Back to the programme printer.gif Print

Thursday, 13 March 2014
11:15 - 12:45 Advanced operation & maintenance
Science & Research  

Room: Llevant
Session description

The session covers the entire area of wind farm and wind turbine operation and maintenance, e.g. how to access, repair and organise operation and maintenance logistics onshore and offshore. In order to keep in hand the current health of turbines and farms, failure detection, identification and prognosis methods are also presented. Maintenance operations are also addressed from the viewpoints of required activities and efficiency. In order to cover management aspects, operation and lifetime cost calculation methodologies are also introduced. Experts from various European countries share their results during the session.

Learning objectives

  • Advanced operation and maintenance
  • Fault detection methods
  • Reliability calculation techniques
  • Monitoring on the field
  • Statistical and artificial intelligence-based solutions for diagnostics data- and model-based solutions for fault detection
Lead Session Chair:
Zsolt Viharos, Hungarian Academy of Sciences, Hungary

Christopher J. Crabtree , University of Durham, United Kingdom
Christos Kaidis Uppsala University - MECAL B.V., The Netherlands
Christos Kaidis (1) F P Bahri Uzunoglu (2) Filippos Amoiralis (3)
(1) Uppsala University - MECAL B.V., Enschede, The Netherlands (2) Uppsala University, Visby, Sweden (3) MECAL B.V., Enschede, The Netherlands

Printer friendly version: printer.gif Print

Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Mr. Kaidis is a new professional in the wind industry. He is currently a junior engineering consultant at Mecal Independent Experts B.V. in Enschede, The Netherlands. He studied in the Wind Power Project Management Master’s Programme in Uppsala University and carried out his research for his Master’s thesis on Wind Turbine Reliability in cooperation with Mecal Independent Experts. He is specialized in wind turbine operation and maintenance.




This research project discusses the life-cycle analysis of wind turbines through the processing of operational data from two modern European wind farms. A methodology for SCADA data processing has been developed combining previous research findings and in-house experience followed by statistical analysis of the results. The analysis was performed by dividing the wind turbine into assemblies and the failures events in severity categories. Depending on the failure severity category a different statistical methodology was applied, examining the reliability growth and the applicability of the “bathtub curve” concept for wind turbine reliability analysis.


The scarcity of wind turbine failure data has been pointed out by several researchers in the past [1]. This was of the main reasons that the use of SCADA data was selected; SCADA data is easier to acquire and more easily manageable compared to hand-written maintenance logs. Initially, the failure events are detected using the SCADA counters. Following, the failures are divided to the different turbine assemblies according to the alarm that initiated the failure and are also divided according to their duration. The failure duration is separated into logistic delay time and service time, with the service time defining the failure severity. As a result, the failures are separated into manual restarts, minor repairs and major repairs.
A point process (Power Law Process) is applied for the statistical analysis of the failures with short service duration (manual restarts and minor repairs) assuming that the wind turbine assembly that failed was brought back to the operational condition it was before the failure (“as-bad-as-old” assumption). For the major failures the Weibull distribution is used adopting the assumption that the assembly is “as-good-as-new” after the failure. Moreover, the results for each wind farm are combined in order to make reliability estimation for a case-specific wind farm. This is made by modifying the results according to the operating conditions of the target wind farm using the findings of other researchers concerning the impact of environmental factors on wind turbine reliability [2]. A more detailed description followed by examples and results is presented in the following section.

Main body of abstract

For the present research 10-min SCADA data from two modern European wind farms were processed. The rated power of the wind turbines varies from 850kW to 3MW and the total amount of data processed to-date is approximately 129 Turbine*Years (Figure 1).

For the failure detection a Visual Basic algorithm was developed mainly using the SCADA counters (Turbine OK, Service On, Alarm On). Through the counters the state of the wind turbine can be defined and thus the downtime events. The total downtime for each event is separated into response time and service time and the failure events are separated according to the service duration (Figure 2), combining the methodologies developed for the Reliawind project [3] (where failures were separated according to their impact) and WMEP [4] (failure division into minor and major depending on the total downtime). In addition to the failure events the algorithm developed can detect automatic restarts of the wind turbine and scheduled services, information that can be used for further research in maintenance planning.

In the studied wind farms, the failure frequency results show that the assemblies with the higher failure frequency are the pitch system, the frequency converter and the control & communication system (Figure 3). For some assemblies the minor repairs are more frequent than the manual restarts. Additionally, the assemblies the failures of which cause the longest downtime when they fail are the frequency converter, the pitch system and the power electrical system (Figure 4).

Most of the previous research projects on wind turbine reliability ([3], [4], [5]) have adopted the “bathtub curve” concept, assuming a constant failure rate in time during the useful lifetime of the wind turbine. Recently, the applicability of the constant failure rate assumption was doubted [6]. Efforts have been made, to estimate more accurately the reliability growth of wind turbine assemblies during the turbine’s lifetime [7]. In [7] the Power Law Process (a Non Homogenous Poisson Process) is used to monitor reliability growth and the Weibull distribution is proposed by [6] and [8]. As explained in [9], a point process is useful for modeling the inter-occurrence of failures assuming that the assembly was brought back to the condition it was before the failure. On the other hand, a probability distribution is useful for modeling one single lifetime of an assembly / system.
Taking the previous into consideration, the Power Law Process was applied for the manual restarts (Figure 5) and minor repairs (Figure 6) whilst the Weibull distribution was used to model the major repairs (an example is given in Figure 7 for the pitch system failures), assuming that after a major repair the assembly was thoroughly repaired or fully replaced. It can be seen in Figures 5 and 6 that the failure rate approaches a constant trend in time but not for all the cases.

The statistical analysis explained above has been performed for each wind farm separately in order to take into consideration the different environmental conditions for each case. The influence of environmental factors has been pointed out by several researchers ([2], [10], [11]).For this research the findings of [2] concerning the influence of turbulence intensity and mean wind speed were used.
In order to make a reliability estimation for a hypothetical wind farm the failure rate of each wind farm of those calculated is modified according to the different failure percentage for each wind speed and turbulence intensity value according to [2]. Using the modified failure rates, the failure rate function for the wind farm under discussion is the weighted average of the wind farms for which failure data is available, i.e. depending on the turbine*years of data available for each wind farm. An example of the estimation of the frequency of manual restarts is shown in Figure 8.


Operation and maintenance cost constitutes approximately 20% of the total cost during the life-cycle of a modern wind farm [12]. This increases the need for more precise knowledge of a wind turbine’s life-cycle that will lead to accurate maintenance scheduling, which is significant especially for offshore wind farms where the logistics cost is much higher. Moreover, more thorough examination of the life-cycle of specific wind turbine assemblies can prove useful in wind turbine design, placing the focus on those that demonstrate a higher failure frequency. Extracting general failure statistics referring to the frequency of downtime events for a wind turbine is not enough and can result to misleading conclusions.
Initially, it is important to detect properly the failure events through operational data and separate them to categories depending on their severity and the assembly they occurred. Obviously, failure events with different severity have a different impact on wind turbine availability and operational expenditure. Moreover, it has been shown that the approach followed for the statistical analysis of each severity category also differs. The bathtub curve concept should not be taken for granted but its validity should be examined instead. Additionally, another piece of information that is not taken into consideration when analyzing cumulative failure data of various wind farms is the effect of environmental conditions on wind turbine reliability. As it has been discussed earlier the failure history of each wind farm should be examined separately in order to make possible the modification of the results according to the environmental conditions of the wind farm we would like to make the reliability estimation for.

Learning objectives
The methodology developed in this research project provides information on how to structure a detailed wind turbine failure database using SCADA data. Additionally, the process of using it for reliability estimation by applying a proper statistical model according to the failure type is explained. Finally, the impact of average wind speed and turbulence intensity is introduced to the estimation process.

[1] Wennerhag, P., & Bertling, L. (2012). Wind turbine operation and maintenance, Survey of the development and research needs, Elforsk report 12:41, Elforsk, Stockholm October 2012
[2] Wilkinson, M., Van Delft, T. & Harman, K., 2012. Effect of environmental parameters on wind turbine reliability. Copenhagen, EWEA.
[3] Tavner, P., 2011. Recommendations from the Reliawind Consortium for the Standardisation for the Wind Industry of Wind Turbine Reliability Taxonomy, Terminology and Data Collection, s.l.: Reliawind.
[4] Faulstich, S., Hahn, B. & Tavner, P., 2010. Wind turbine downtime and its importance for offshore deployment, s.l.: John Wiley & Sons, Ltd.
[5] Ribrant, J. & Bertling, L. M., 2007. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997-2005. IEEE Transactions on Energy conversion, March, 22(1), pp. 167-173
[6] Buckley, S., 2013. Forecasting wind farm component failures and availability post-warranty. Vienna, EWEA.
[7] Tavner, P., Xiang, J. & Spinato, F., 2007. Reliability Analysis for Wind Turbines. Wiley Interscience, Volume 10, pp. 1-18.
[8] Andrawus, J. A., 2008. Maintenance optimisation for wind turbines, Aberdeen: The Robert Gordon University
[9] Crow, L. H., 2004. Practical Methods for Analyzing the Reliability of Repairable systems. Reliability EDGE, 5(1), pp. 4-9.
[10] Tavner , P. et al., 2010. Study of effects of weather & location on wind turbine failure rates. Warsaw, s.n.
[11] Faulstich, S., Hahn, B., Lyding, P. & Tavner, P., 2009. Reliability of offshore wind turbines. Identifying risks by onshore experience. Stockholm, EWEA.
[12] Zhang, J., Chowdhury, S., Messac, A. & Castillo, L., 2012. A response surface-based cost model for wind farm design. Energy policy, March, pp. 538-550