1. Error Detection in the Frequency Monitoring Network (FNET)
Alexander Skwarczynski1, Jerel Culliss2, Dr. Liu2
Bearden High School1, University of Tennessee2
Introduction
The frequency monitoring network (FNET) is a useful tool for visualizing the frequency of the electric grid. In the US the grid is made to operate at a frequency of 60 Hz. All equipment is therefore made to operate close to 60 Hz. When too much power is generated or an outage occurs the frequency fluctuates and can cause have adverse effects on electronics. FNET tests new methods of analyzing and visualizing frequency data before those methods go to industry partners.
A map of frequency disturbance recorders (FDRs) that monitor the grid’s frequency.
Properly working unit
Research Question
Sometimes due to GPS signal loss, unit miscalibration, or faulty hardware the FNET sensors will transmit inaccurate data. Can a program be developed to detect errors for rapid fixes in order to decrease unusable data and possibly create a tool for industry.
Method
Errors come in two types: timing errors and frequency measurement errors. The sensors find the time of their measurements using a GPS connection and sometimes the units lose signal which makes the data faulty. The timestamp that the unit generates should follow a predictable pattern of increasing by one every tenth of a second. I set up an algorithm to compare the predicted timestamp to the measured timestamp and detect if it is faulty. To test frequency I developed an algorithm to create a channel that frequency should be within. The algorithm calculates the average frequency of an interconnection and sets upper and lower bounds from the deviation of the frequency data. If a unit is outside the channel then a counter increases. At the end of the analysis the counter is looked at and if a unit existed outside of the channel for a certain percentage of time it is considered a faulty unit.
Timestamp shift error
Over reactive unit
Unit not reacting correctly
Conclusion
The error detection program is able to detect both timing errors and frequency measurement errors with high accuracy. As more new data comes in the algorithms can be further refined to increase the error detection rate.
Acknowledgement
This work was supported primarily by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC and the CURENT Industry Partnership Program.
Acknowledgements:
This work was supported primarily by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC and the CURENT Industry Partnership Program.