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3D Animation of Power System Data
Sean Indelicato, Blair Johnson, Shutang You
Abstract: The goal of this project was to create a 3D model of the electric power grid
using data from FNET. This form of visualization allows for better analysis of disturbances. The
program we developed in MATLAB can read this FNET data and interpolate it on a map using
the latitude and longitude of the frequency disturbance recorders. We were able to gain better
insight into the dynamics and trends of the power grid through observation of the visualized data.
Location of disturbances and propagation speed of instability were both easy to estimate using
this 3D method of visualization.
Key words: Frequency, Angle, FNET, FDR, MATLAB, visualization
1. Introduction
FNET is a frequency monitoring system. It uses the Frequency Disturbance Recorder
(FDR), as shown in Fig.1, as its sensor. FDRs spread across the United States as well as other
parts of the world. The FDRs measure the change in frequencies to the nearest ten-thousandths
place. The frequency should be very close to 60.00 Hertz, as that is the standard in the United
States. Each FDR can measure the frequency and voltage angle at household outlets while
timestamping the measurements by GPS information. The accurate GPS timestamp realized
wide-area synchronized measuring of the power grid.
The US power grids consist of three synchronized interconnections: the Eastern
Interconnection, WECC, and ERCOT. Fig.3 shows the FDR deployment map in the three
interconnections in the U.S. The data collected by FDRs are transmitted to data centers running
at the University of Tennessee and Oak Ridge National Laboratory.
Figure 1. Frequency Disturbance Recorder [2] Figure 2. FNET system architecture [1]
Figure 3. FNET FDR Deployment Map [1]
The data allows us to triangulate the location of any disturbance in the grid, and generate
a map to visualize how a disturbance affects the grid. A disturbance is caused by either a fault of
the network, a sharp increase of load, or a malfunction in a power plant that causes less
electricity to be available to the consumer. The frequency is an indicator of the balance between
the energy producers and consumers. A frequency higher than 60Hz usually means a generation
surplus while a lower than 60 Hz frequency means generation insufficiency. Our objectives
included visualizing the frequency and voltage angle data from FNET as an animated 3D contour
map, creating an automated program, and understanding how this new imaging can provide
better insight into the electrical grid.
2. Visualization of power system measurements
Frequency disturbance recorders provide four important types of data: Location of the
FDR, frequency of the grid at the FDR’s location, voltage angle at the FDR’s location, and the
exact time of frequency and voltage angle readings. This data is critical to the visualization
process. The information is recorded in the format of a text file, as shown in Fig.2.
Figure 2. Format of the recorded FDR data
2.1. Methods
We wanted our program to be a versatile, automated, and efficient as possible. The
program needed to be capable of automatically importing and sorting the FDR data, visualizing
frequency and voltage angle data, and storing and recalling previously run animations. The
workflow of the project followed the following outline.
Figure 4. The structure of the MATLAB animation program
1) With help from our mentor, we added code to the program that imports data from the
FDRs into MATLAB and sorts it into variables. The complete automation of this process
was not achieved, as errors such as missing data, and FDR location data needed to be
corrected.
2) A script was written to associate the FDR data with the corresponding FDR locations
within a unified matrix.
3) A grid was then constructed using the latitude and longitude values from the imported
FDR location data. This grid was created using a “meshgrid” function.
4) The FDR data was then interpolated over the previously created meshgrid using the
“griddata” function.
5) The “vec2mtx” function and the built in “coast.mat” file were used to create a logical
matrix in the shape of the United States’ coastline. The matrix was then saved as a .mat
file in the project folder where it could be read from as needed. This greatly improved the
efficiency of the program.
6) The coastile matrix was compared to the interpolated FDR data, and the data points that
fell outside of the coastline were removed.
7) The “surf” function was then used to create a 3D contour map of our data. This “surf”
function was placed inside a FOR loop to animate it, with each tenth of a second of data
corresponding to a single frame, and each frame being stored in cell matrix. This matrix
was then saved as a .mat file in the project folder until manually reset.
8) When run, the program retrieves and plays each frame from memory. This enables the
animation to play much more smoothly than if it were being calculated in real-time.
9) Each individual case was then manually debugged. This was done by running the
program until it encountered an error and then deleting the FDR with missing data, or
manually adding location data for FDRs with missing GPS locations.
10) A “line” function was used to draw a 2D map of the U.S. state borders on the ideal z axis
value. For the frequency graphs this map was at 60hz. On the phase angle graph, this
reference point was placed at 0 radians.
Visualization of phase angle measurements required more processing, as unlike the raw
frequency data, the raw angle data is unusable in its original state. In addition, further processing
was required to isolate small oscillations in the phase angle data.
1) Phase angle data was unwrapped using the “unwrap” function.
2) The mean of the unwrapped angle data was then calculated and stored.
3) The difference between the unwrapped data and the mean was found and visualized using
the same method as frequency data.
4) To isolate oscillations in the phase angle data and remove noise, the “memd” function
was used to decompose the difference from the mean phase angle data into component
channels. Only the channels containing the smaller oscillations were stored and
subsequently visualized using the previous method.
2.2 Results
The completed program displayed a 3D animated contour map of the United
States. The x and y axes represented latitude and longitude, and the z axis represented either
frequency in hertz or angle deviation from the mean in radians. This 3D method of visualizing
the data made locating disturbances very easy, and in every case tested, we could clearly see the
beginning of the disturbance and the propagation of instability across the grid. Using a 2D plot of
our data, it was easy to find the time of the location as each frame corresponds to .1 seconds.
Using the location and time of the fault it was possible to estimate the propagation speed in miles
per second using the formula:
Ps = (La - Le) / ( (Ta - Te) * .1)
where La is the location of a given point of arrival, Le is the location of the event, Ta is the time
of arrival, and Te is the time of the event. Analyzing the animated contour map allowed us to
identify which regions of the power grid were more susceptible to instability than others.
Figure 5. 2D plot of frequency data (left), and 3D plot of frequency data (right)
Figure 6. 2D plot of phase angle data (left), and 3D plot of phase angle data (right)
Figures 5 and 6 show data from the same case. On April 27, 2011, several large hail
storms hit East Tennessee. These storms took out eleven 500kv transmission lines. The storm
resulted in numerous extended outages and damage to roofs and cars. Examination of the 3D
map in Figure 6 reveals large phase angle oscillations beginning in East Tennessee and spreading
outward. It is also apparent that aside from the location of the initial disturbance, the midwest
and Florida are particularly susceptible to large oscillations.
4. Conclusions
The use of 3D animation provides a superior method of viewing and analyzing power
system data. This kind of visualization allows the user to easily see the time and location that an
event occurred, as well as estimate the propagation speed of a disturbance. The use of 3D
visualization also makes it easy to observe the dynamics of a power system, as well as trends
such as major oscillation regions.
Acknowledgements:
Special thanks to Dr. Chen and Erin Wills.
References
[1] FNET Web Display. University of Tennessee. http://fnetpublic.utk.edu/
[2] Frequency Disturbance Recorder. CURENT.
http://curent.utk.edu/contact-us/facilities/university-of-tennessee/
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