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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Ferdy Hengeveld MECAL, The Netherlands
Co-authors:
Ferdy Hengeveld (1) F Christos Kaidis (1)
(1) MECAL, Enschede, The Netherlands

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Ferdy Hengeveld has a Bachelor degree in Mechanical Engineering, obtained Cum Laude at the Saxion University of Applied Sciences in Enschede, the Netherlands (1998) with a focus on Energy Technology.

Throughout his career, he has worked in renewable energy, with a strong focus on wind turbine technology. Currently, he is heading MECAL Wind/Energy, supporting OEMs and operators/owners of wind farms by improving their products and assets. Furthermore he is part of the Management Team of MECAL.

Mr. Hengeveld is member of the NEC88 subcommittee that is defining the guidelines for extended operation of wind farms in the Netherlands.


Poster

Poster Download poster (9.67 MB)

Abstract

Improving wind farm reliability and performance using hybrid life cycle analysis method

Introduction

Today, wind farms are treated more and more as power stations, instead of a group of wind turbines. Levelised Cost of Energy (LCoE) has the highest priority in the operation. Optimisation of Operation and Maintenance (O&M) is of the utmost importance, facilitated by the almost standardised application of condition monitoring systems and extensive SCADA.
Older wind farms have limited SCADA systems and often no CMS, but their owners/operators have the same desire to get as much out of their assets as possible.
This paper presents the application of life cycle analysis on existing wind farms, with the purpose of giving the operators better insight in unscheduled maintenance costs and potential to decrease these. The method combines analysis of SCADA data with statistical failure data and environmental conditions.


Approach

The scarcity of public wind turbine failure data is well known. The available statistical data describes the failures on a high level, without details. On the other hand, the SCADA data of individual wind farms gives detailed information, but with small statistical significance due the limited size of the wind farm. The developed method combines both:
The historical performance of the wind farm is analysed using the available SCADA data and maintenance data. From this, Key Performance Indicators are extracted to evaluate the reasons for suboptimum performance. The data is then correlated to the general statistics, taking into account environmental factors such as mean wind speed and turbulence intensity, which are known to have influence on the reliability of a wind turbine.


Main body of abstract

For the failure detection of the wind farm, an algorithm was developed using basic SCADA counters, which are in general available, also for older wind farms. Through the counters (Turbine OK, Service On, Alarm On) 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, service time and duration, combining the methodologies developed for e.g. Reliawind and WMEP. As a result, basic reasons for long downtime events can be identified (e.g. lack of spare parts that led to logistic delays, low crew responsibility).
For the different failures (manual restart, minor repair, major repair) different assumptions are made for the situation after repair: A non-homogenous poison process has been applied to model the interoccurence of Manual restarts and Minor Repairs under the assumption that the WTG is partially or not at all improved compared to its state before the failure.
The Weibull distribution was used to model the major repairs, assuming that after a major repair the assembly was thoroughly repaired or fully replaced. In order to transfer the results of wind farms analyzed to others, the influence of the environmental conditions is taken into account, so that the reliability figures will be adjusted accordingly.

The result of this gives an indication of the reasons for suboptimum operation of the wind farm and is used to define improvements, e.g. in O&M strategy. Using this method, various wind farms in operation have been analysed and improved. In the full paper the application on an existing wind farm will be described


Conclusion

For older wind farms, the analysis of cost of unscheduled maintenance on SCADA data only is challenging due to limited available data. A hybrid method has been developed, combining statistical data with available SCADA data and environmental conditions. This increases the accuracy of reliability analyses and gives better insight to operators in the performance and optimisation of their assets.


Learning objectives
• Implementation of the impact of average wind speed and turbulence intensity on reliability estimation
• Application to improve the performance of wind farms
• Evaluation of the maintenance strategy (spare parts, available crew etc.) based on Operational data