CERTS Microgrid Test Bed Demonstration Data
The objective of the CERTS Microgrid Test Bed Demonstration, which was hosted by American Electric Power, was to enhance the ease of integrating small energy sources into a microgrid.
The project accomplished this objective by developing and demonstrating three advanced techniques, collectively referred to as the CERTS Microgrid concept,
that significantly reduce the level of custom field engineering needed to operate microgrids consisting of small generating sources.
The techniques comprising the CERTS Microgrid concept are:
(1) a method for effecting automatic and seamless transitions between grid-connected and islanded modes of operation;
(2) an approach to electrical protection within the microgrid that does not depend on high fault currents; and
(3) a method for microgrid control that achieves voltage and frequency stability under islanded conditions without requiring high-speed communications.
The test data is grouped by each phase of the project. Each phase of the project has an associated .xls file that contains a log of the test set-up, comments and the folder where the data is stored.
The waveform data for each test is stored in a .parquet file. These files can be easily imported using many standard programming languages including MATLAB and using the pandas library in Python.
The columns in each file are
- Datetime_ns: The datetime stamp of the measurement in nanoseconds.
- EventTime_s: The timestamp trigger of the recorded event in seconds. The meter typically records 0.25 seconds before the event and up to 5 seconds after the event trigger
- Va: The instantaneous voltage measured on Phase A as a % of the nominal voltage
- Vb: The instantaneous voltage measured on Phase B as a % of the nominal voltage
- Vc: The instantaneous voltage measured on Phase C as a % of the nominal voltage
- Ia: The instantaneous current though Phase A in amps
- Ib: The instantaneous current though Phase B in amps
- Ic: The instantaneous current though Phase C in amps
The data 'Datetime_ns' is timezone agnostic and can be converted to human-readable format for cross-referencing with the test logs using the pandas function pandas.Timestamp.