WTPHM

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.

Installation

Install using pip!

pip install wtphm

Is my Data Compatible?

The data manipulated in this library are turbine events/status/alarms data and 10-minute operational SCADA data. They must be in the formats described below.

Event Data

The event_data is related to any fault or information messages generated by the turbine. This is instantaneous, and records information like faults that have occurred, or status messages like low- or no- wind, or turbine shutting down due to storm winds.

The data must have the following column headers and information available:

• turbine_num: The turbine the data applies to
• code: There are a set list of events which can occur on the turbine. Each one of these has an event code
• description: Each event code also has an associated description
• time_on: The start time of the event
• stop_cat: This is a category for events which cause the turbine to come to a stop. It could be the functional location of where in the turbine the event originated (e.g. pitch system), a category for grid-related events, that the turbine is down for testing or maintenance, in curtailment due to shadow flicker, etc.
• In addition, there must be a specific event code which signifies return to normal operation after any downtime or abnormal operating period.

SCADA/Operational data

The scada_data is typically recorded in 10-minute intervals and has attributes like average power output, maximum, minimum and average windspeeds, etc. over the previous 10-minute period.

For the purposes of this library, it must have the following column headers and data:

• turbine_num: The turbine the data applies to
• time: The 10-minute period the data belongs to
• availability counters: Some of the functions for giving the batches a stop category rely on availability counters. These are sometimes stored as part of scada data, and sometimes in separate availability data. They count the portion of time the turbine was in some mode of operation in each 10-minute period, for availability calculations. For example, maintenance time, fault time, etc. In order to be used in this library, the availability counters are assumed to range between 0 and n in each period, where n is some arbitrary maximum (typically 600, for the 600 seconds in the 10-minute period).

User Guide

• Overview
• Input Data Needed for Batch Creation
  • Events Data
  • SCADA Data
  • Sample Data
• Group Faults of the Same Type
• Creating Batches
• Assigning High-Level Root Causes to Stoppages
  • Using the root events
  • Using the Availability Counters
  • Combining the Labelling Methods
• Analysing Stoppages
• Labelling the SCADA data
• References

API

• wtphm.batch
• wtphm.pred_processing
• wtphm.clustering
  • wtphm.clustering.batch_clustering
  • wtphm.clustering.event_probs

Github Page

Can be found at https://github.com/lkev/wtphm.

Indices and tables

• Index
• Module Index