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Contract No. DE-AC36-08GO28308

Oscillation Damping: A 

Comparison of Wind and 

Photovoltaic Power Plant 

Capabilities

Preprint 

M. Singh, A. Allen, E. Muljadi, and V. Gevorgian

National Renewable Energy Laboratory 

To be presented at the IEEE Symposium on Power Electronics and 

Machines for Wind and Water Applications 

Milwaukee, Wisconsin

July 24–26, 2014

Conference Paper

NREL/CP-5D00-62260

July 2014 
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Abstract—This paper investigates the potential for wind 

power plants (WPPs) and photovoltaic power plants (PVPPs) to 

damp inter-area oscillations. Inter-area oscillations may be the 

result of a single or a group of generators oscillating against 

another group of generators across a weak transmission link. If 

poorly damped, these power system oscillations can cause system 

instability and potentially lead to blackouts. Power conversion 

devices, particularly megawatt-scale converters that connect 

wind turbines and PVPPs to the grid, could be used to damp 

these oscillations by injecting power into the system out of phase 

with the potentially unstable mode. Over time, the net energy 

injection is near zero; therefore, providing this “static damping” 

capability is not expected to affect annual energy production. 

However, WPPs and PVPPs have different capabilities due to the 

inherent physical nature of these plants. WPPs have some energy 

stored in the rotating masses of the turbines, whereas PVPPs 

have no such stored energy. Thus, the challenge to provide 

oscillation damping services will have to be approached 

differently for WPPs and PVPPs. This work compares and 

contrasts strategies for providing oscillation damping services 

from WPPs and PVPPs. 

Kundur’s well-known, two-area, four-generator system is 

modeled in PSCAD/EMTDC. The WPP and PVPP models are 

based on the Western Electricity Coordination Council (WECC) 

standard models. Controllers to damp inter-area oscillations are

developed and added to the WECC WPP and PVPP models, and 

their effects are studied. Analysis is performed on the data 

generated by the simulations. 

Index Terms—wind power generation, photovoltaic power 

generation, power system oscillations, power system stability

I. INTRODUCTION

ARIABLE generation (VG) penetration levels are 

increasing throughout the United States. This trend is 

expected to continue in the coming decades [1]. In certain 

regions of the United States, peak penetration levels can 

approach 30% [2]. At these penetration levels, in many cases 

it is expected that VG such as wind power and photovoltaic 

(PV) power will displace conventional generation. This 

displacement may be permanent as a result of conventional 

plant retirements based on emissions- or age-related concerns 

and because utilities may prefer to install VG instead of new 

conventional generation [3,4]. This displacement of 

conventional synchronous generation by asynchronous VG 

 M. Singh, A. Allen, E. Muljadi, and V. Gevorgian are with National 

Renewable Energy Laboratory, Golden, CO, USA (e-mail: 

eduard.muljadi@nrel.gov).

will have significant stability impacts. In this paper, we focus 

on inter-area oscillation modes in particular. In the literature, 

numerous simulation-based studies have been conducted, with 

specific regard to wind power, with inconclusive results 

suggesting that the damping of modes may be improved or 

worsened by wind [5]-[8]. The consensus appears to be that 

wind power plants (WPPs) do not participate directly in 

oscillation modes, but their presence leads to the displacement 

of conventional plant inertia and other topology changes that 

have the potential to influence the oscillation modes [8]. 

However, these papers study the passive effects of wind 

integration rather than active attempts to damp out 

oscillations. With regard to the effects of photovoltaic power 

plants (PVPPs) on system modes, the community has not yet 

generated much literature due to low PV penetration levels 

and the assumption that PV integration would be a distribution 

system issue rather than a transmission system issue. 

In our present work, we model a familiar two-area test 

system [9] with an additional WPP. To study the effects of 

PV, we replace the WPP in our model with a PVPP of equal 

megawatt rating (204 MW each). We also add an additional 

oscillation damping controller in each case to directly 

influence modes. The two-area system model is a time domain 

model developed using the PSCAD/EMTDC platform [10]. 

This platform was chosen for its short simulation time step, 

giving insight into any dynamics that may appear. This 

platform has been used before for two-area stability analyses 

[11]. The output from the simulations can be filtered and 

down-sampled to simulate phasor measurement unit (PMU) 

data. The WPP model is based on the Western Electricity 

Coordinating Council (WECC) Wind Generator Modeling 

Group’s standard model for Type 3 (doubly-fed induction 

generator, or DFIG) WPPs [12]. The PVPP model is also 

based on the WECC standard model [13]. The standard 

models are ported to PSCAD/EMTDC based on the work 

reported in [14]. Additional controls for inter-area oscillation 

damping have been added to the standard models to inject

power into the system out of phase with the potentially 

unstable mode. A detailed explanation of the model 

development is provided in Section II. 

In the work presented here, a method based on the YuleWalker power spectral density (PSD) calculation algorithm 

[15] is applied to analyze the simulated PMU data generated 

by the model. A description of the method is provided in 

Section II. The effectiveness of the damping controls for 

different WPP and PVPP output levels is investigated with 

Oscillation Damping: A Comparison of Wind 

and Photovoltaic Power Plant Capabilities 

M .Singh, A. Allen, E. Muljadi, and V. Gevorgian

V
2 

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respect to changes in oscillation modes. These scenarios are 

discussed in detail in Section IV. The results of the analysis 

indicate that the oscillation damping controller is able to 

influence modes by improving the damping of the system; 

however, it has to be tuned for one particular mode, and its 

effects on other modes are difficult to predict. Detailed results 

and discussion are provided in Section V.

II. MODEL DEVELOPMENT

The model used for these simulations was developed in 

three stages. In the first stage, a model of the two-area system 

was developed in PSCAD/EMTDC. In the next stage, a model 

of the WECC standard WPP was developed and integrated 

into the two-area system model. The original WECC model 

was intended for phasor-based modeling software such as 

PSLF or PSS/E [16]. In our work, we use a time-domain 

PSCAD/EMTDC equivalent of the WECC model (discussed 

in detail in [14]). In the third stage, an oscillation damping 

controller was developed and added to the model. 

A. Two-Area System Model

A one-line diagram of the two-area system is shown in Fig. 

1. The base system is symmetrical in terms of generation and 

line impedance. The model parameters are taken from [9]. In 

steady-state conditions with no wind, there is a 400-MW 

transfer from Area 1 to Area 2 across the weak transmission 

tie between the areas. It should be noted that in our model 

power system stabilizers and automatic generation control are 

not included; however, each generator’s excitation system and 

governor are modeled. PSCAD/EMTDC parameters for 

modeling generators and controls not provided in [9] are left 

at default values when reasonable. For electromechanical 

transients, reflection of traveling waves at transmission line 

ends is not important; hence, instead of using traveling-wave 

or frequency-dependent transmission line models, a coupledpi transmission line representation is used to model each of 

the transmission lines in the system.

Fig. 1. Two-area system from Kundur [9] with an additional WPP. 

B. WPP Model

The WPP model is a PSCAD/EMTDC equivalent of the 

DFIG WPP model developed by the WECC Wind Generator 

Modeling Group. Fig. 2 illustrates a schematic of the WECC 

DFIG WPP model framework. 

Fig. 2. Schematic of a WECC DFIG WPP model.

The WPP is sized such that the wind penetration level in 

the two-area system is 10% when the WPP is supplying rated 

power. The WPP collector system model is represented by an 

aggregated single-line equivalent. Details on how this 

aggregation is performed are provided in [16]. The collector 

system data for the aggregation process is from a real WPP 

and is presented in [16]. The WECC DFIG WPP model is well

documented, and the parameters for the model are available in 

[17]. 

C. PVPP Model

The positive sequence dynamic model for PVPP is 

developed based on the recommendation of the WECC 

Renewable Energy Modeling Task Force in the detailed report 

presented in [13]. The dynamic model assumes that the solar 

irradiance is constant throughout the electrical transient events 

at the transmission line set by the load flow solution in the 

pre-fault event. The grid interface is equipped with voltage 

ride-through capability and current limit to limit the current 

flow through the power electronics switches (IGBTs).

Fig. 3. Block diagram of a PVPP 

The dynamic model also represents the capability to limit 

the ramp rates of the output power injected to the grid and the 

current limit representing the maximum current-carrying 

capability of the power electronics switches (IGBT). The 

output reactive power can be controlled to maintain the 

voltage, the power factor, or the reactive power generated by 

the PVPP as implemented in the actual hardware of a PVPP. 
3 

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D. Oscillation Damping in WPP Controls

Additional WPP controls may be provided by the turbine 

manufacturer for the purpose of frequency support, but no 

manufacturer yet offers a dedicated oscillation damping 

control. Typical frequency support controls include governor 

droop control and synthetic inertia. Detailed explanations of 

droop control and synthetic inertia are provided in [18]-[23]. 

The effects of these controls on oscillation modes are 

unknown. These controls are not considered in our modeling 

effort because they are non-standard additions to the WECC 

WPP model and their effect on modal behavior is debatable. 

Oscillation damping controls also differ from frequency 

support controls in one major aspect: energy injection. The 

goal of the oscillation damping control is to have a near-zero 

injection of energy during the time frame of controller action. 

This means that unlike in the case of frequency support 

controls, the WPP operator does not suffer much revenue loss 

during controller action. 

In our test case, the WPP is connected at Bus 6 (see Fig. 1) 

and thus is supplying only Area 1 directly. The WPP output 

power at the WPP’s point of interconnection (Bus 6 in our test 

case) is measured by the PMU. The damper is tuned to the 

dominant inter-area modal frequency (0.76 Hz in our test 

system). When this frequency is observed in the local WPP 

PMU measurements, the controller is activated.

The implementation of the oscillation damping controller is 

shown in Fig. 4. The WPP power injected into Bus 6 is our 

measured variable. Removing the DC component of this 

power leaves us with the oscillatory component. Applying a 

notch filter tuned to 0.76 Hz to the oscillatory component 

allows for the isolation of the dominant oscillation mode. 

After we see how much the power swing is in this dominant 

mode, we set the WPP to oppose this mode. We scale down 

the oscillatory component (so as to not exceed converter 

ratings for the turbines; more on this in Section V) and add the 

inverse of this scaled oscillatory component to the reference 

power command of the WPP. (Fig. 2 shows where this 

reference power is inputted into the model.) It is expected that 

the WPP controller will then assign power commands to 

individual turbines; however, the WECC WPP model does not 

include individual turbine representations, so individual 

turbine behavior is not modeled in detail. 

Remove DC 

offset

Notch filter 

for dominant 

mode

Scale factor k 

(0<k<1, kmax= 

2% of WPP 

rating)

PBus6 Posc Posc_dom Posc_WPP

-

+

Pref

Pref*

Fig. 4. Control block diagram of oscillation damping control.

For PVPPs, a similar controller can be used if the PVPP is 

operated off peak (i.e., not at its maximum power point). 

However, this entails some loss in energy production. For 

WPPs, a small amount of energy is stored in the rotating 

masses of the turbine blades and hub that can provide this 

damping energy even if the turbine is operating at its 

maximum power point; hence, loss in energy production is 

negligible. Therefore, we also experimented with a PVPP 

controller that holds some energy in reserve by operating at a 

voltage slightly higher than the voltage at the maximum power 

point. 

III. SIMULATION CASES

Simulations were performed in four configurations with 

respect to power output level and oscillation damping 

controller status (enabled or disabled), for both WPPs and 

PVPPs. The cases are listed in Table I. Each of the 

synchronous units from Generator (G) 1 through G4 is 

assumed to be a perfectly coherent representation of multiple 

synchronous generators. The presence of a WPP or PVPP 

leads to the displacement of conventional units, hence leading 

to a reduction in the number of machines comprising a 

coherent unit. This is represented in our simulation by a 

reduction in the inertia of coherent unit G2, which is closest to 

Bus 6, the point of interconnection of the WPP or PVPP. The 

decision to reduce inertia on this coherent unit to a third of its 

original value represents the removal of turbogenerators 

(typical inertia 3 s to 9 s) from a coherent unit, whereas hydro 

units (typical inertia 2 s) remain [24]. Two WPP and PVPP 

power output levels were considered: 0.5 pu and 1 pu. If WPP 

or PVPP power output were to be 0 pu, the provision of the 

oscillation damping service would be impossible. 

IV. RESULTS

Results from the case studies are presented here. The 

simulated two-area system is excited by a large disturbance—

a breaker connecting an impedance load in parallel to Load 2 

located at Bus 9 in Fig. 1 is suddenly switched on. The 

additional load is 1% of Load 2 in terms of real and reactive 

power. Signal-processing methods [15] are applied to the 

resulting electromechanical oscillations to estimate the modal 

frequency, damping, and the mode shape of the system. The 

estimated modes for each case are compared to determine if 

the WPP or PVPP output levels or the controller status 

influence the system modes. 

A. PSD Analysis—Wind

For the case studies, the frequency at each generator bus, 

the voltage phase angle with respect to the calculated center of 

angle [9], the voltage phase angle at each generator bus, and 

the power output at each generator were used to estimate the 

modes of the system after the power system disturbance was 

TABLE I

LIST OF CASES BASED ON WIND POWER/PV POWER OUTPUT AND DAMPING 

CONTROLLER STATUS

Case No.

Power 

Output 

(pu)

Oscillation 

Damper 

Control 

Status

1 0.5 Disabled

2 0.5 Enabled

3 1.0 Disabled

4 1.0 Enabled
4 

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applied. The results of the analysis on the power output at G1 

are presented in Fig. 5. The power output signal was selected 

to analyze modes because the mode estimates were clearest 

for this signal compared to the voltage phase angle and 

frequency signals. 

0 0.5 1 1.5 2 2.5 3

-60

-40

-20

0

20

40

Frequency, Hz

PSD, dB

Generator 1, Wind at 0.5 pu

No Damping

Damping

0 0.5 1 1.5 2 2.5 3

-60

-40

-20

0

20

40

Frequency, Hz

PSD, dB

Generator 1, Wind at 1 pu

No Damping

Damping

Fig. 5. PSD estimates for the G1 power signal with a WPP.

Fig. 5 shows the significant peaks in the PSD, which

indicate that modal frequencies are present in the power 

output at G1 located at Bus 1 for all cases. The top of Fig. 4 

shows the PSD for wind output at 0.5 pu (Case 1 and Case 2). 

The bottom of Fig 4 shows the PSD for wind output at 1 pu 

(Case 3 and Case 4). All of these plots indicate the presence of 

0.76 Hz, both with the damping controller disabled and with it 

enabled. This 0.76-Hz frequency falls in the inter-area 

oscillation range (0.1 Hz to 0.8 Hz) [25], indicating that this 

frequency is associated with one group of generators 

oscillating against another group of generators in the system. 

The presence of the damping controller reduces the peak 

magnitude of the 0.76-Hz mode by approximately 7 dB when 

the wind output is at 0.5 pu and by approximately 5 dB when 

the wind output is at 1 pu. This reduction in the mode’s peak 

magnitude indicates that the damping controller is indeed 

performing its function. The damping controller cannot 

eliminate the mode entirely because of the small amount of 

power that it can supply (limited to 2% of the WPP’s rating)

compared to the power involved in the inter-area oscillation. 

The lack of significant additional peaks when wind power is 

changed from 0.5 pu to 1 pu indicates that the WPP output 

levels investigated do not have a direct impact on the modes 

of the system. Figs. 6, 7, and 8 show that a similar damping 

effect is observed at each generator’s bus. 

0 0.5 1 1.5 2 2.5 3

-120

-100

-80

-60

-40

-20

Frequency, Hz

Magnitude, dB

Generator 2, Wind at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-120

-100

-80

-60

-40

-20

Frequency, Hz

Magnitude, dB

Generator 2, Wind at 1 pu

No Damp

Damp

Fig. 6. PSD estimates for the G2 power signal with a WPP.

0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 3, Wind at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 3, Wind at 1 pu

No Damp

Damp

Fig. 7. PSD estimates for the G3 power signal with a WPP.
5 

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0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 4, Wind at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 4, Wind at 1 pu

No Damp

Damp

Fig. 8. PSD estimates for the G4 power signal with a WPP.

B. PSD Analysis—Solar

An analysis similar to that performed for a WPP was 

carried out with a PVPP of similar rating. The same control 

design was used to provide damping. The plots below (Figs. 9, 

10, 11, and 12) show that the PVPP provided some damping 

for the case when the plant output was 0.5 pu but no damping 

when the plant output was 1 pu. 

When the PVPP was operated at 0.5 pu, the solar insolation 

was held at 0.55 pu. In our case, 1 pu of solar insolation 

corresponded to 1,000 W/sq.m., thus 0.55 pu of solar 

insolation corresponded to 550 W/sq.m. The plant’s output 

was held to 0.5 pu rather than 0.55 pu by operating at a 

suboptimal point on the current-voltage curve; hence, 

additional energy available was available (5% of total plant 

rating) that the controller could use to damp oscillations. 

Fig. 9 shows that with the controller in operation and the 

PVPP at 0.5 pu output, up to 10 dB of damping could be 

achieved. If a mode were close to the stability margin, this 

level of damping could be very beneficial. A similar 10 dB 

damping effect is seen for generators 2, 3 and 4 at 0.5 pu 

PVPP output in Figures 10, 11 and 12. Note that the 

oscillation damper does not have to be activated all the time; 

when an inter-area oscillation is observed, then the PVPP can 

be temporarily derated to perform the function of oscillation 

damping until the oscillation disappears, and subsequently

operation can be returned to normal. Thus, no energy 

production is impacted under normal operation.

The plots at each generator bus corroborate the conclusion 

that the PVPP can provide damping only if there is some 

power held in reserve. The magnitude of the damping 

provided is similar to that provided by the WPP; however, a 

WPP can provide this service in any operating condition. 

Thus, if this service is provided in the future, WPPs may be 

better candidates than PVPPs to provide this service. If gridsized energy storage becomes widespread, storage units may 

take over this role, depending on the relative locations of the 

WPPs and the storage units. 

0 0.5 1 1.5 2 2.5 3

-100

-50

0

50

Frequency, Hz

Magnitude, dB

Generator 1, Solar at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 1, Solar at 1 pu

No Damp

Damp

Fig. 9. PSD estimates for the G1 power signal with a PVPP. 

0 0.5 1 1.5 2 2.5 3

-150

-100

-50

0

Frequency, Hz

Magnitude, dB

Generator 2, Solar at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-150

-100

-50

0

Frequency, Hz

Magnitude, dB

Generator 2, Solar at 1 pu

No Damp

Damp

Fig. 10. PSD estimates for the G2 power signal with a PVPP.
6 

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0 0.5 1 1.5 2 2.5 3

-150

-100

-50

0

50

Frequency, Hz

Magnitude, dB

Generator 3, Solar at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-100

-80

-60

-40

-20

0

Frequency, Hz

Magnitude, dB

Generator 3, Solar at 1 pu

No Damp

Damp

Fig. 11. PSD estimates for the G3 power signal with a PVPP.

0 0.5 1 1.5 2 2.5 3

-100

-50

0

50

Frequency, Hz

Magnitude, dB

Generator 4, Solar at 0.5 pu

No Damp

Damp

0 0.5 1 1.5 2 2.5 3

-100

-50

0

50

Frequency, Hz

Magnitude, dB

Generator 4, Solar at 1 pu

No Damp

Damp

Fig. 12. PSD estimates for the G4 power signal with a PVPP.

V. CONCLUSION

The work discussed in the full paper indicates that WPPs 

and PVPPs can make a significant contribution to the damping 

of inter-area oscillation modes. A controller has been 

developed that allows WPPs and PVPPs to inject power into 

the system out of phase with the inter-area oscillation to 

increase the damping of the oscillation. The controller is able 

to increase the damping of the oscillation, provided there is 

some energy available, either in the rotating masses of the 

WPP or the reserve power held by the PVPP. The reserve 

power is based on derating the PVPP but it need only be held 

while the oscillation is occurring, therefore having negligible 

impact on energy production. Future work will validate these 

results using archived PMU data from real power systems and 

using the capabilities of the National Renewable Energy 

Laboratory’s controllable grid interface. In the future, by 

providing this service, WPPs and PVPPs could play an active 

role in improving the grid’s stability. As VG penetration 

levels increase, such analyses will become increasingly 

relevant.

VI. ACKNOWLEDGEMENT

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08-GO28308 with the National 

Renewable Energy Laboratory.

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VIII. BIOGRAPHIES

Mohit Singh (M’2011) received his M.S. and Ph.D. in electrical engineering 

from the University of Texas at Austin in 2007 and 2011, respectively. His 

research focuses on the dynamic modeling of wind turbine generators. Dr. 

Singh is currently working at the National Renewable Energy Laboratory in

Golden, Colorado, USA, as a postdoctoral researcher in transmission and grid 

integration of renewable energy. His current interests include modeling and 

testing various applications of wind turbine generators and other renewable 

energy resources. He is a member of the IEEE, and he is involved in the 

activities of the IEEE Power and Energy Society. 

Alicia Allen (M’2013) received her Ph.D. in electrical engineering from the 

University of Texas at Austin in 2013. She joined the transmission and grid 

integration group at the National Renewable Energy Laboratory, in Golden, 

Colorado, in April 2013. Her current research focuses on power system 

impacts of the integration of MW-scale wind turbines on the distribution 

system. Her other research interests include wide-area measurement and 

control systems, power system protection, and power system stability.

Eduard Muljadi (M’82-SM’94-F’10) received his Ph.D. in electrical 

engineering from the University of Wisconsin at Madison. From 1988 to 

1992, he taught at California State University at Fresno. In June 1992, he 

joined the National Renewable Energy Laboratory, in Golden, Colorado. His 

current research interests are in the fields of electric machines, power 

electronics, and power systems in general, with emphasis on renewable energy 

applications. He is member of Eta Kappa Nu and Sigma Xi and a fellow of the 

IEEE. He is involved in the activities of the IEEE Industry Application 

Society (IAS), Power Electronics Society, and Power and Energy Society

(PES). He is currently a member of various committees of the IAS and a 

member of the Working Group on Renewable Technologies and Dynamic

Performance Wind Generation Task Force of the PES. He holds two patents in 

power conversion for renewable energy. 

Vahan Gevorgian (M’97) graduated from the Yerevan Polytechnic Institute 

(Armenia) in 1986. During his studies, he concentrated on electrical machines. 

His thesis research dealt with doubly-fed induction generators for stand-alone 

power systems. He obtained his Ph.D. in electrical engineering from the State

Engineering University of Armenia in 1993. His dissertation was devoted to

the modeling of electrical transients in large wind turbine generators.

Dr. Gevorgian is currently working at the National Wind Technology

Center of the National Renewable Energy Laboratory, in Golden, Colorado, 

USA, as a research engineer. His current interests include the modeling and 

testing of various applications of small wind turbine-based power systems.