The Wind Turbine Prognostics and Health Management library processes wind turbine events (also called alarms or status) data, as well as operational SCADA data (the usually 10-minute data coming off of wind turbines) for easier fault detection, prognostics or reliability research.
Turbine alarms often appear in high numbers during fault events, and significant effort can be involved in processing these alarms in order to find what actually happened, what the root cause was, and when the turbine came back online. This module solves this by automatically identifying stoppages and fault periods in the data and assigning a high-level “stoppage category” to each. It also provides functionality to use this info to label SCADA data for training predictive maintenance algorithms.
Although there are commercial packages that can perform this task, this library aims to be an open-source alternative for use by the research community.
Please reference this repo if used in any research. Any bugs, questions or feature requests can be raised on GitHub. Can also reach me on twitter @leahykev.
This library was used to build the “batch creation” and “data labelling” steps of this paper.
Install using pip!
pip install wtphm
- Input Data Needed for Batch Creation
- Group Faults of the Same Type
- Creating Batches
- Assigning High-Level Root Causes to Stoppages
- Analysing Stoppages
- Labelling the SCADA data