NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Synchronized Phasor Data for 

Analyzing Wind Power Plant 

Dynamic Behavior and Model 

Validation

Y.-H. Wan

National Renewable Energy Laboratory

Technical Report

NREL/TP-5500-57342

January 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Synchronized Phasor Data for 

Analyzing Wind Power Plant 

Dynamic Behavior and Model 

Validation

Y.-H. Wan

Prepared under Task No. WE11.0930

Technical Report

NREL/TP-5500-57342

January 2013
NOTICE

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iii

List of Acronyms

AC alternating current

BPA Bonneville Power Association

DOE U.S. Department of Energy

EHV extra-high-voltage

GPS Global Positioning System

LRS Laramie River Station

LVRT low-voltage ride-through

MISO Midwest Independent Transmission System

Operator

NREL National Renewable Energy Laboratory

NERC North American Electric Reliability Council

NSP Northern States Power

OG&E Oklahoma Gas and Electric Company

PMU phasor measurement unit

PSC Public Service Company of Colorado

SCADA supervisory control and data acquisition

VFD variable frequency drive

WAPA Western Area Power Administration

WECC Western Electricity Coordinating Council

WPP wind power plant
iv

Table of Contents

List of Figures ............................................................................................................................................. v

List of Tables .............................................................................................................................................. vi

1 Introduction........................................................................................................................................... 1

2 Objectives.............................................................................................................................................. 2

3 Approach............................................................................................................................................... 2

3.1 Existing PMU Data From Utilities .................................................................................................. 2

3.2 New PMU Data From WPPs ........................................................................................................... 4

4 PMU Data Analysis ............................................................................................................................... 4

4.1 WAPA PMU Data ........................................................................................................................... 4

4.2 OG&E PMU Data .......................................................................................................................... 11

4.2.1 Transient Event Example ................................................................................................ 11

4.2.2 WPP Voltage Oscillations............................................................................................... 14

4.2.3 Voltage Transient and Oscillation................................................................................... 17

4.2.4 Subsynchronous Frequencies From WPPs...................................................................... 23

4.2.5 WPP Behavior During Grid Contingencies .................................................................... 27

5 Summary and Conclusions ............................................................................................................... 30
v

List of Figures

Figure 1. Locations of OG&E PMUs. ......................................................................................................... 3

Figure 2. Transmission system in the WAPA Colorado Missouri region.............................................. 4

Figure 3. PhasorFile graphic display example......................................................................................... 7

Figure 4. Spectrum analysis showing low-frequency oscillation. ......................................................... 8

Figure 5. Frequency disturbance example............................................................................................... 9

Figure 6. Power flow changes and voltage fluctuations associated with frequency disturbance... 10

Figure 7. Example of minor frequency and voltage disturbances....................................................... 10

Figure 8. Event of double frequency dips. ............................................................................................. 11

Figure 9. Wind plant output for a 24-hour period of June 10, 2011 UTC............................................. 12

Figure 10. Real and reactive power profiles during transient events.................................................. 13

Figure 11. Voltage profiles during transient events. ............................................................................. 13

Figure 12. Other voltage profiles during the second transient event.................................................. 14

Figure 13. WPP voltage oscillations. ...................................................................................................... 15

Figure 14. WPP output power during voltage oscillations. .................................................................. 15

Figure 15. Frequency analyses of the voltage signals during oscillation. ......................................... 16

Figure 16. Frequency analysis of WPP voltage signals before oscillation......................................... 16

Figure 17. Voltage oscillations at WPPs................................................................................................. 18

Figure 18. Wind plant output power profiles (12:27 – 13:00 UTC) ....................................................... 19

Figure 19. WPP voltage profiles during transient.................................................................................. 20

Figure 20. Real and reactive power profiles during voltage transient. ............................................... 21

Figure 21. Frequency components of voltage oscillations. ................................................................. 22

Figure 22. WPP voltage and spectrum prior to oscillation................................................................... 23

Figure 23. Subsynchronous frequencies at WPP during low wind speed period.............................. 24

Figure 24. Subsynchronous frequencies at WPPs during moderate wind speed.............................. 25

Figure 25. Subsynchronous frequencies at WPP at high wind speed. ............................................... 26

Figure 26. EHV line voltage profiles showing line outages.................................................................. 28

Figure 27. WPP voltage profiles during grid disturbance..................................................................... 29

Figure 28. WPP responses to loss of generation. ................................................................................. 30
vi

List of Tables

Table 1. WPPs with Synchronized Phasor Data....................................................................................... 3
1

1 Introduction

The U.S. power industry is undertaking several initiatives that will improve the operations of the 

power grid. One of those is the implementation of “wide area measurements” using phasor 

measurement units (PMUs) to dynamically monitor the operations and status of the network and 

provide advanced situational awareness and stability assessment.

Wind power as an energy source is variable in nature. Similar to other large generating plants, 

outputs from wind power plants (WPPs) impact grid operations; conversely, grid disturbances

affect the behavior of WPPs. The rapidly increasing penetration of wind power to the grid has 

resulted in more scrutiny of every aspect of wind plant operations and the demand that large 

WPPs should behave similarly to conventional power plants under normal and contingency grid 

conditions. The low-voltage ride-through (LVRT) requirement for WPPs is one such example.

Other proposed requirements include frequency response and simulated plant inertia.

To completely describe the system condition (state) of the electric power grid at any instant, it is 

necessary to know the voltage (V), current (I), and apparent power (S) of every point (node/bus) 

of the system. All three quantities in the alternating current (AC) power system are complex 

numbers that can be represented by phasors with both a magnitude and a phase angle. Out of the 

three phasor quantities, only two (any two) are needed to derive the third based on the equation 

S=VI*=P+jQ. The advance of computing power and worldwide availability of Global 

Positioning System (GPS) time signals make it possible for a PMU to measure voltage and 

current at a precise time and output these quantities in phasor form. GPS time signals can be 

accurate within 1 microsecond (μs) anywhere the signal is available. GPS time signals enable the 

synchronization of measurements across the very large distances that power system 

interconnections span. This new technology not only produces very accurate phasor

measurements but also enables synchronized measurements in the same instant.

Current PMUs can output synchronized phasor measurements at 60 Hz. The faster PMU data 

rate enables the observation and analysis of many grid and WPP dynamic behaviors that were 

not possible with the standard utility supervisory control and data acquisition (SCADA) system, 

which typically provides one measurement every 4 to 6 seconds (s). Because of precise timing 

and higher data rates, synchronized phasors provide much better information for investigating 

interactions between WPPs and the grid. This gives system operators and planners 

unprecedented insight into the grid. A new set of tools have been developed around PMUs and 

synchrophasor data that enable operators and engineers to make real-time system stability 

assessments and post-event analyses. The U.S. Department of Energy (DOE), North American

Electric Reliability Council (NERC), utilities, vendors, federal and private researchers, and 

academia are collaborating on the research, installation, and application of phasor data under the 

North American SynchroPhasor Initiative. However, although utilities are installing more than

1,000 new PMUs in the grid with support from the DOE Smart Grid Investment Grant, there is 

no concerted effort to put PMUs at large WPPs.

This task seeks to obtain PMU data from WPPs and grid reference points and develop software 

tools to analyze and visualize synchrophasor data to better understand WPP dynamic behaviors 

under normal and contingency conditions.
2

2 Objectives

The purposes of this task are to obtain PMU data from large WPPs and grid reference points and 

analyze the data for a better understanding of the dynamic interactions between WPPs and the 

grid under normal and contingency conditions. Such analysis will help system operators and 

turbine designers understand how a plant’s LVRT capability can survive a forced outage of the 

critical transmission line and under what conditions. Phenomena such as inter-area oscillations, 

system frequency response and damping, and other forced oscillations in the system can be 

studied in more detail over a much wider area. The PMU data will also be used for WPP model

validation. This task is part of our ongoing effort related to renewable energy plant modeling, 

documentation, and industry support. The three primary objectives are to:

1. Work with utilities and WPP operators to obtain PMU data;

2. Adopt and develop software tools to analyze and visualize PMU data; and

3. Use PMU data to validate the dynamic WPP models being developed at the National 

Renewable Energy Laboratory (NREL). 

3 Approach

Two parallel efforts were used to gain access to PMU data from WPPs and grid reference points: 

existing (historic) PMU data from utilities and new PMU data from WPPs.

3.1 Existing PMU Data From Utilities

Two utilities, Oklahoma Gas and Electric Company (OG&E) and Bonneville Power 

Administration (BPA), have expressly required PMU installations at all large WPPs as part of 

the interconnection agreement for their systems. We approached both, but we were able to reach 

a nondisclosure agreement with only OG&E to access its PMU data. OG&E has 94 PMUs 

installed in its system, including nine PMUs to monitor six of the seven WPPs in its system (the 

six monitored wind plants totaling 930 MW of installed capacity). We received data from 79

OG&E PMUs for the period from December 2010 through June 2012. This large amount of data 

will allow us to develop algorithms to detect unusual behaviors of WPPs. We will take advantage 

of the nondisclosure agreement and request additional data sets from OG&E. Figure 1 shows the 

locations of OG&E PMUs. The data rate from OG&E PMU is 30 Hz.
3

Figure 1. Locations of OG&E PMUs.

Table 1 below lists the six WPPs in the OG&E system where PMU data is available.

Table 1. WPPs with Synchronized Phasor Data

Name Transmission 

Voltage

Installed 

Capacity

Turbine 

Manufacturer

Keenan 138 kV 152 MW Siemens

OU WindSpirit 138 kV 101 MW Siemens

Centennial 138 kV 120 MW GE

Minco Wind 345 kV 200 MW GE

Crossroads 345 kV 228 MW Siemens

Taloga 138 kV 130 MW Mitsubishi

NREL also completed a confidentiality and nondisclosure agreement with the Western Area 

Power Administration (WAPA) to gain access to its PMU data. WAPA has five PMUs in its 

Colorado Missouri, Upper Colorado, and Lower Colorado Regions, located at the Ault (near 

Loveland, Colorado), Craig (near Craig, Colorado), Shiprock (near the Four Corners area in New 

Mexico), Yellowtail (southeast of Billing, Montana), and Mead (near Hoover Dam, Nevada) 

substations (Figure 2). None of these substations connects to or is near a WPP; however, the 

PMU data from them can provide adequate information about the electric system conditions in 

Colorado because of their locations in the high-voltage transmission grid, and they will be the 

grid reference points when PMU data from a Colorado WPP become available through our work 

with the Public Service Company of Colorado (PSC), an operating company of Xcel Energy for 

Colorado. (Details of working with the PSC are discussed in Section 3.2 below.) We received 

sample PMU data from WAPA. The data rate of WAPA PMU is also 30 Hz.
4

Figure 2. Transmission system in the WAPA Colorado Missouri region.

Northern States Power (NSP)—an operating company of Xcel Energy for Minnesota, Wisconsin, 

and North Dakota—is participating in the Midwest Independent Transmission System Operator 

(MISO) SynchroPhasor Deployment Project and will have 25 PMUs in its system when the 

installation is complete; two of them will be at WPPs. The MISO project will establish a 

synchrophasor infrastructure that will provide PMU data for the entire MISO reliability footprint 

and intermediary areas when completed. We are currently working on a research nondisclosure 

agreement with NSP and MISO to gain access to their PMU data.

3.2 New PMU Data From WPPs

NREL is in discussion with Xcel Energy to have a PMU installed at a substation in Colorado to 

obtain synchronized phasor data from a line connected to one of the WPPs in the PSC system. 

PSC does not have any PMUs installed in its system now, but it has indicated its willingness to 

work with NREL to provide PMU data. This work is planned for completion by the end of 

September 2013.

4 PMU Data Analysis

4.1 WAPA PMU Data

The five PMUs in the WAPA system provided the following phasor measurements:
5

Ault

• Ault-Craig: 345 kV line voltage

• Ault-Craig: 345 kV line current

• Ault–Laramie River Station: 345 kV line voltage

• Ault–Laramie River Station: 345 kV line current

• Ault–Fort Saint Vrain: 230 kV line voltage

Bears Ears

• Bears Ears–Craig: 345 kV line voltage

• Bears Ears–Craig: 345 kV line current

• Bears Ears–Bonanza: 345 kV line voltage

• Bears Ears–Bonanza: 345 kV line current

Shiprock

• Shiprock–San Juan: 345 kV line voltage

• Shiprock–San Juan: 345 kV line current

• Shiprock–Four Corners: 345 kV line voltage

• Shiprock–Four Corners: 345 kV line current

• Shiprock–Lost Canyon: 230 kV line voltage

• Shiprock–Lost Canyon: 230 kV line current

• Shiprock: 230 kV bus voltage

• Shiprock: 115 kV bus voltage

• Shiprock-Kayenta: 230 kV line current

• Shiprock–Hood Mesa: 115 kV line current

Yellowtail

• Yellowtail: 230 kV east bus voltage

• Yellowtail: 230 kV west bus voltage

• Yellowtail–KCB: 230 kV transformer current

• Yellowtail–PP&L: 230 kV tie-line current
6

• Yellowtail-Crossover: 230 kV line current

Mead

• Hoover: 230 kV south bus voltage

• Hoover: 230 kV north bus voltage

• Hoover #1: generator current

• Hoover #2: generator current

• Hoover #3: generator current

• Hoover #4: generator current

• Hoover #5: generator current

• Hoover #6: generator current

• Hoover #7: generator current

• Hoover #8: generator current

In addition to the above phasors, all PMUs also provided frequency and rate of frequency change 

(dF/dt) outputs.

The WAPA PMU binary data files NREL received were extracted directly from a phasor data 

concentrator in WAPA’s control center. The same data is transmitted to BPA as part of the 

Western Interconnection Synchrophasor Program implemented by the Western Electricity 

Coordinating Council (WECC) for real-time system monitoring. The data came to NREL via 

optical disks sent through regular mail or e-mail attachments because there is no secured 

communication link between the WAPA control center and NREL.

Each binary file contained 5 minutes (min) of data streams from all five WAPA PMUs. The 

binary files are in a proprietary format, and a software package called PhasorFile was used to 

convert the data into different formats that could be read and processed by other programs, such 

as MatLab and Excel. The PhasorFile program also has a basic graphing capability that allows 

users to visually scan the data set (any four quantities at a time). Figure 3 is an example of the 

graphic display. The PhasorFile program was developed by BPA and is publically available; 

however, this software package does not possess the ability of data quality check for missing or 

erroneous data. Also, the program does not detect any grid events (such as large voltage swing 

and frequency deviations) automatically. These capabilities need to be developed separately.
7

Figure 3. PhasorFile graphic display example.

Plot a in Figure 3 is the trace of frequency recorded by the Ault PMU during a 1-min window. 

Plots b and c are the voltage magnitude and phase angle of the Ault-Craig 345 kV line measured 

at Ault during the same 1-min window. Plot d is the phase angle difference between the voltage 

phasors at Ault and Shiprock. The traces in Figure 3 are typical patterns of voltage phasors and 

frequency in normal operating conditions. The three electric grids of North America (Canada and 

the United States) are very reliable and have very stable system frequencies. The maximum 

frequency deviation from peak to peak was only 0.02 Hz (or 0.033%). Only when an abnormal 

operating condition occurs do the system frequency, voltage, and current experience large 

changes.
8

The frequency in Plot a in Figure 3 shows some features of an oscillation. The results of a 

spectrum analysis on the frequency time series using discrete Fourier transformation routine 

confirm the existence of a low frequency (about 0.4 Hz) oscillation during this period (see Figure 

4). It turns out that this type of short-duration, low-frequency oscillation is not unusual in the 

PMU sample data we received. Inter-area oscillations in a large interconnected system are 

typically in this range (0.1 to about 1 Hz), and may always be present in the PMU data during 

normal operations of the system because of load switching in the system and the resulting slight 

differences in the response of generators in one subregion and another. It is not easy to detect 

exactly the source of such oscillations. Much more PMU data from different parts of the system 

is needed to pinpoint the cause.

Figure 4. Spectrum analysis showing low-frequency oscillation.

A routine to detect larger frequency deviation was developed to identify windows of data for 

disturbance analysis. After some experiments and discussions with BPA engineers, a threshold of 

60 mHz (the difference between consecutive frequency values) was chosen to flag data points in 

the frequency time series for further event analyses. A lower threshold would catch too many 

events that are not important, and too high a threshold may cause the routine to miss some grid 

events. The 60-mHz frequency deviation is still a relatively small value compared to the range of 

frequency fluctuations under normal operation conditions of the grid. For example, WECC’s 

Off-Nominal Frequency Load Shedding Plan does not require utilities to begin shedding load 

until the system frequency drops to 59.1 Hz (or 900 mHz off the nominal frequency of 60 Hz) 

for 1 min.1

The frequency deviation detection routine found one frequency event from the available WAPA 

PMU data. Figure 5 shows the frequency disturbance recorded by PMUs at the Ault, Shiprock, 

and Yellowtail substations. (PMU data streams from Bear Ears and Mead were bad during this 

time.) Plot a of Figure 5 shows frequency traces for 10 min; whereas Plot b shows frequency 

traces for the first 30 s of Plot a to display frequency disturbance in more detail. As shown, the 

 1 www.wecc.biz/library/Documentation Categorization Files/Retire and Archive/Off Nominal Frequency Load

Shedding Plan.pdf. It was approved by the WECC board of directors on March 17, 2011.
9

frequency at Ault experienced a relatively large initial drop of 600 mHz and some oscillations 

before it started to recover. The frequency at Yellowtail dropped about 270 mHz and oscillated 

briefly before it settled on a slow recovery trajectory. The frequency at Shiprock had a smaller 

drop (200 mHz) without oscillations. The frequency took almost 10 min to return to its 

predisturbance level.

Figure 5. Frequency disturbance example.

Although the exact cause of this frequency drop could not be determined with the available data, 

it was certainly associated with a sudden loss of generation. The PMU data set from Ault showed 

that the 400-MW flow on the 345 kV line from the Laramie River Station (LRS) to Ault was 

interrupted at the moment the frequency drop began. At the same time, flows to Ault on other 

lines increased. Figure 6 shows the changes in power flow to Ault and bus voltage fluctuations 

before and after the event—the 680-MW flow from LRS to Ault dropped to 0; the Craig-to-Ault 

flow reversed direction and increased to 378 MW; the flow from Shiprock to San Juan (in the 

direction toward Ault) increased to 140 MW (from 15 MW before the event). The voltage at the 

Ault substation 345 kV bus experienced a sharp dip, but the 345 kV bus voltage at the Shiprock 

substation was only slightly affected. This suggests that a generating unit at LRS had tripped. It 

also partially explains the measured frequency data shown in Figure 5. Ault and Yellowtail are 

closer to LRS than Shiprock; therefore, the frequencies measured at these two locations showed 

more fluctuations than the frequency at Shiprock. However, one cannot be sure if this scenario 

was true without examining other data, but the available information indicates that it was highly 

probable.
10

Figure 6. Power flow changes and voltage fluctuations associated with frequency disturbance.

Figure 7 and Figure 8 are two additional examples of frequency events detected in the WAPA 

data set. In the minor event shown in Figure 7, the frequency dipped briefly to 59.94 Hz, as did 

the voltages of the 345 kV buses in the WAPA system. The likely cause could have been a 

switch operation (opened and reclosed) somewhere in the WECC system. If synchrophasor data 

from WPPs were available during the same period, wind plant response to such disturbance 

could be analyzed.

Figure 7. Example of minor frequency and voltage disturbances.

The event in Figure 8 showed a second frequency dip about 170 s after the first one. The 

magnitudes of both frequency drops were relatively small. The voltage profile at the Shiprock 

345 kV bus confirmed that this is minor. The scale of the plot makes the voltage dip appear 

large, but the actual voltage drop was only 0.4%. Power flow on the Shiprock-to-Four Corners 

345 kV line suggests that the first frequency dip could have been caused by the loss of 

generating capacity in the Four Corners area. The loss of generation that triggered the second 

frequency dip likely occurred at a place farther away from the Four Corners area because there 

were no apparent changes in the line flow out of the Four Corners area.
11

Figure 8. Event of double frequency dips.

The real use of the WAPA PMU data is for it to be a grid reference point when we can obtain 

synchronized phasor data from a WPP in this region. No further analysis of the WAPA data will 

be performed until synchronized phasor data from a wind plant in Colorado is available.

4.2 OG&E PMU Data

PMU data from OG&E were delivered to NREL via an external hard drive. OG&E processed the 

raw data to include only positive-sequence line-to-neutral voltage, line current, frequency, and 

dF/dt from every PMU before sending it to NREL. Fifteen minutes of data from all PMUs were 

compressed and stored in a single file to reduce the storage space needed for the data. Despite 

these data reduction measures, 17 months of OG&E PMU data still requires about 2 TB of disk 

space. When fully decompressed and converted to text format (so other programs can access it 

without further processing), the data set requires 11 TB of disk storage space. Because of the 

disk storage limitation, we chose to decompress and convert the entire 2011 data set first for 

analysis in this report. OG&E also provided a list of known disturbances (breaker operations in 

its transmission system) during the 17-month period of delivered PMU data.

4.2.1 Transient Event Example

Figure 9 shows the real and reactive power from the Centennial WPP during the 24-hour period 

starting 0:00 June 10, 2011, UTC. The real and reactive power values are calculated from voltage 

and current phasors of a PMU at the grid interconnection point of this WPP. Each of these two 

traces in Figure 9 was based on 2,592,000 data points.

2 The size of the plot prevents it from 

showing many details. This particular day was chosen at random among all days that do not have 

a grid disturbance according to the disturbance list provided by OG&E to establish a baseline 

measure of normal wind plant operations as observed through PMU data. However, PMU data of 

the day revealed not only normal behavior of a WPP but also several events that are unusual.

 2 24 (hours/day) x 60 (seconds/hour) x 30 (data points/second) = 2,592,000 (data points/day)
12

Figure 9. Wind plant output for a 24-hour period on June 10, 2011, UTC.

As noted in Figure 9, this supposedly grid disturbance–free day contained three clearly 

distinguished events: twice the plant output dropped to 0 momentarily, and there was a 

seemingly sudden jump of output mid-day. Discussions with OG&E engineering staff provided

an explanation for the sudden jump of plant output around 13:56 UTC (8:56 a.m. CDT).

According to OG&E engineering staff, the relatively flat output of the Centennial WPP was 

because of curtailment. A 345 kV line, which was designed to carry wind power from the 

northwest region of Oklahoma (where the majority of wind power capacity is located), was 

damaged by a tornado on May 24 and was out of service until the evening of June 9. During that 

time, three wind plants in the general area (Centennial, Keenan, and OU WindSpirit) had to limit 

their outputs to avoid the lower voltage transmission lines in the area from being overloaded 

during high-wind periods. The 345 kV line was returned to service on June 9, 8:00 p.m. CDT,

and the curtailment order was lifted on June 10, 8:45 a.m. CDT. The three affected WPPs started 

to ramp up soon after. The output of Centennial WPP increased 40 MW in 5 min; however, the 

maximum ramping was only 12 MW per minute (or about 10% of the installed capacity per 

minute). The scale of the plot makes it appear to have been a much steeper increase.

Figure 10 shows the real and reactive power profiles during the other two transient events noted 

in Figure 9. Figure 11 shows voltage profiles. Scales in Figure 10 and Figure 11 are different 

from that of Figure 9 to show more details.
13

Figure 10. Real and reactive power profiles during transient events.

Figure 11. Voltage profiles during transient events.

During the first transient event—which occurred at 0:30:17.267 UTC (June 9, 7:30 p.m. CDT),

when the plant output dropped to near 0—the wind plant was still producing reactive power 

(indicated by the positive values of reactive power at the time of near-0 power), which suggests 

that whatever disturbance caused the plant power to drop to near 0 occurred outside the plant. 

Records from OG&E did not show any breaker operation during that time. The grid disturbance 

very likely occurred in the neighboring transmission system of the general area. It is also clear 

from the voltage profile that they were not breaker open-and-reclose operations because of the 

very short voltage dip durations (about 4 cycles), which were not long enough for a breaker to 

open and reclose. OG&E engineering staff stated that there was a weather front moving through 

the area during the time. It is very likely that those voltage transients were caused by lightning.

These conditions caused the turbine controllers to act to either shut down or reduce the power 

output of the turbines. It took about 4.3 s for the power to decrease to a low but fairly constant 

value, so we are certain the main breaker was not open and the wind plant stayed online. There 

was a small positive value of power (slightly more than 1 MW) during the period. A WPP of this 

size (120 MW installed capacity, as shown in Table 1) will draw 1 MW to 2 MW when no 

turbine is generating power because of parasitic losses associated with pad-mounted step-up 

transformers, cables, and all electronic monitoring and control equipment in the WPP.

During the second transient event—which occurred at 18:05:14.467 UTC (1:05 p.m. CDT),

when the plant output dropped to 0—the real power swing was larger and the reactive power was 
14

flowing into the plant (indicated by the negative value of the reactive power). This suggests that 

the disturbance was within the plant (behind the PMU at the grid interconnection point). OG&E 

records showed there was a fault on phase C of one of the circuits in the plant’s collecting system 

at the time of the transient, and the fault might have caused some of the turbines to shut down;

however, this type of localized fault should not affect the whole plant. It took about 4.5 s for the 

real power to decrease to a low value (less than 1 MW). The plant seemed to remain online, but 

most of its turbines were shut down.

The low power condition lasted slightly more than 3 min and the output power picked up again 

(not shown in Figure 10, but can be observed in Figure 9). Voltage profiles from other PMUs in 

the general area, including those at 345 kV buses, all showed a voltage dip at the same time. 

Figure 12 provides four voltage profile examples for the 15-min period surrounding the second 

transient event. PMU 114 signals are from Keenan WPP, and PMU 56 signals are from OU 

WindSpirit WPP; both are about 15 miles away from Centennial WPP. PMU 112 is at a 345 KV 

substation in the same area. (Outputs from all three WPPs feed into this substation.) PMU 1 is at 

a 345 kV bus north of Oklahoma City (100 miles away from the three WPPs). It seems unlikely 

that a single-phase fault on a low-voltage circuit within a WPP would affect voltages on faraway 

extra-high-voltage (EHV) transmission buses. However, the data suggests that that was what 

actually occurred. It is not clear which turbines were shut down and which remained online. It 

would require additional data from the plant SCADA system and fault recorder in the general 

area to clearly establish the sequence of the events and conditions of the system. For a transient 

of this magnitude, fault recorder data may not be available because the voltage dip might not 

have been deep enough to trip an event recording.

Figure 12. Other voltage profiles during the second transient event.

4.2.2 WPP Voltage Oscillations

PMU data can help operators detect subsynchronous oscillations in the system that are too fast 

for the SCADA system to capture. One example of such subsynchronous oscillation is shown in 

Figure 13, which plots the voltage signals from two WPPs during a 33-min window from April 

5, 2011, 12:27 UTC to 14:00 UTC (8:27 AM CDT to 9:00 AM CDT).
15

Figure 13. WPP voltage oscillations.

The voltage oscillations at both plants followed almost identical paths and lasted more than 25 

min unabated. The voltage oscillations had a peak-to-peak amplitude of 5.1 kV (3.7% of the 

nominal voltage of 138 kV). Figure 14 shows output power of these two WPPs during the same 

period. As shown, the plant output power also oscillated. It appears that the plant operators at 

Keenan WPP tried to damp the oscillation by reducing plant outputs as directed by OG&E 

control center operators. However the oscillations were not stopped until both plants 

significantly reduced their outputs: Keenan WPP output first dropped 20 MW (in 1 s), and 571 s 

later another 49 MW (in 2 s); OU WindSpirit WPP gradually dropped 48 MW over 5 min. Lower 

power output helped Keenan WPP reduce its power oscillation, but it did nothing to mitigate the 

voltage oscillation.

Figure 14. WPP output power during voltage oscillations.

0 200 400 600 800 1000 1200 1400 1600 1800 2000 40

60

80

100

120

PMU 56 (OU Windspirit-Woodward EHV) Power

Time (seconds)

Power (MW)

0 200 400 600 800 1000 1200 1400 1600 1800 2000 20

40

60

80

100

120

PMU 114 (Woodward EHV-Keenan) Power

Time (seconds)

Power (MW)

0 200 400 600 800 1000 1200 1400 1600 1800 2000 136

138

140

142

144

146

148

PMU 56 (OU Windspirit-Woodward EHV) Voltage

Time (seconds)

Voltage (kV)

0 200 400 600 800 1000 1200 1400 1600 1800 2000 136

138

140

142

144

146

148

PMU 114 (Woodward EHV-Keenan) Voltage

Time (seconds)

Voltage (kV)
16

It is not clear what triggered the oscillation. During that time, the OU WindSpirit WPP was 

producing near full capacity (97% of the installed capacity); whereas Keenan WPP was 

producing at a relatively lower level of 72% of its installed capacity. Figure 15 shows that both 

voltage and power oscillated at 13.33 Hz. The output power of Keenan oscillated at a smaller 

amplitude (as shown in Figure 14). Figure 16 shows the voltage signals and their frequency 

components of both WPPs 12 min before the start of the oscillation. The data did not show any 

grid event or transient prior to the oscillation. Both voltages had almost identical frequency 

spectrums. They contained prominent frequency components of 5 Hz, 5.46 Hz, 8Hz, 10 Hz, 12 

Hz, and 14 Hz. However, neither voltage signal before the oscillation contained the 13.33-Hz 

frequency. The components around 5 Hz might have been caused by variable frequency drive 

(VFD) motors in the nearby industrial plants, but the 8 Hz, 12 Hz, and 14 Hz appeared to be 

inherent to the wind turbines. Both WPPs have Siemens wind turbines. According to OG&E 

engineers, the turbine manufacturer was notified of this event, and it modified the turbine 

controller logic in an attempt to damp such oscillations.

Figure 15. Frequency analysis of the voltage signals during oscillation.

Figure 16. Frequency analysis of WPP voltage signals before oscillation.

There were several incidents of WPP voltage oscillations after the April 5 event. The subsequent 

voltage oscillations were either 12.44 Hz or 13.33 Hz, and their amplitudes were either greater or

smaller than that in this case. One thing in common about the observed voltage oscillations was 

the high level of wind plant outputs (more than 80% of the installed capacity) when voltage 

oscillation occurred. The available data shows that when the oscillation amplitudes (peak-to0 5 10 15 0

2000

4000

6000

PMU 56 (OU Windspirit-Woodward EHV) Voltage Spectrum

Frequency (Hz)

|Y(f)|

0 5 10 15 0

2000

4000

6000

8000

10000

PMU 56 (OU Windspirit-Woodward EHV) Power Spectrum

Frequency (Hz)

|Y(f)|

0 5 10 15 0

1000

2000

3000

4000

5000

PMU 114 (Woodward EHV-Keenan) Voltage Spectrum

Frequency (Hz)

|Y(f)|

0 5 10 15 0

1000

2000

3000

4000

5000

PMU 114 (Woodward EHV-Keenan) Power Spectrum

Frequency (Hz)

|Y(f)|

0 100 200 300 400 500 600 700 142

142.5

143

143.5

144

PMU 56 (OU Windspirit-Woodward EHV) Voltage

Time (seconds)

Voltage (kV)

0 5 10 15 0

10

20

30

40

50

Single-Sided Amplitude of Spectrum

Frequency (Hz)

|Y(f)|

0 100 200 300 400 500 600 700 141

141.5

142

142.5

143

PMU 114 (Woodward EHV-Keenan) Voltage

Time (seconds)

Voltage (kV)

0 5 10 15 0

10

20

30

40

50

Single-Sided Amplitude of Spectrum

Frequency (Hz)

|Y(f)|
17

peak) exceeded 5% of the nominal voltage, the WPP operators reduced the plant output (as

directed by OG&E control center operators) in a fashion similar to that shown in Figure 14 to 

abate the oscillation.

4.2.3 Voltage Transient and Oscillation

The analyses of PMU data from OG&E and discussion in the previous section indicate that 

voltage at some WPPs may have a tendency to oscillate at a frequency around 13 Hz and 14 Hz 

under certain wind conditions. Small voltage oscillations of different frequencies appear to be 

present at WPPs constantly. A grid disturbance in or around the wind plants may excite one or

more of these small voltage oscillations and make them more prominent. The most common 

frequency of these types of oscillations is between 13 Hz and 14 Hz. These types of voltage 

oscillations are generally not observed on SCADA signals because of the low data rate of one 

data sample for every 4 to 6 s. However, nearby utility customers may experience flicker when a 

voltage oscillation occurs. The oscillations die down when wind conditions change, and the 

turbine controller also tries to damp it. Another one such voltage oscillation incident occurred on 

December 14, 2011. Figure 17 shows the voltage signals from five WPPs on December 14, 2011, 

from 12:27 to 13:00 UTC (6:27 a.m. CST to 7:00 a.m. CST).
18

Figure 17. Voltage oscillations at WPPs.

Voltage oscillation was seen in four out of the five WPPs during this 33-min period. Only Minco 

WPP, which is connected to 345 kV line, did not experience voltage oscillation during this time.

Oscillations at OU WindSpirit and Keenan WPPs had higher amplitude–3.0 kV peak-to-peak or 

2.2% of the nominal voltage. As shown, there was a voltage transient at 12:33:23.333 UTC. This 

particular voltage transient was observed throughout the OG&E system except for the very east 

end, where it is interconnected with Entergy at a 500/345 kV substation at the 

Oklahoma/Arkansas border. Physically, OU WindSpirit, Keenan, and Centennial WPPs are 

closer together and are further northwest in relation to the location of Taloga WPP. The Minco 

WPP is further south in relation to Taloga. The amplitudes of voltage oscillation became smaller 

the further south the WPPs are located. The southernmost WPP connected to a 345 kV line did 

not experience voltage oscillation. Figure 18 shows the real power profiles of these five WPPs 

during this time. The voltage transient showed up in output power of the four WPPs connected at 
19

138 kV, but not the output power of Minco WPP. Similar to the oscillation event discussed in the 

previous section, WPP output power levels were also high in this event. During this period,

output at OU WindSpirit was about 82% of its installed capacity; Keenan was at 87% of its 

installed capacity; Minco WPP was higher, at 94% of its installed capacity; Taloga WPP was at 

90% before the sudden drop; and only Centennial WPP was at a relatively lower level of 33% of 

its installed capacity.

Figure 18. Wind plant output power profiles (12:27–13:00 UTC).

The voltage transient did not appear to have a noticeable effect on the power trace of Minco 

WPP, which is connected to the grid at 345 kV, but it showed up in the power traces of four

other WPPs connected to the grid at 138 kV, and it actually triggered the shutdown of half the 

turbines at Taloga WPP. The Taloga WPP was generating at about 117 MW when the transient 

occurred. After the start of the transient, Taloga WPP output dropped to about 59 MW in 2 s 
20

(almost exactly half the level before transient). It was obvious the transient caused the output 

drop (and not the other way around) because of the 2-s delay in power drop. Figure 19 shows the 

voltage profile of the four 138-kV connected WPPs in a 4-s window to show more details. The 

very short duration of the voltage transient suggests that the transient was likely caused by a 

lightning strike on the transmission line. It was noted that voltage profiles of the four WPPs 

during the transient were very similar, including the oscillation behavior.

Figure 19. WPP voltage profiles during transient.

Figure 20 shows the real and reactive power profiles of the four WPPs in detail. The P and Q 

plots showed turbines by different manufacturers had different responses to a system transient 

despite the similarity in their voltage profiles. When voltage at a WPP begins to drop, the normal 

response for the turbines is to increase reactive power output in an attempt to hold up the voltage.

There may be minor variations in how manufacturers implement this basic scheme. At the OU 

WindSpirit and Keenan WPPs (both have Siemens turbines), the reactive power initially dropped 

(in this case drawing more reactive power from the grid) along with the real power before it 

picked up. Reactive power at Centennial (with GE turbines) and Taloga (with Mitsubishi 

turbines) began to increase as soon as the voltage began to drop. The differences in reactive 

power were also reflected on the voltage profiles shown in Figure 17. Both Keenan and OU 

WindSpirit WPPs experienced a voltage drop of 21% in magnitude (compared to the beforetransient value); whereas Centennial WPP experienced a 15% voltage drop and 17% at Taloga 

WPP. The voltage transient event did not cause the WPPs to go off-line, and the oscillations 

were stable.
21

Figure 20. Real and reactive power profiles during voltage transient.

Figure 18 and Figure 19 showed the voltage oscillations after the transient event. Smaller voltage 

oscillations actually appeared before the transient, and the oscillations became more prominent 

and lasted longer after the transient. The reactive power also displayed oscillation behavior 

because it was closely linked to voltage. Figure 21 plots the results of spectral analysis of WPP 

voltage signals during the 33-min window shown in Figure 17.

There were several prominent subsynchronous frequencies in the voltage signals of these four 

WPPs. The most noticeable group of higher frequency components in Figure 21 were 13.33 Hz, 

14 Hz, and 14.67 Hz. The frequencies of 14.67 Hz and higher were the artifacts of aliasing of 

fast Fourier transformation calculation. The 13.33-Hz oscillation, which was present during the 

event discussed in the previous section, showed up again in this event. After the April 5, 2011, 

oscillation incident, the turbine manufacturer modified the turbine controller to damp the 13.3 Hz 

oscillation. However, under similar wind conditions (high WPP outputs), the voltage still 

oscillated at this frequency, albeit at a lower amplitude. The difference between the April 5 event 

and this event was the voltage transient, which seemed to excite the generators to oscillate at 

13.33 Hz and 14 Hz. Additional data and research will be needed to determine why WPPs 

oscillate and how the oscillations were damped.
22

Figure 21. Frequency components of voltage oscillations.

Frequency analysis of the voltage signals before the transient and oscillation again showed 

familiar subsynchronous frequency components, such as 8 Hz, 10 Hz, 12 Hz, and 14 Hz. Figure 

22 plots the voltage profiles and their spectral components of the five WPPs for the 12-min 

period from December 14, 2011, 13:15 to 13:30 UTC (before the start of the voltage oscillation).

The four WPPs connected to the grid at 138 kV (OU WindSpirit, Keenan, Centennial, and 

Taloga) had very similar frequency spectrums. The Minco WPP, which connected to the grid at 

345 kV, had a significantly different frequency spectrum. It is also shown that the spectrum of 

the voltage signals of OU WindSpirit and Keenan were very similar to that shown in Figure 16.

0 5 10 15 0

50

100

150

200

250

300

OU WindSpirit

Frequency (Hz)

Single-Sided Amplitude

0 5 10 15 0

50

100

150

200

250

300

Keenan

Frequency (Hz)

Single-Sided Amplitude

0 5 10 15 0

50

100

150

200

250

300

Keenan

Frequency (Hz)

Single-Sided Amplitude

0 5 10 15 0

50

100

150

200

250

300

Taloga

Frequency (Hz)

Single-Sided Amplitude
23

Figure 22. WPP voltage and spectrum prior to oscillation.

Figure 22 reinforces the observations from WPP PMU data that several subsynchronous 

frequencies are inherent to wind turbines installed at these WPPs. Three turbine manufacturers 

(Table 1) are present here, but only two types of turbine designs: Type 3 wind generators with 

doubly-fed induction generators and power converters by GE and Mitsubishi and Type 4 wind 

generators with variable-speed generators and full AC-DC-AC conversion by Siemens. The 

subsynchronous frequencies that originated from Minco WPP were less prominent because of the 

inherent strength of the 345 kV EHV transmission grid. At the 345 kV level, the pair of 

subsynchronous frequencies around 5 Hz became prominent.

4.2.4 Subsynchronous Frequencies From WPPs

Figure 23 shows the voltage profiles and frequency components of six WPPs in the OG&E 

system under normal conditions. The original voltage signals are from the 15-min window 

beginning December 6, 2011, 23:30 UTC (December 6, 2011, 5:30 PM CST). During that time,

all six WPPs were online but at very low power level, between -1 MW (drawing power from 
24

grid) to +0.1 MW (generating barely enough power to overcome its own losses). These low 

power levels indicate that the power converters (most likely sources of subsynchronous 

frequencies) of the turbines were active. Another factor for selecting data from this period is that 

there were no grid transient events or breaker operations. The purpose here is to establish a 

baseline pattern of subsynchronous frequencies from WPPs.

Figure 23. Subsynchronous frequencies at WPP during low wind speed.

During low wind speed periods, the inter-area oscillation mode frequencies from the grid 

dominated the spectrum. The most obvious was 0.48 Hz, which is typical of inter-area power 

oscillations and the frequencies around 5 Hz that are associated with VFD motors. Although very 

small, the 11-Hz and 12-Hz components associated with WPP can still be detected in Figure 23.
25

As the WPP output power increased, the subsynchronous frequencies from turbines began to

appear, as shown in Figure 24. It shows voltages and their frequency spectrums during a 15-min 

period of moderate wind speed, which in this case was defined as the time the WPP outputs were 

at about 26% of their installed capacity. As shown, the subsynchronous frequencies associated 

with inter-area oscillations and VFD motors were still present, but the 10- to 12-Hz components 

became more obvious. 

Figure 24. Subsynchronous frequencies at WPPs during moderate wind speed.

At higher wind speed and higher plant output levels, the above-10-Hz frequency components 

become even more prominent. Figure 25 shows WPP voltages and frequency spectrums during a 

15-min period when plant outputs were more than 80% of installed capacity. In addition to 10-, 

11-, and 12-Hz components, the 14-Hz component also appeared. The spikes in the voltage 

traces of the four WPPs at 138 kV appear to have been the result of a transformer tap change 
26

somewhere in the system; however, unlike the cases discussed in the two previous sections, 

outright voltage oscillations did not occur in this case.

Figure 25. Subsynchronous frequencies at WPPs during high wind speed.

The three examples of WPP voltages and their spectrums in this section showed that 

subsynchronous frequency components were present at WPPs, and their amplitudes would 

increase along the plant outputs. It is also clear from the available data that when a WPP is 

connected to the grid at 345 kV, it is less likely to experience voltage oscillation. Under exactly

what conditions those subsynchronous frequencies will develop into voltage oscillation is not 

clear. This will be an area for further research.
27

4.2.5 WPP Behavior During Grid Contingencies

One of the objectives of collecting PMU data from WPPs is to study the behaviors of WPPs 

during grid contingencies such as a loss of a major generation and loss of a major system 

component (transformers, EHV lines, etc.). The available data so far does not contain an event of 

major generation loss (i.e., an entire large generating plant tripping off-line). It does contain 

many incidents of individual generating units of a plant tripping off-line and outages of EHV 

lines. Two such events are analyzed in this section to illustrate if such so-called N-1 

contingencies affect WPP operations.

The first case was an example of EHV-line outages during a thunderstorm (with tornados 

reported) that occurred on May 24, 2011, around 17:00 CDT. The thunderstorm caused many 

outages in the OG&E transmission system in the area. In addition to the outages of lower voltage 

transmission lines, OG&E records show that three EHV line segments in relatively close vicinity 

suffered some damages and tripped open within 17 min of each other. Figure 26 shows the 

voltage profiles of the three line segments and a reference line segment that is 80 miles away

within a 20-min window. The first 345-kV line segment opened at 16:48:50.167 CDT; the 

second 345-kV line segment opened 5 min later, at 16:53:23.343 CDT, after a failed reclose 

attempt (the scale of Figure 26 cannot display the reclose operation); and 10 min later, at 

17:03:5.063 CDT, the third 345-kV line segment opened, following a failed reclose attempt. The 

outages of these three line segments produced noticeable but relatively minor voltage sags on the 

reset of the transmission system. The voltage profile of the reference Draper-Seminole line in 

Figure 26 is an example.
28

Figure 26. EHV line voltage profiles showing line outages.

During this time, all WPPs were online and were producing at moderate levels ranging from 27% 

of the installed capacity at Minco WPP to 68% of the installed capacity at Centennial WPP. 

Figure 27 shows the voltage profiles of the four WPPs during the same 20-min window.3 WPPs 

did experience voltage transients when EHV lines were opened, but their operations were not 

adversely affected.

 3 Wind plant PMU data quality during this particular 20-min window was somewhat suspicious—e.g., data from 

PMU 114 and 115 were almost (but not exactly) identical. However because data streams before and after this 

period appeared to be normal, data from this period was considered adequate.
29

Figure 27. WPP voltage profiles during grid disturbance.

Although there were many breaker operations during this period that resulted in EHV 

transmission line outages, it appeared that those outages did not depress the voltage profile 

enough and the transmission system was not seriously stressed to the point to really test the 

LVRT capability of the wind turbines. As shown in Figure 27, the grid contingency caused the 

voltage at the WPP to drop to 84% of the nominal level. It was outside the normal operation 

range, but it was far from the required LVRT level, which is 0.

4

The available data contained several events of loss of generation. The second case analyzed was 

an example of this. A significant event occurred on April 27, 2011, at 21:36 UTC, when all three 

units at Browns Ferry nuclear plant were tripped because loss of external power resulted from

damages to power lines by a tornado. This event, although severe, was remote (in Alabama);

therefore, its impact on the WPPs within the OG&E system was relatively minor. The event 

showed up on frequency and voltage signals at the WPPs, but the plant operations apparently 

were not affected. Figure 28 shows the frequency, voltage, and power traces of the three WPPs 

where PMU data were available at the time. The frequency in these parts of the system dropped 

0.1 Hz by this event, and it took more than 200 s for the system frequency to recover. A

 4 Joos, G. “Wind Turbine Generator Low Voltage Ride Through Requirements and Solutions.” IEEE Power and 

Energy Society General Meeting, July 20–24, 2008.
30

momentary voltage dip was also evident. However, WPP output power was not affected by this 

event. Power output from the WPPs did not respond to system frequency changes, which was 

expected because of current state-of-art turbine technologies.

Figure 28. WPP responses to loss of generation.

There were other loss-of-generation events in the available PMU data, but all of them were 

minor in terms of the amount of generation lost compared to the system load and available 

generation on-line. Consequently, the responses of WPPs were the same as that of this event.

5 Summary and Conclusions

The availability of synchronized phasor data from PMUs offers unprecedented opportunity for 

observing and analyzing the WPP operations under normal and grid-contingency conditions. The 

analyses of PMU data from OG&E provided several noteworthy results.

The most noticeable finding is the tendency of certain WPPs to oscillate. The data suggests that 

certain subsynchronous frequencies may be present in such WPPs all the time, and grid 

disturbances may excite one or a group of these subsynchronous frequencies to begin to oscillate 

during high-wind periods. It appears that Siemens turbines have a higher tendency to oscillate,

but GE and Mitsubishi turbines are not totally immune from this behavior. It is not clear from the 

available PMU data why this is the case. It is also not clear if this is unique to WPPs in the 

OG&E system because we do not have PMU data from other WPPs outside the OG&E system.
31

Results show that the WPPs will routinely ride through grid disturbances as designed. Lightning 

strikes, line outages, and loss of generation will cause WPP voltage to sag and swing in a way 

not very much different from the responses of other types of generators. There was a case in the 

data set in which one of the WPPs tripped off-line during a high-wind period; however, the 

OG&E disturbance list did not indicate any breaker operations or grid event at the time and there 

was no oscillation. No conclusion could be drawn from that event. The other events included in 

the available data set were not severe enough (in terms of magnitude of voltage drop and 

duration) to really show how WPPs will perform under more severe conditions. Additional data 

and analyses are needed to provide a more quantitative answer.

The results also point to the critical need for accurate WPP models and the knowledge of turbine 

controller logic and controls. The PMU data showed how WPPs behave under different

conditions, but to know why WPPs behave in these ways we need good WPP models to tell us.

The ultimate goal of analyzing WPP PMU data is to improve WPP dynamic performance under 

normal and grid contingency conditions. Using PMU data to better understand WPP behavior is 

only the first step in this process. We will also need to work with turbine manufacturers on this 

aspect.

Future work will include analyzing additional PMU data to identify more severe events under 

different situations. The example in Section 4.2.1 serves as a good example of how much 

information can be obtained from the PMU data. We will also try to obtain PMU data from other 

WPPs in other regions and of turbines by different manufacturers to broaden our database and to 

further our understanding of WPP oscillations.