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/