Initially Presented at 72nd Georgia Tech Protective Relay Conference

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AEP Experience with Sub-Synchronous Oscillation

Phenomena

Zachary Campbell

Transmission PCE

Standards & NERC CIP

American Electric Power

New Albany, Ohio,

United States

zpcampbell@aep.com

Kiril Andov

Advanced Transmission

Studies & Technologies

American Electric Power

New Albany, Ohio,

United States

kandov@aep.com

Shawn Coppel

Transmission PCE

Standards & NERC CIP

American Electric Power

New Albany, Ohio,

United States

scoppel@aep.com

Abstract— Recent installations of renewable energy resources

in combination with series capacitor bank installations on AEPs

network has renewed concerns about the power system

phenomenon known as subsynchronous control interaction

(SSCI) and subsynchronous resonance (SSR) on the AEP

electrical network. In this paper, historical events which have

occurred on the AEP transmission electrical network are

illustrated which demonstrate the frequency and magnitude of

the subsynchronous oscillation, the network conditions, and the

protection system modifications made following the events.

These events are further analyzed to support the development of

a generic subsynchronous oscillation (SSO) detection relay

algorithm. The signal processing techniques used to do this are

illustrated, analyzed and the relay detection algorithm is

demonstrated through simulation.

Keywords—sub-synchronous oscillation, American Electric

Power, sub-harmonic, oscillations, SSTI, SSCI, SSR

I. INTRODUCTION

AEP is one of the nation’s largest electrical utility providers with more than 26GW of generating capacity, 40,000 miles of transmission lines, and nearly 5.4 million customers operating within 11 states [1]. AEP also operates and maintains 9 series capacitors and dozens of customer operated windfarm and solar interconnections. Traditionally, the power system network combination of a turbine-generator shaft driven system and series compensation would necessitate specific analysis to uncover the potential for forced oscillations of the shaft system, commonly referred to as subsynchronous resonance (SSR) [2]. Most notably, the shaft failures at the Mohave Generating Station in Southern Nevada drew specific attention to this power system phenomenon. Recently, the increased penetration of renewable energy resources in combination with series compensation on the AEP network has renewed similar concerns. This is because of the likelihood for similar problems, like SSR, to exist where certain electrical network configurations or conditions can cause renewable generation resource instability [3]. In these cases, it is not a traditional shaft driven mechanical system that could be damaged due to the subsynchronous content. Instead, the instability of the renewable generation resource can generate a combination of nominal and subsynchronous voltages applied

to the power system network which can result in system voltages or currents so large that damage to both network and renewable generation resource occurs. This phenomenon is sometimes referred to as subsynchronous control instability (SSCI). Because of this possibility, there is again a need to study the conditions which can drive this phenomenon. In these cases, however, some of the traditional methods used to analyze the traditional turbine-generator shaft driven system are not applicable. In these cases, the analysis performed in determining the possibility of problems resulting in the combination of renewable energy resources and series compensation uses a ‘black-box’ model of the renewable generation resource, wherein the understanding of the dynamic behavior of the renewable energy resource is obscured. This is purposeful due to the proprietary nature of the models of the interconnecting entity, but this obscurity limits the understanding of the causes of any potential instability. Regardless, special analysis is performed which attempts to uncover any SSCI phenomena which could occur. While it is not the goal of this paper to inform the user of the analysis methods used in uncovering the system conditions which generate SSCI, it is the goal of this paper to demonstrate various events which have occurred on the AEP network which demonstrate the phenomena. Because it is currently thought that SSR or SSCI is a rare system occurrence [4], it is rarely considered by protective relay engineers who operate within a unique role in which the potential detection of and remedial action to SSCI is within their expertise.

II. AEP SYSTEM HISTORICAL EVENTS

A. Event 1

On October 22, 2009 a fault on an AEP line in south-

eastern Texas ultimately resulted in an event in which wind

turbine and electrical network damage occurred. In this case,

the network in the vicinity of the fault consisted of two 345kV

transmission lines, two windfarms, and a series capacitor bank

positioned on one of the transmission lines. A simplified

network diagram for this system is shown in Fig. 1. The fault

caused the station A to station L line to de-energize which

resulted in the radial connection of both windfarms

infrastructure into the series capacitor bank. The result of this

Pg. 2

system configuration and initial disturbance is shown in Fig.

2, which illustrates the station Z to station A line relay

oscillography data captured. As can be seen in the figure,

following the initial fault, system voltages rose to nearly 1.5

PU. However, the frequency content of the currents and

voltages during the second half of the event of Fig. 1 was not

made up of only 60Hz information. In fact, as shown in Fig.

3, the frequency content of the current was made up of more

than 1000A of 25Hz content and less than 500A of 60Hz

content, while the voltages were made up of nearly 200kV of

60Hz content and nearly 100kV of 25Hz content. It was

thought, at the time that, damage to infrastructure was caused

when system voltages far exceeded the electrical ratings of

system infrastructure in the area of the event [5].

Windfarm 1

Windfarm 2

Station Z Station A67 miles

37 miles

7 miles

13 miles

Station L

Station R

345kV Fig. 1 Event 1 Simplified Electrical Network

Fig. 2 Event 1 Oscillography Record

Fig. 3 Event 1 FFT Spectrum Analysis of Phase A Voltage and Current

The event ended after nearly 1.5 seconds when the series

capacitor bypassed. The event was recreated through

simulation using a detailed PSCAD model to attempt to excite

to subsynchronous mode of oscillation. Fig. 4 illustrates the

PSCAD model simulation results from the development

efforts made to replicate the event. The phenomenon was

attributed to SSCI between the network and the windfarms [3].

This event demonstrated the need for the detection of the SSCI

conditions and justified the installation of a relay specifically

dedicated to sensing the modal frequency identified during the

analysis of the event. This relay measured the voltages of the

system at station Z, filtered the voltages phasor signals coming

in from conventional relay signal processing, and operated

based on the detected output of the subsequently filtered

subsynchronous voltage magnitude. A software flow diagram

of the subsynchronous oscillation (SSO) detection algorithm

deployed in this case is shown in Fig. 5. [5] discusses this

relay in more detail.

Fig. 4 Event 1 PSCAD Simulation Results [3]

Fig. 5 Event 1 SSO Detection Software Flow Diagram [5]

B. Event 2

On January 8th 2013, a trip occurred on a line in northern-

central Texas from what appeared, at the time, to be a power

swing condition. During this operation the line between

station M and S, shown in Fig. 8, was open due to temporary

local switching. The oscillography record of this event can be

seen in Fig. 6, where the FFT of the current and voltage

waveforms between 0.0 seconds and 0.6 seconds into the

event can be seen in Fig. 7.

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Fig. 6 Event 2A Oscillography Record

Fig. 7 Event 2A FFT Spectrum Analysis of Phase A Voltage and Current

In this case, the only nearby generating resource was a

windfarm and there appeared to be no initial disturbance as to

the cause of the swing. It is notable that the 69kV bus where

this windfarm connects has a low short circuit MVA (less than

160MVA with the station’s strongest source out of service) in

comparison with the output capability of the windfarm (nearly

50MW). Power swing blocking was enabled on the

transmission line relaying that initially tripped due to this

event and the condition was considered remedied. A

simplified electrical network diagram, illustrating the

windfarm connection and surrounding line terminals, is shown

in Fig. 8. The line between stations M and P, labeled as Event

2A, is the line which tripped during the event.

Windfarm

Station M

Station P

6 miles

35 miles

Station T60 miles

Station S43 miles

69kV

Event 2A

Event 2B Fig. 8 Event 2 Simplified Electrical Network

Additional customer complaints mentioning blinking and

dimming of lighting in the area around the facility which the

windfarm connects were noted following the initial

disturbance. A project was executed which was aimed at

capturing any system events which were similar to the nature

of event 2A. A relay was conceived of at this time dedicated

to monitoring the output of the windfarm. This relay was set

to detect for conditions when between 3-7Hz phasor

fluctuations occurred on any two of the three voltage or

current measurements and the content was above a minimum

pickup for a fixed time. A diagram of the approximate signal

processing and logic methods used to detect for these

conditions are shown in Fig. 9. This relay was configured to

trip the line to the windfarm.

VAp BP÷

3-7Hz

>

>Setting

Setting

VBpVCp

IAp BP÷

3-7Hz

>

>Setting

Setting

IBpICp

2/3

2/3

Timer

Fault

Detection

Fig. 9 Event 2 Installed SSO Detection Algorithm

After only one month in service, in November of 2015, the

SSO relay that was installed detected a subsynchronous

oscillation and tripped the windfarm. Fig. 10 illustrates the

oscillography captured during this event and Fig. 11 illustrates

the FFT of the waveforms illustrated from 0.0 seconds to 0.9

seconds. During this event a permanent fault on the station M

to station S line of Fig. 8 caused the initial oscillation

disturbance and then continued the subsynchronous oscillation

after the time-delayed reclose open internal times out and the

line circuit breaker closes back into the fault at the 69kV bus

to which the windfarm is connected. The windfarm was

tripped nearly one-second after the initial fault by the

subsynchronous oscillation relay, ending the event.

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Curr

ent (A

)V

olta

ge (

V)

Fig. 10 Event 2B Oscillography Record

0 20 40 60 80 100 120

Frequency (Hz)

50

100

150

Am

plit

ude

Spectrum Analysis of IA

0 20 40 60 80 100 120

Frequency (Hz)

0

2

4

6

Am

plit

ude

104 Spectrum Analysis of VA

Fig. 11 Event 2B FFT Spectrum Analysis of Phase A Voltage and Current

With this event record captured demonstrating new

evidence of the interaction of the electrical network and the

windfarm facilities, a PSCAD simulation was worked out to

simulate the event conditions. Fig. 12 demonstrates the

simulation results from the PSCAD simulation and Fig. 13

illustrates the FFT of the waveforms captured during the entire

time window shown. The same network conditions were

simulated where the line M to line S opened at time 3.75

seconds into the simulation and at the same windfarm dispatch

level as that seen in the November 17th

event. As can be seen,

the results fairly closely mirror the event records captured

from the facility during the November 17th

event. The FFT

results of Fig. 13 demonstrate that the frequency of oscillation

was near the event 2B frequency. From this simulation, it can

be noted that the scenario listed does not illustrate traditional

power swing conditions, but rather a SSCI. This is because

there is no machinery within the network where slip rate or a

rotating mass is the cause of the subsynchronous oscillation.

Instead, in this case, the only dynamic electrical facilities

present in the electrical vicinity of the event was the windfarm

infrastructure.

Fig. 12 Event 2B PSCAD Simulation Results

Fig. 13 Event 2B PSCAD Spectrum Analysis of Phase A Voltage and Current

C. Event 3

In late 2015 and early 2016, the installation of several

series capacitor banks in western Texas near windfarm

infrastructure, the historical context within AEP, and the

precedent set forth in multiple industry reported events

justified the installation of relays installed at the series

capacitors which would quickly react to almost any SSCI

phenomena. It was at this time that the relay of Fig. 9 was

Pg. 5

duplicated and modified such that it could act faster and a

broader range of frequencies would be detected. Also, the

relays were initially set and configured such that they could

bypass the series capacitors shown in Fig. 14 in the event of

detection of SSO. Initial screening studies identified potential

SSCI modes in the range of 30Hz. On August 24th

2017, a

personnel error at station P, shown in Fig. 14, caused an

erroneous DTT signal to be sent to station D. This event

caused the windfarms (shown as WF V3 and WF V4)

connected to station D to become radially connected to the

station D to station C line in series with the in-service series

capacitor banks shown. 0.3 seconds after the opening of the

line between the two stations due to the DTT, the Fig. 15

oscillography was recorded on the line relaying at station D on

the station D to station C line relaying. An FFT of the Fig. 15

oscillography waveform content is shown in Fig. 16. After

nearly 2 seconds of steady state conditions, similar to that

shown in Fig. 15, the series capacitors which were both

initially in service bypassed automatically on protective trip of

subsynchronous overcurrent function. Nearly 20 minutes

passed and dispatch personnel manually reinserted the two

series capacitors, at nearly the same time.

Station C

Station L

WF B

WF V3

WF V4Station D

82 miles

29 miles

13 miles

11 miles

27 miles

Station P

WF LM11 miles

Station N

20 miles

26 miles

Fig. 14 Event 3 Simplified Electrical Network

Cu

rre

nt (A

)

Vo

lta

ge

(V

)

Fig. 15 Event 3A Oscillography Record

Fig. 16 Event 3A Spectrum Analysis of Phase A Voltage and Current

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Fig. 17 Event 3B Oscillography Record

Fig. 18 Event 3B Spectrum Analysis of Phase A Voltage and Current

Approximately 0.4 seconds after the first of the two series

capacitor banks was reinserted, the second of the two series

capacitor banks was reinserted and caused the creation of the

oscillography shown in Fig. 17 was recorded when the second

of the two series capacitors was re-inserted. A FFT of the

oscillography waveform captures of Fig. 17, from 0.0 seconds

to 0.65 seconds, is shown in Fig. 18. The event stopped when

the windfarms at V3 and V4 tripped. Fortunately, no

infrastructure damage was noted by personnel at either the

windfarm or on the electrical network. Following the event,

PSCAD simulations were developed which were aimed at

replicating the event. These simulations resulted in the

development of Fig. 19 and Fig. 20 demonstrating the last part

of the Aug 24th

event. Fig. 19 shows that at time 0.0 seconds

the station D to station P line opens, which immediately

increases the amount of subsynchronous frequency content in

the waveforms. At 0.25 seconds in the figure the windfarms

of the simulation trip. Fig. 20 shows a FFT of the Fig. 19 time

series data, from 0.0 seconds to 0.25 seconds. As can be seen,

the simulation resembles the actual event waveforms, though

slightly shorter, less severe, and with slightly different SSO

mode frequency than the actual event. This simulation also

illustrates an event in which SSCI conditions existed due to

the combination of radially connected windfarm infrastructure

and a heavily series compensated electrical network.

Fig. 19 Event 3B PSCAD Simulation Results

Fig. 20 Event 3B PSCAD Simulation Spectrum Analysis of Phase A Current

and Voltage

III. AEP SUBSYNCHRONOUS OSCILLATION DETECTION RELAY

As mentioned in each of the events, AEP has made use of several different SSO detection relays. Due to the fact that there is an increasing amount of renewable generation resource connection requests to the AEP network an attempt has been made to standardize a detection algorithm which would work to detect a large range of subsynchronous frequencies with a high degree of reliability. Also, while there are a few out-of-the-box solutions readily available for use in the detection of SSO events, none of the solutions demonstrated the level of customization or flexibility desired by AEP. Therefore, AEP developed a solution using off-the-shelf components but on a

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Fig. 21 Event 1, 2B, 3B Multimodal Symmetrical Component Analysis

platform which was customizable enough that the degree of flexibility and the functions AEP sought to employ were possible.

As can be seen in each of the events demonstrated earlier, SSCI conditions are clearly dominated by a three phase system phenomenon where there exists 60Hz waveform content and subsynchronous waveform content in both the voltage and current signals. To demonstrate whether the subsynchronous waveform content generated during the system conditions of the three AEP events previously shown demonstrated a balanced subsynchronous three phase source, the event records had their individual analog voltage or current channel data filtered according to either a 60Hz FIR filter, or a subsynchronous filter designed to extract only the subsynchronous content from the waveform data. Then the filtered data was turned into a phasor using a discrete Fourier transform, where, for 60Hz filtered data the DFT was fitted to one cycle of the 60Hz period, or, for subsynchronous waveform content, the DFT was fitted to one cycle of the subsynchronous frequency period. Then, the symmetrical components of the three phase current or voltage phasors were calculated and displayed as shown in Fig. 21. Figure data labels shown as ‘XXN’ illustrates 60Hz symmetrical component information (0 as zero sequence, 1 as positive sequence, 2 as negative sequence), while the ‘XXS’ labels refer to subsynchronous sequence components. Fig. 21 illustrates that in each of the previously illustrated events (events 1, 2B and 3B) there does exist a large amount of positive sequence subsynchronous content.

Using the observation that each of the events demonstrates

a system condition where there are well-balanced nominal

frequency phasors and well-balanced subsynchronous

frequency phasors, it is possible to build a model of power

system voltages and currents operating under SSCI conditions

where the relationships of Eq.’s 0A thru 0F are true.

Additionally, [6] suggests the use of this signal model for

multimodal signal representation.

(0A)

(0B)

(0C)

(0D)

(0E)

(0F)

Defining as the positive sequence current magnitude of

the nominal frequency, as the subsynchronous positive

sequence current , as the positive sequence voltage

magnitude of the nominal frequency, as the

subsynchronous positive sequence voltage, ias 120°, as

the phase difference between the nominal frequency voltage

and current, β as the phase difference between the

subsynchronous frequency voltage and current, as the

nominal frequency, and as the subsynchronous frequency.

If one were to calculate the instantaneous power of the

hypothetical power system operating under these conditions,

the following would result.

(1)

Under very balanced system conditions, like those observed in

Fig. 21, the following would be true.

Pg. 8

and and

and

Through simplification, Eq. 1 would result in the following.

(2)

Where:

(3)

(4)

Eq. 2 illustrates that there are two DC modes to the instantaneous power represented in Eq. 2. The first two terms are generated purely by the balanced three phase content of the nominal system frequency waveforms and the subsynchronous frequency waveforms. But, because the first two terms are not discernable from one another in the frequency domain, because they are both made up purely of DC content, one cannot ascertain only the subsynchronous instantaneous power from the nominal frequency power. However, the last term of Eq. 2 illustrates that instantaneous power oscillates with a radian

frequency of . As can be seen in Fig. 21, it is clear that when using the output of a phasor calculation when observing subsynchronous oscillation phenomenon using conventional relay signal processing techniques, there is clear indication that the magnitude of a phasor oscillates with the

same radian frequency noted above as . This fact illustrates that the use of the phasor magnitude of voltages and currents within a relay to ascertain whether subsynchronous power is present, is clearly possible. In fact, [5] suggests the use of power, as others have done in a similar manner [7], as input to an operating quantity in a protection element for use in the SSCI detection application space. There is one problem however, and that is that the conventional signal processing techniques used in conventional protective relaying platforms attenuates waveform information in the range of subsynchronous frequencies. Fig. 22 illustrates this phenomenon well, where, from 0Hz – 40Hz, depending on what type of conventional signal processing filter is used, 70% of waveform attenuation can occur [8][9]. Having less than 70% of the original signal available to determine whether SSCI conditions exist using the phasor data processed from these filtering techniques is clearly a problem. Fortunately, this problem can be broken down into two parts.

The first part of the solution to the problem can be illustrated considering the SSCI phenomenon of Fig. 10, where it is clear that the subsynchronous mode of oscillation is on the order of 55Hz. A 55Hz subsynchronous oscillation will

generate a oscillation in the magnitude of the phasors used in a conventional relay. As can be seen in Fig. 22, 55Hz signal data is not attenuated significantly by the conventional filtering methods. This means that the 5Hz phasor oscillation data is also not attenuated significantly. Additionally, because it is very challenging to distinguish between 55Hz and 60Hz frequency due to the close proximity of the frequencies using digital filter techniques, the use of phasor magnitude, which contains a DC mode meant to capture 60Hz content and an AC mode meant to capture the subsynchronous mode, to detect near nominal

frequency SSO modes solves the first part to the problem. The solution involves separating the DC mode of the phasor magnitude from the AC part of the phasor magnitude using more conventional signal processing techniques. This is simpler to accomplish than separating 55Hz waveform content from 60Hz waveform content because DC mode rejection is simpler than AC mode rejection. Obtaining the magnitude of the AC mode provides the ability to extract the magnitude of the third term of Eq. 2.

0 20 40 60 80 100 120

Frequency (Hz)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Gain

COSINE FILTER

SINE FILTER

Fig. 22 Digital filtering frequency response used in Conventional Relaying

The second part of the problem is defined by the fact that there is significant loss of subsynchronous information that requires conventional signal processing embedded within protection relays, and even PLCs, be bypassed altogether and an alternative method meant to extract only the subsynchronous content be used in its place. To accomplish this, a team at AEP developed a simple analog filter meant to extract subsynchronous content from the current and voltages being monitored and completely rejects 60Hz waveform content. The filtered information is then scaled appropriately, fed back into a relay, filtered using only a DC mode filter, and instantaneous power is then calculated from the current and voltage channel data. This signal, when in the presence of a balanced subsynchronous source, is a DC mode with

magnitude using the same term definitions discussed previously.

Using the solutions discussed, these two methods of SSO detection were then deployed in a modern PLC where the signal processing methods, hardware, and a small portion of the logic used for SSO detection is shown at a high level, within Fig. 23. This figure illustrates the combined concept deployed to detect generic SSO conditions which can have a wide range of subsynchronous frequencies that can be detected.

Pg. 9

Curr

ents

CTs

PTs

Volt

ages

HS-

XDCR

HS-

XDCR

PLC

VT Inputs

CT Inputs

LEA Inputs

Analog

Filtering

a/d

a/d

cos

f

G

dft

a/d cos

Sfp

CT/PT Card

DC

Blo

ckin

g

Pow

er

Calc

Logic

RMS1

RMS2

>

Setting1

>

Setting2

X1

SSO

Detect

Pow

er

Cal

c

0

Frequency

Calculator

Frequency

Calculator

(1/ƒ1)*Setting3

(1/ƒ2)*Setting4X2

X1

Scal

ing

Zero

CrossingRun

0X2

Sfi

dft

÷ SEL

20%>

I0I1

[A]^-1

Zero

CrossingRun

PT1

PT2

Fig. 23 Generic SSO Relay Detection Algorithm Signal Processing and Logic Flowchart

Initial observations shown in Fig. 21 provided some insight on how to set the pickup settings for the two elements onboard the relay. The observation noted that the SSO power delivered during the event was approximately 100% of the power output rating of one of the two facilities. Therefore, precedent was established to pick a power level that is somehow proportional to the power output of the facilities being monitored by the relay. This being the case, and in a similar manner to the way in which overcurrent elements are selected, the suggestion is made that a selected level of pickup of the output capacity of the facilities being monitored be configured. For the ‘setting2’ value this is a simple exercise to perform where the pickup of the element is simply the power rating of the facility divided by the pickup level desired and then reflected thru the CT, PT, and analog scaling ratios. The calculation can be shown in Eq. 5. This pickup value ensures a dependable but also secure response to the SSO condition.

(5)

The ‘setting1’ value of the element is more difficult to set. This is because the amplitude of the subsynchronous mode has

a magnitude of which is made up of both nominal frequency voltages and currents and subsynchronous frequency voltages and currents. Also, the magnitude of this term is made up of a phase angle difference between the nominal frequency current and voltage and the subsynchronous frequency current and voltage. To simplify the task of attempting to determine all of these quantities in real time, extracting their values, and then determining the subsynchronous power from then, the authors

first propose to assume that the term of Eq. 3 is zero. Doing so will assume the smallest amount of signal availability for use, which allows for a dependable manner to

detect SSO conditions. Also, the authors propose that be set to nominal system voltage reflected to the relay input as is similar to the conditions shown in each of the events shown earlier. Additionally, using the fact that the SSO mode voltages that started out during each of the events were only a small fraction of the nominal voltages, the authors propose to

use an assumed = 0.15 (15% of nominal voltage). Setting the pickup value in this way ensures a dependable but also secure response to the SSO condition. Using each of these assumptions, Eq. 3 simplifies to the following Equation, Eq. 6.

(6)

As shown in Fig. 23, the timers of the elements (Setting3 and Setting4) are dynamic in such a way that oscillations resulting in low frequency disturbances are responded to by the SSO detection element at a slower rate than those at a higher frequency. This allows for a secure response to any SSO condition.

To illustrate the detection element, MATLAB simulations were developed which analyzed the reaction of the algorithm and simulated hardware when exposed to each of the three system phenomena as well as external faults. Table I, below, illustrates the settings of the elements as developed from the settings guidance mentioned earlier. The following figures illustrate the response of the simulated relay to each of the events.

Pg. 10

TABLE I. SETTINGS OF SIMULATED GENERIC SSO DETECTION

ALGORITHM FOR SIMULATED EVENTS OF FIG.’S 24, 25, 26 AND 27

Event Ratings, CTR, PTR, Standard Pickup, Vnom,Inom

Setting1

Setting2

Setting3/4

1 FacilityRating = 150MW, CTR=400, PTR=3000, Pickup=5, Vnom=67, Inom=5

20 25 5/3

2B FacilityRating = 50MW, CTR=120, PTR=600, Pickup=5, Vnom=67, Inom=5

71 138 5/3

3B FacilityRating = 200MW, CTR=600, PTR=3000, Pickup=5, Vnom=67, Inom=5

18 22 5/3

Ext. Flt

FacilityRating = 200MW, CTR=600, PTR=3000, Pickup=5, Vnom=67, Inom=5

18 22 5/3

The simulated relay response for event 1 is shown in Fig. 24. As this figure shows, the relays hardware filters quickly respond to the subsynchronous frequency injection into the currents and voltages, as can be seen in the two time series plots below the raw event time series plots. The digital filters then begin to pass through the frequency content of the instantaneous powers that they are each designed to detect. The signal SFi, in the middle figure, is the subsynchronous power calculated using the hardware filtered channels. The

signal SFp is the power calculated using the phasor data amplitudes. Because the frequency of the SSO content in this event is near the cutoff frequency of both the hardware filters and the phasor data channels, the SSO event is detected by both protection algorithms filters. The time series plot below the filtered operating quantity amplitudes illustrates the instantaneous RMS content of each of the signals, as well as the settings of Setting1 and Setting2. As can be seen in the last time series plot in Fig. 24, when the RMS content is above the pickup level of the settings, the variables PT1 or PT2 will assert, and the frequency tracking function will begin to detect the frequency of oscillation of the SSO modes. Once this occurs, the variables X1 and X2 in Fig. 23 show that the period timers (1/f1*setting and 1/f2*setting) begin to change from their initial values of Setting3 and Setting4 to the actual oscillation period multiplied by these values. Once the timers recognize that the inputs to the timers have been on longer than this value, a trip occurs. As Fig. 24 shows, a trip occurs at 0.78s into the event; which is nearly 0.35 seconds after the initial fault occurs. This is illustrated by the signal T2OUT changing from a logical zero to a logical one in the figure. In this case, the hardware filtered detection algorithm detected the SSO event before the phasor based method.

Fig. 24 Generic SSO Relay Response to Event 1

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Fig. 25 Generic SSO Relay Response to Event 2B

The simulated relay response for event 2B is shown in Fig. 25. As this figure shows, the relays hardware filters do not respond to the subsynchronous frequency injection into the currents and voltages, as can be seen in the two figures below the raw event captures. This is because the SSO mode frequency falls far outside the cutoff frequency of the hardware filtering. Because of this, the signal SFi, being the instantaneous subsynchronous power, is zero during the entire event. The signal SFp is the power calculated using the phasor data amplitudes. Because the frequency of the SSO content in this event is captured only in the phasor data channels, the SSO event is seen by only the phasor based portion of the protection algorithms. The fourth time series plot in Fig. 25 illustrates the instantaneous RMS content of each of the signals, as well as the settings of Setting1 and Setting2. As can be seen in the last capture in Fig. 25, when the RMS content is above the pickup level of the settings, the frequency tracking function begins to detect the frequency of oscillation of the SSO mode. Once this occurs, the variable X1 in Fig. 23 show that the period timer (1/f1*setting) begin to change from its initial value of Setting3 to the actual oscillation period multiplied by these values. But, in this case, because the subsynchronous mode is highly damped, the RMS content falls below the pickup setting before timer1 reaches its terminal value. This drops out the timer which was timing to trip. Once the second fault occurs, the SSO RMS content grows again. This time, however, the event ends due to the line tripping from to the previously installed SSO relay at the windfarm terminal monitoring this event. If the new SSO relay algorithm had been in place during this event, it is highly likely that the SSO event would have continued to decrease in amplitude and completely be attenuated in magnitude, resulting in no SSO event detection.

This is acceptable because for highly damped SSO modes, there is little desire to trip electrical infrastructure.

The simulated relay response for event 3B is shown in Fig. 26. This event is very similar to the response of the simulated relay of event 1. The main difference is that in this event playback, there is pronounced subsynchronous voltage and current waveform content as soon as the event starts. As this figure shows, the relays hardware filters are responding to the subsynchronous waveform content before the 1.7s unstable SSO event into the currents and voltages, as can be seen in the two time series plots below the raw event captures. The signal SFi, in the middle figure, is the subsynchronous power calculated using the hardware filtered channels and quickly grows immediately following the unstable SSO event which occurs at approx. 1.7s in the event playback. The time series plot below the filtered operating quantity amplitudes in Fig. 26 illustrates the instantaneous RMS content of each of the signals, as well as the settings of Setting1 and Setting2. As can be seen in the last time series plot in Fig. 26, when the RMS content is above the pickup level of the settings, the frequency tracking function begins to detect the frequency of oscillation of the SSO modes. Once this occurs, the variables X1 and X2 in Fig. 23 show that the period timers (1/f1*setting and 1/f2*setting) begin to change from their initial values of Setting3 and Setting4 to the actual oscillation period multiplied by these values. Because the frequency of the SSO content in this event is near the cutoff frequencies of both the hardware filters and the phasor data channels, the SSO event is detected by both protection algorithms filters, but the phasor based algorithm frequency tracking function cannot determine a frequency of oscillation and therefore does not operate first.

Pg. 12

Once the timers recognize that the inputs to the timers have been on longer than this value, a trip occurs. As Fig. 26 shows, a trip occurs at 1.94s into the event; which is nearly 0.25 seconds after the SSO event becomes unstable. This is illustrated by the signal T2OUT changing from a logical zero to a logical one in the figure. In this case, the hardware filtered detection algorithm detected the SSO event before the phasor based method.

To further illustrate the detection elements capability,

faults were simulated to demonstrate the security of the relay

algorithm to fault conditions external to system infrastructure

where the SSO algorithm would be applied. As can be

observed in Fig. 23, the relay detection algorithm is somewhat

immune to large phase to ground faults because a large zero

sequence current will continue to feed the previously

measured phasor power delivered into the bandpass filter of

the algorithm, which will allow the relay algorithm to

effectively ignore the fault. Because of this, a permanent three

phase fault with a typical high speed reclose external to the

relay was simulated such that this immunity would be

bypassed. The simulation results of this scenario with the

settings used in the Event 3 simulation are seen in Fig. 27. As

can be seen in the figure, the three phase currents increase

rapidly during the faults, while the voltages decrease in

magnitude. Each fault is nearly 4 cycles long, and the open

internal time of the high speed reclose is a typical AEP value

of twenty cycles. The hardware filtered channels detect these

large step changes in magnitude but quickly settle out.

However, the bandpass filtered output of the phasor based

instantaneous power calculation increases rapidly following

each of the faults and then falls after each of the faults. These

rapid rises and falls each increase the magnitude of the phasor

based RMS power above the pickup setting of the algorithm

detection element. This releases the frequency detection

algorithm. Because the frequency detection algorithm does

not detect an SSO mode, the timer value used to detect the

SSO condition never times out and the SSO detect condition is

not declared. This is a proper operation of the relay element

and demonstrates the security of the detection algorithm in the

presence of a challenging fault against which the relay should

hold.

Fig. 26 Generic SSO Relay Response to Event 3B

Pg. 13

Fig. 27 Generic SSO Relay Response to Three Phase Permanent Fault Conditions with High-Speed Reclose

IV. CONCLUSIONS

Several SSCI events that have occurred on the AEP system

have been illustrated. It has been demonstrated using historical

event record capture signal analysis that SSO can be detected

by modeling the waveforms captured during the event using

balanced nominal frequency voltages and currents in sum with

balanced subsynchronous voltages and currents.

Demonstrations have been provided using a detection

algorithm which is both secure and dependable in the presence

of common power system phenomenon and SSO event

phenomenon. Settings guidance has been provided which

would assist with setting this detection algorithm.

REFERENCES

[1] http://aep.com/about/

[2] P.M. Anderson, and R.G. Farmer, Series Compensation of Power Systems, PBLSH! Inc, pp. 229–258, 1996.

[3] G. Irwin, “Simulation and Analysis Methods for SSR/SSTI/SSCI,” PUCT Panel Session, Nov 19th 2014. Available online: http://www.ercot.com/content/meetings/other/keydocs/2014/1119-PROJECT4350/AnalysisMethods_SSR_SSCI_SSTI.ppt

[4] S.H. Horowitz, A.G. Phadke, Power System Relaying, 3rd ed., New Jersey: Wiley, pp. 189-190.

[5] L.C. Gross, ”Sub-Synchronous Grid Conditions: New Event, New Problem, and New Solutions,” 37th Annual Western Protective Relay Conference, Oct 19-21, 2010.

[6] P.M. Anderson, Power System Protection, Vol.1, New Jersey: Wiley, 1999, pp. 955-999.

[7] Z. Zhang, P.Eng., Ilia Voloh, J. Cardenas, I. Antiza, F Iliceto. “Inter-Area Oscillation Detection by Modern Digital Relay”. CIGRE St. Petersburg, May 2011.

[8] V.K. Ingle, and J.G. Proakis, Digital Signal Processing using MATLAB, Brooks/Cole, 2000, pp. 197-208.

[9] E.O. Schweitzer, and D. Hou, ”Filtering for Protective Relays,” 19th Annual Western Protective Relay Conference, Spokane, Washington, Oct 20-22, 1992.

BIOGRAPHIES

Zachary P. Campbell received his bachelor’s degree from the University of

Akron, in Akron, Ohio, in 2008, and his master’s degree from The Ohio State

University, in Columbus, Ohio, in 2012. Zak is a principal engineer at

American Electric Power (AEP) where he has worked in various protection

and control departments since 2008. He is a member of IEEE, CIGRE and is

a registered professional engineer in the state of Ohio. zpcampbell@aep.com

Kiril Andov received his bachelor’s degree from the University of St Cyril &

Methodius in Skopje, Macedonia, 2001. Currently, Kiril works at AEP as

senior engineer in system performance analysis group. In his current role,

Kiril performs various EMT studies and provides support to various

departments. Kiril is AEP’s subject matter expert of insulation coordination

studies area and subsynchronous oscillations studies area.

kandov@aep.com

Shawn Coppel graduated Magna Cum Laude from DeVry Institute of

Technology in 1996 with a bachelor’s degree in Electronics Engineering

Technology. Shawn is a Senior Engineering Technologist for American

Electric Power (AEP) where he has worked in the transmission protection &

control laboratory since 2000. Previously, Shawn worked in

telecommunications prototype development at Lucent Technologies. Shawn

is a Veteran of the U.S. Army and specialized in radio systems repair.

scoppel@aep.com