Author has detect as reliability analysis results are not taken into account, probably for a kind of different organization, but this fact doesn't result in a good efficiency.
The MTBF data may be very useful in organizing preventive mainteinance and the reliability assessment according eq. (2) may useful to know the specific reliability and the MTBF expected value if failure rate is constant. A larger use of mainteinance procedures based on reliability analysis may be very useful particularly in wind farms offshore, in mountainous areas and in adverse climate conditions. A greater diffusion of these procedures among the companies that deal with maintenance would be very important to reduce intervention times and the global mainteinance duration. In this way, downtimes may be reduced with a lower lost in energy pruduction (LEP).
Also a reduction in AFR value using higher quality components may be very useful. To reach this objective the reliability assessment based on eq. (4) may be useful.
As above shown, wind turbines achieve an excellent technical availability of about 98% on average, although they have to face a high number of malfunctions.
It can be assumed that these good availability figures can only be achieved by an high number of service teams who respond to turbine failures within short time.
Furtherly, in order to improve the reliability of WTs, the designers have to better the choice of electric and electronic components. This is particularly true and absolutely necessary in the case of new and large turbines, expecially for machines operating in adverse and hasch climate conditions sites.