  Anomaly detection plays a crucial role in the field of predictive maintenance
for wind turbines, yet the comparison of different algorithms poses a difficult
task because domain specific public datasets are scarce. Many comparisons of
different approaches either use benchmarks composed of data from many different
domains, inaccessible data or one of the few publicly available datasets which
lack detailed information about the faults. Moreover, many publications
highlight a couple of case studies where fault detection was successful. With
this paper we publish a high quality dataset that contains data from 36 wind
turbines across 3 different wind farms as well as the most detailed fault
information of any public wind turbine dataset as far as we know. The new
dataset contains 89 years worth of real-world operating data of wind turbines,
distributed across 44 labeled time frames for anomalies that led up to faults,
as well as 51 time series representing normal behavior. Additionally, the
quality of training data is ensured by turbine-status-based labels for each
data point. Furthermore, we propose a new scoring method, called CARE
(Coverage, Accuracy, Reliability and Earliness), which takes advantage of the
information depth that is present in the dataset to identify a good all-around
anomaly detection model. This score considers the anomaly detection
performance, the ability to recognize normal behavior properly and the
capability to raise as few false alarms as possible while simultaneously
detecting anomalies early.
