Metadata-Version: 2.1
Name: wtphm
Version: 0.1.3
Summary: SCADA data pre-processing library for prognostics and healthmanagement and fault detection of wind turbines
Home-page: https://github.com/lkev/wtphm
Author: Kevin Leahy
License: UNKNOWN
Description: .. comment
        
        WTPHM
        *****
        
        The **W**\ind **T**\urbine **P**\rognostics and **H**\ealth **M**\anagement 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
        ----------
        
        .. start event comment
        
        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.
        
        .. end event comment
        
        SCADA/Operational data
        ----------------------
        
        .. start scada comment
        
        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).
        
        .. end scada comment
        
        Documentation
        =============
        
        Documentation and user guide can be found on readthedocs
        `here <https://wtphm.readthedocs.io/en/latest/>`_. A local copy of the docs can
        be built by running `<docs/build_docs.bat>`_ with sphinx installed.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
