Recommended practices for wind farm data collection and reliability assessment for O&M optimization

The paper provides a brief overview of the aims and main results of IEA Wind Task 33. IEA Wind Task 33 was an expert working group with a focus on data collection and reliability assessment for O & M optimization of wind turbines. The working group started in 2012 and finalized the work in 2016. The complete results of IEA Wind Task 33 are described in the expert group report on recommended practices for "Wind farm data collection and reliability assessment for O & M optimization" which will be published by IEA Wind in 2017. This paper briefly presents the background of the work, the recommended process to identify necessary data, and appropriate taxonomies structuring and harmonizing the collected entries. Finally, the paper summarizes the key findings and recommendations from the IEA Wind Task 33 work

Failure Modes, Effects and Criticality Analysis for Wind Turbines Considering Climatic Regions and Comparing Geared and Direct Drive Wind Turbines

The wind industry is looking for ways to accurately predict reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique utilized to determine the critical subsystems of wind turbines. There are several studies in the literature which have applied FMECA to wind turbines, but no studies so far have used it considering different weather conditions or climatic regions. Furthermore, different wind turbine design types have been analyzed applying FMECA either distinctively or combined, but no study so far has compared the FMECA results for geared and direct-drive wind turbines. We propose to fill these gaps by using Koppen-Geiger climatic regions and two different turbine models of direct-drive and geared-drive concepts. A case study is applied on German wind farms utilizing the Wind Measurement & Evaluation Programme (WMEP) database which contains wind turbine failure data collected between 1989 and 2008. This proposed methodology increases the accuracy of reliability and availability predictions and compares different wind turbine design types and eliminates underestimation of impacts of different weather conditions

Performance and Reliability of Wind Turbines: A Review

Performance (availability and yield) and reliability of wind turbines can make the difference between success and failure of wind farm projects and these factors are vital to decrease the cost of energy. During the last years, several initiatives started to gather data on the performance and reliability of wind turbines on- and offshore and published findings in different journals and conferences. Even though the scopes of the different initiatives are similar, every initiative follows a different approach and results are therefore difficult to compare. The present paper faces this issue, collects results of different initiatives and harmonizes the results. A short description and assessment of every considered data source is provided. To enable this comparison, the existing reliability characteristics are mapped to a system structure according to the Reference Designation System for Power Plants (RDS-PP®). The review shows a wide variation in the performance and reliability metrics of the individual initiatives. Especially the comparison on onshore wind turbines reveals significant differences between the results. Only a few publications are available on offshore wind turbines and the results show an increasing performance and reliability of offshore wind turbines since the first offshore wind farms were erected and monitored

Autoencoder-based anomaly root cause analysis for wind turbines

A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder. These models have proven to be very successful in detecting such deviations, yet cannot show the underlying cause or failure directly. Such information is necessary for the implementation of these models in the planning of maintenance actions. In this paper we introduce a novel method: ARCANA. We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder. It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably. This reconstruction must be similar to the anomaly and thus identify only a few, but highly explanatory anomalous features, in the sense of Ockham’s razor. The proposed method is applied on an open data set of wind turbine sensor data, where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment. The results are compared with the reconstruction errors of the autoencoder output. The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features, whereas using the non-optimised reconstruction error does not. Even though the deviation in one specific input feature is very large, the reconstruction error of many other features is large as well, complicating the interpretation of the detected anomaly. Additionally, we apply ARCANA to a set of offshore wind turbine data. Two case studies are discussed, demonstrating the technical relevance of ARCANA