Model Validation for Wind Turbine Generator Models

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Model Validation for Wind Turbine Generator Models
Ad Hoc Task Force on Wind Generation Model Validation of the IEEE PES Working Group on Dynamic Performance
of Wind Power Generation of the IEEE PES Power System Stability Controls Subcommittee
of the IEEE PES Power System Dynamic Performance Committee,
Mohamed Asmine, Jacques Brochu, Jens Fortmann, Richard Gagnon, Yuriy Kazachkov, Charles-Eric Langlois,
Christian Larose, Eduard Muljadi, Jason MacDowell, Pouyan Pourbeik, Slavomir A. Seman, and Kevin Wiens
Index Terms—Model validation, renewable generation modeling, wind generator modeling.
I. INTRODUCTION
HROUGH the efforts of the WECC Wind Generation
Modeling Task Force (now the Renewable Energy Modeling Task Force) of the WECC Modeling and Validation
Working Group, four generic models have been developed
and proposed for positive sequence stability analysis of wind
turbine generators (WTG), for use in power system studies.
These models have been released in the latest versions of two
commercial software packages GE PSLF™ and Siemens PTI
PSS™E. A sister group under the IEEE Dynamic Performance
of Wind Generation (DPWG) working group has developed a
paper describing the current version of generic models [1]. The
goal of this ad hoc Task Force document is to:
1) identify adequate methods and means by which to validate
such wind turbine generator models;
2) report on case studies of model validation exercises;
3) document potential deficiencies in any of the models based
on attempted model validation exercises, and thus provide
feedback on potential refinements to the model; and
4) provide an outline of suitable methods and approaches for
model validation for wind power plants; this means validating the model both at the wind turbine generator level,
but more importantly at the aggregated plant level.
In general, when we refer to models for power system studies,
there are different categories of models. For basic power system
design studies, steady-state powerflow and short circuit models
are needed, as well as dynamic models for stability analysis. For
T
these types of studies, generally what are referred to as positivesequence (or sometimes RMS) models are adequate—aside: for
full-converter and doubly-fed asynchronous generators, positive-sequence models may be limited in their ability to properly
capture the unit’s response to unbalanced faults; more on this
latter in the paper. For equipment design, and the assessment
of fast electromagnetic transients, detailed three-phase electromagnetic transient (EMT) level models are typically used. This
paper is primarily focused on generic (that is, non-vendor specific and publicly available) positive-sequence models for stability analysis, and does not address electromagnetic transient
(EMT) models.
For a more thorough discussion on the reasons for testing
and model validation, particularly from a policy perspective, see
some of the documents being developed by reliability entities
such as [2]–[5].
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Abstract—This paper summarizes the work of the Ad Hoc Task
Force on Wind Generation Model Validation. The paper describes
the concept of model validation, how this applies to wind turbine
generation systems, and then gives clear examples of the most recent efforts to achieve model validation for wind turbine power
plants. The document ends with a summary of the learning from
the work presented and the conclusions which can be derived. Recommendations are made on the path forward for wind turbine
generator modeling and model validation, primarily focused on
generic models (i.e., standardized and publicly available) for stability analysis in power system studies.
Manuscript received August 09, 2010; revised October 08, 2010; accepted
November 08, 2010. Paper no. TPWRS-00632-2010.
Corresponding author: P. Pourbeik, TF Lead (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TPWRS.2010.2092794
II. MODEL VALIDATION
Based on extensive experience with model validation of other
types of generation (synchronous generation) and transmission
equipment (e.g., FACTS), we can broadly say that the goal of
model validation is to establish that a model and its chosen
parameters adequately represent the dynamic performance of
the “as-installed” device being modeled for the purpose of
power system studies. This statement, however, leaves significant room for interpretation on the meaning of “adequate” and
“power system studies”. Thus, the goal here is to more clearly
define these and then present an outline of possible methods
for achieving model validation and at what point a model is
considered valid for wind turbine generators.
A. Process of Model Validation
Model validation, of any kind, is a three-step process:
1) Define the model and model structure to be used for modeling the device(s) under study—in our case, a wind turbine
generator (or an entire wind power plant1).
2) Collect recorded/measured data from the actual device(s)
to be modeled. Such data is typically collected either from
a set of “staged” tests (e.g., purposefully injecting a small
change in the reference set-point of a controller such as the
voltage set-point of a voltage regulator) or through online
monitoring of the device to see its response to naturally occurring disturbances (e.g., in our case to see the response of
1In the case of an entire wind power plant, one also needs the data related
to the collector system and transformer impedances, cable capacitance, reactive
power compensation at the turbine level, and at the plant level, if any.
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the wind power plant to disturbances on the electric power
system, etc.).
3) Attempt to simulate the same set of tests/events as occurred/forced during the data collection process using the
model(s) in step 1 and compare the simulated response of
the device to the recorded response in step 2. If the two responses match adequately, we have a validated model.
In some cases, where technology is new and innovative (as is the
case with many renewable generation technologies), or where
field tests may be destructive or harmful to the equipment and
network stability, step 2 may involve collecting data through a
series of factory tests, performed by the manufacturer.
stability model testing. The data provided were model specifications or simulations from equipment-design level models.
With this in mind, in the development of the first generation of
generic WTG models, a vendor specific model was considered
the only benchmark available for validating the generic models.
The first generation of generic models were developed for
used in grid interconnection studies—for detailed description of
these models and the model structures, please refer to our sister
TF paper [1]. As such, they should adequately simulate wind
turbine dynamics in terms of terminal voltage, active, and reactive power response to disturbances coming from the grid. They
are not designed for studying the effect of changing wind. Of
all four available generic models, only the WT3 model (DFAGbased wind turbine) has a reference to the initial per unit wind
speed which is used for a simplified calculation of the aerodynamic torque and initial blade pitch angle. All the rest of the
generic models do not use any wind correlation. In the WT1
(conventional induction generator) and the WT2 (variable rotor
resistance induction generator), aerodynamics and pitch control
are replaced by the pseudo-governor action. In the WT4 (full
size converter connected generator), the machine is decoupled
from the grid and the mechanical side is not modeled at all.
Conventional Induction Generator (WT1 Model): The
electrical generator is modeled as a full-order induction machine, as is done for vendor specific models. The remaining
part of the model is the mechanical side—namely the turbine
aerodynamics and control. In the course of preliminary testing
of some vendor specific models of this type, it was noticed
that the response of the mechanical power to step changes in
the machine active power Pelec or the rotor slip WTRBSP can
be approximated by a first or second order transfer function.
This led to the idea of the pseudo-governor mimicking the
complicated joint action of the aerodynamic conversion and
pitch control by calculating the mechanical torque WAEROT
as a function of two inputs: Pelec and WTRBSP (see Fig. 1).
Therefore, the main concern is the validity of the pseudo-governor approach. The response of the Mitsubishi 1000-kW Type
1 wind turbine to a three-phase fault at the 230-kV point of interconnection in terms of real electrical power (Pelec), reactive
power (Qelec), and terminal voltage (Vterm) is illustrated by
plots of Fig. 2, for the vendor specific model and generic WT1
model. In general, we see a relatively good fit, with some difference in attenuation of electrical power (Pelec).
An outstanding question is the validity of these models
for system frequency disturbances. Again, the models have
been tested against the vendor specific models; however, it is
acknowledged that comparison to actual recorded disturbance
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B. Defining the Process and Requirements for Validation
Fig. 1. Pseudo-governor controller.
Thus, we see from the brief outline above that the following
is needed for defining the process of, and the requirements for,
model validation:
1) Model Structures: the actual model structures to be used.
For WTG, these have been defined through the WECC WG
effort and our sister ad hoc TF within the DPWG WG [1].
2) Data Requirements: to define the minimum data that
needs to be recorded in the field or in the factory from the
wind turbine generator(s).
3) Adequacy: to define at what point the model performance
is considered adequate. This is perhaps the most difficult
task and can at times be subjective.
The process of model validation does not necessarily mean a
“perfect” match between the measured and simulated response,
but rather an adequate match that clearly demonstrates:
• capturing the relevant dynamics;
• proper representation of the plant’s dynamic response; and
• the ability to account for possible discrepancies.
C. Wind Turbine Generator Model Validation Case Studies
In this section, we present a series of examples of model validation efforts for wind turbine generator models as conducted
by a few manufacturers and utilities.
As stated in the previous section, model validation is ideally achieved by comparing model response to actual measured
equipment response either in the field or in factory tests. In the
early development of WTG models, however, such data was rare
and only now is there an emphasis on obtaining such data. As
such, the early development of generic WTG models was based
on comparing simulations between the generic models and the
vendor specific proprietary models. Thus, in this section, we will
start with a brief summary of that approach and results achieved,
and then present more recent and rigorous approaches using
measured unit response. Here the focus is on detailed model validation of dynamic stability models, reference [6] can be consulted for an example of model validation with an emphasis also
on validating the collector system and steady-state conditions.
Some other valuable references are [7]–[9].
1) Model Comparison Between Detailed Vendor Specific
Models and Generic Models: Numerous vendor specific
models have been developed in the Siemens PTI PSS™E platform in direct collaboration with WTG vendors. In this effort,
thus far, none of these models were based on any manufacturer
providing results of field or factory tests as a benchmark for
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ASMINE et al.: MODEL VALIDATION FOR WIND TURBINE GENERATOR MODELS
Fig. 2. Results of validation of the WT1 model against Mitsubishi 1000-kW
vendor specific model.
Fig. 4. Results of validation of the WT2 model against Vestas V80 60-Hz
vendor specific model.
Fig. 3. WT2 external rotor resistance control model.
monitoring would be the most effective means of validation.
This remains to be done.
Variable Rotor Resistance Induction Generator (WT2
Model): This model also includes the full-order induction
machine model with the addition of a wound rotor connected
to an external controlled resistor (Fig. 3). The main difference
between this and a vendor specific model is two-fold:
1) the replacement of the aerodynamic conversion by a
pseudo-governor model; and
2) omitting any handling of the rotor current and using Pelec
instead.
3) The look-up table characterizes the reference power versus
slip (linear slope) for operation below rated slip and constant rated power above rated slip. The look-up table is provided in the input data.
Despite these differences, as shown in Fig. 4, we are still able
to obtain a close match between the vendor specific Vestas V80
model and the, respectively, adjusted generic WT2 model.
Doubly-Fed Asynchronous Generator (WT3 Model): This
generic model in its functionality and structure is the closest to
one of the vendor specific models of this type of wind turbine
(the model was based on the GE vendor specific model). Actually, the only difference is the aerodynamic conversion which
was simplified and linearized based [10]. The WT3 generic
model has been successfully adjusted to a couple of wind
Fig. 5. Results of validation of the WT3 model against GE vendor specific
model [10].
turbines of this type and validated against respective vendor
specific models (e.g., see Fig. 5).
Full-Converter Decoupled Generator (WT4 Model): The
concept of this model is based on the idea that from the system
viewpoint, the machine performance is not relevant because it
is decoupled from the grid by a power converter whose controls
determine the impact of the wind turbine. The plots in Fig. 6
demonstrate a good match between the Siemens 2.3-MW wind
turbine vendor specific model and the, respectively, adjusted
WT4 generic model. Since, as stated, in the generic model, only
the line-side converter is modeled and the dynamics of the turbine-generator is not modeled, this model cannot reproduce the
oscillations caused by a rotor torsional mode, which are seen in
electrical power in the vendor specific model.
2) Example of the German Experience With Model Validation: At the international level, the IEC 61400-21 ed. 2
[2] describes measurement requirements for wind turbines. In
Germany, joint measurement requirements for renewable power
sources like wind energy, solar energy, and biomass have been
defined in the FGW TR3 [3], which is based on [2], with some
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a WTG is connected to the grid using a delta-Y transformer
with the star point connected to ground on the low voltage side
(delta on the collector system voltage side), the zero sequence
component of the voltage can be ignored at the terminals of the
WTG. That means that single phase to ground and two phases
to ground faults in the grid translate to phase to phase faults at
the WTG terminals.
An example of a measurement of a voltage dip with phase
measurements, a calculated instantaneous voltage,2 and positive
and negative sequence representation of voltages can be found in
Figs. 7 and 8, subplot 1 and 2 for a three-phase and a two-phase
fault. For the description of the measurement points, please refer
to Fig. 9.
One should note that the measured quantities, even when
extracting positive and negative sequence quantities, still have
higher frequency content. Thus, some filtering is required to
compare measured and simulated results.
The measurement set-up, to emulate a voltage dip, is shown
in Fig. 9. The power generation unit (PGU) is connected to the
grid through a testing device. The testing device is designed
to reduce the voltage at the PGU to a specified level within a
very short period of time. During normal operation, switch S1
is closed and switch S2 is open. In order to reduce the impact
of activating impedance Z2 on the grid, in a first step, switch
S1 is opened. This connects a series impedance of Z1. After
the transients have decayed, switch S2 is closed and the short
circuit impedance Z2 is connected in parallel to the PGU. This
causes a voltage dip at the PGU. After 150–2000 ms depending
on the tests required, switch S2 is opened again. The voltage at
the PGU then recovers. A short time later, switch S1 is closed,
and normal operation resumes.3
Three-phase measurements of voltages and currents must be
performed at least at the measurement points MP1 and MP2.
Measurements at MP2 describe the behavior of the PGU at its
terminals. MP1 is necessary to calculate the data of the grid
representation needed for the simulation.
It is also necessary to measure the wind speed and power
output before the voltage dip, to compute the initial condition
of the wind turbine(s). Compared to rates of change of wind
speed, voltage dips represent very short periods of time. As a
result, the wind speed can generally be assumed to be constant
during a voltage dip. This is in-line with the current assumptions
in the development of the generic WTG models, where wind
speed variations are neglected.
In case the measurements are performed at turbulent sites or
if there are fast changes of wind speed during measurements,
it may be necessary to measure the wind speed during the test.
This is the case if the behavior of the active power of a wind
turbine cannot be modeled with the needed accuracy.
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Fig. 6. Results of validation of the WT4 model against Siemens 2.3-MW
vendor specific model.
extensions to cover specific requirements of the current grid
codes and regulations [12] in Germany. The model validation
Standard that corresponds to the measurement standard is the
FGW TR4 [4]. The FGW TR4 is a “grey box” approach with
respect to the model description. That is, a manufacturer is
required to provide a model and a model description. The
results of simulations are compared to measurements. If the
deviation between measurement and simulation is in within a
certain limit, the model is accepted as validated.
One option would be to compare measurements—which
are three-phase instantaneous measurements—with an EMT
model that yields three-phase values as well. The concern here
is that for power system studies, RMS/stability level models
are needed. Therefore, once the EMT model is validated, then
the stability model would have to be benchmarked against the
EMT model, which leads to accumulated errors and complications. It is therefore required in the FGW TR4 to compare
measurements directly to RMS modeling data.
In the EMT simulation, switching transients and higher frequency components of currents affect the power quality, but
they are not relevant for system stability studies. Therefore, only
the fundamental frequency component is of interest for stability
analysis.
In power systems, the majority of grid faults are single- or
two-phase faults. Such faults lead to unbalanced conditions. In
order to represent such conditions, it is common to use positive,
negative, and zero sequence components of voltages and currents. Three-phase faults have the most severe impact on synchronous generators, but unbalanced faults can have a major
impact on modern WTGs such as the type 3 and 4 designs (and
other converter-based generator technologies), and are more difficult to model in positive-sequence stability programs. Further
work is needed to identify adequate means of addressing the
simulation of unbalanced conditions in positive-sequence programs for these two types of WTGs.
A description of the calculation of positive sequence values
from three-phase measurement values can be found in [2]. If
2Using the Clarke-transformation, the values of a three-phase system (a, b,
c) can be converted into the so-called alpha/beta/0 (also referred to as alpha/
beta/gamma) components. For a balanced three-phase system, the 0 (or gamma)
component is zero, and alpha and beta are rotating phasors at fundamental grid
frequency. The magnitude of the sum of the alpha and beta phasors, the “length”
of the total rotating phasor, is what is referred to here as “instantaneous voltage”.
3Note: although the approached presented here may be a reasonable approach
for emulating a grid disturbance for the purpose of model validation, it does not
precisely represent actual fault conditions and thus is not an adequate means of
assessing actual fault performance of the WTG.
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Fig. 10. Definition of transient intervals: A transient interval begins when a
measured value or a setpoint changes within a short period of time. A transient
interval ends when the measured value remains within a range of 10% of the
stationary value.
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Fig. 7. Measured voltage at measurement point 2 (MP2 Fig. 9) for a symmetric
voltage dip. (1) Measured voltage of the 3-phases. (2) Instantaneous voltage,
positive, and negative sequence of measured voltage. (3) Instantaneous voltage,
positive, and negative sequence voltage of simulation.
Fig. 8. Measured voltage at measurement point 2 (MP2 Fig. 9) for a two-phase
voltage dip. (1) Measured voltage of the three phases. (2) Instantaneous voltage,
positive, and negative sequence of measured voltage. (3) Instantaneous voltage,
positive, and negative sequence voltage of simulation.
Fig. 9. Setup to measure the effect of voltage dips on a power generation unit,
for example a wind turbine. PGU stands for power generating unit, i.e., in this
case, the wind generation unit or plant.
An additional measurement point MP3 is proposed for PGU
connected at low voltage to a test device with medium voltage
terminals. Further turbine measurement data (low voltage, rotor
speed, pitch angle) can be measured to add additional confidence to the model. Additional measurements are required in
case they are necessary to explain the turbine behavior.
3) Quantification of Simulation Error: In order to describe
how well a simulation model is able to represent its physical
counterpart, one approach taken in Germany is to 1) define an
allowed error between measurement and simulation and 2) to
define criteria for quantifying the deviation between measurement and simulation. Thus, in Germany, the following approach
has been taken for the fault-ride through (FRT) analysis. The
FRT Tests can be divided in three periods
A) Period “A”: before the voltage dip. A pre-fault measurement and simulation time of 2 s is recommended to show
that the turbine is operating at stationary conditions.
B) Period “B”: from the beginning of the voltage dip to the
beginning of the voltage recovery. The duration of this
period depends on the grid code requirements and may be
between 140 ms at 0% voltage up to several seconds at
75% voltage.
C) Period “C”: the voltage recovery period.
During each of these periods, there are transient and stationary
intervals. Per the German TR4 document [11], the beginning of
the transient interval is defined as a fast change of a measured
value or a setpoint. The end of a transient period is reached when
of its stationary value
the measured value settles to within
(see Fig. 10). Transient intervals are related to physical effects
that are typically not modeled in detail in the stability simulation
models.
The transients are often linked to phenomena such as
• drive train oscillations;
• the operation of protection devices of the inverter (e.g.,
active-crowbar);
• saturation effects in the electrical equipment, etc.
The German concept developed in broad terms is as follows:
1) defining the start and end for periods A, B and C (see description above of these periods) for the FRT-Test;
2) identifying transient and stationary intervals during periods
A, B and C;
3) comparing the difference of the averages for measurement
and simulation for each interval with an allowed limit;
4) comparing the difference of the positive sequence values
of each stationary interval with an allowed limit;
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Fig. 14. Measurement points for typical wind power plant in Hydro-Québec.
The process of model validation in Germany has been described and illustrated through an example of a 45% voltage dip.
It should be noted that FRT-Tests down to lower voltages and
the validation of active power is significantly more demanding.
Nonetheless, the process has been shown to work adequately. In
[11], an example is shown also with a two-phase fault, used to
validate a model of a WT3 turbine.
4) Hydro-Québec Experience With Model Validation:
Hydro-Quebec plans to integrate 4000 MW of wind power to
its network by 2015. Consequently, research has been conducted to insure reliable integration of wind generation into the
system. Here a brief account is given on some of the pertinent
issues related to modeling and model validation. See [13]–[16]
for more details.
Validation of Type-3 Wind Turbine and Wind Power Plant
Models Using Online Disturbance Monitoring: For the purpose
of model validation, online monitoring equipment has been installed on a typical wind power plant (WPP) connected to the
Hydro-Quebec network. This WPP is composed of 73 1.5-MW
type-3 wind turbines. Fig. 14 shows the WPP, with the voltage
and currents monitored and their locations at the turbine, feeder,
and point of interconnection (POI) levels.
From 2007 to 2009, various disturbances (e.g., faults and frequency deviations) were recorded. Those recordings have been
used to build and validate a type-3 WTG model in the EMT domain.
The model validation process used is based on playback techniques, where the model is fed with recorded voltages from the
actual wind turbine, and validation is confirmed when the model
produces the same current as those recorded during the disturbance. Following the same approach of waveform playback, the
entire WPP model has also been validated, using recorded voltages and currents at the POI level. The wind plant management
system was also modeled and validated in the mean time.
Fig. 15 shows an example of the comparison between simulation and field measurement for an event. It can be seen that
the conformity of the model with the field measurements is very
good for this particular event.
Such good correspondence of the model for a number of
different operating conditions and recorded disturbances has
greatly contributed to increase the confidence in the validity
of the model. Fine-tuning the model is a process relatively
straightforward for small disturbance, but it becomes more
complex with large and/or unbalanced disturbance due to various nonlinearities. Regardless of the disturbance severity, this
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Fig. 11. (1) Measured and (2) simulated reactive current at measurement
point 2 for a symmetric voltage dip. The average values are calculated
for each interval (doted lines) for (1) measurement and (2) simulation.
Transient periods start at = 1, t = 2, and t = 4 s.
Fig. 12. Difference between measurement and simulation of reactive currents
at measurement point 2 for a symmetrical voltage dip as well as allowed tolerance. In (1), the difference of averages values (blue) and the allowed tolerance
(red) is shown; in (2), the difference of positive sequence values (blue) and allowed tolerances for the stationary intervals (red) is displayed.
Fig. 13. Measurement and simulation of reactive currents of a FRT-Test with
a voltage dip down to 45% rated voltage. The voltage setpoint has been set to
unity, the reactive power is changed as the voltage changes. Transient periods
are highlighted with red color, stationary periods green.
5) calculating a global deviation based on a weighted average
over the entire FRT-Test.
A more detailed account (with a discussion of the method for
calculating these averages) is provided in [11]. Figs. 11–13 show
an example of applying this method for a balanced 45% voltage
dip applied through the FRT test to a doubly-fed asynchronous
generator (type 3) WTG.
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Fig. 15. Comparison of recorded and simulated waveforms at the type-3 WTG
level during a fault on the network.
approach requires a good understanding of internal dynamics
and control strategies of the WTG to model.
Validation of a Type-3 Wind Turbine Stability Model by
Comparison With an EMT Model: Hydro-Quebec uses an
EMTP-PSS/E modeling interface to build its own Siemens
PTI PSS™E user-written models. The validated EMT model
discussed in the previous section was used to help design and
validate a type-3 WTG stability model. This stability model
includes dynamics of the asynchronous generator and WPP
voltage control. In this case, positive-sequence phasor signals
from simulations of both models were used to compare and
validate their behavior. Fig. 16 shows an example of this validation. This approach is helpful to gaining a better understanding
of the impact of the numerous levels of controls of the type-3
WTG on the network. See [13] for more details.
Validation of a Type 2 Wind Turbine Stability Model Using
Online Disturbance Monitoring: A type-2 WTG model has
also been developed by Hydro-Quebec for stability studies. The
model has been adjusted and validated with online monitoring
from the network for various disturbances including frequency
deviations, faults, and a local voltage collapse following the
loss of a line (low short-circuit level). Fig. 17 shows an example
of validation for a remote fault as seen from a substation collecting power from two WPPs with type-2 WTGs. The resulting
model is now widely used in Hydro-Quebec for planning and
operation studies.
Validation of Aggregation Techniques for WPP Modeling:
The use of aggregated models for WPP modeling is a common
practice. However, until recently, the precision and the validity
of such approximation, for various types of power system
studies, remained to be evaluated.
Consequently, in order to validate the precision of aggregated
models for EMT simulation, a detailed model of an actual WPP
in the EMT domain, with all wind turbines represented and
the collector network, was developed. Given the huge amount
Fig. 16. Response of the Hydro-Quebec stability (EMTP-PSS/E) and EMT
(Matlab/SPS) models compared to a generic stability model for a 150-ms threephase fault at the POI.
Fig. 17. Comparison of recorded and simulated waveforms (using the HydroQuebec model) for a remote fault seen from a substation with type-2 WTGs.
of computation this kind of model requires, efficient modeling
techniques were developed [14] to reduce simulation time to a
practical level.
With the availability of a fast-simulating detailed model of
a WPP, an exhaustive simulation study [15] was performed for
validating the adequacy of the NREL equivalencing method [17]
and [18] for modeling WPP. As a result, the method proposed by
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Fig. 19. Generic WT4 model.
Fig. 18. Comparison of a detailed WPP model with a one-, two-, and
four-WTG equivalent WPP models. More than one equivalent WTG is not
necessary for modeling a WPP when all WTGs are exposed to the same wind
speed.
NREL appears to offer precise results for various types of disturbances and operating conditions, for both EMT and stability
studies. Fig. 18 shows the performance of the NREL method
for WPP modeling. In this figure, a two-phase fault is applied
to four different models of WPP: a detailed 73-WTG model,
and three different aggregated WPPs consisting of one, two,
and four equivalent WTGs. The significance of this plot is to
show that a single aggregated equivalent generator with a single
equivalent impedance representing the entire collector system
can adequately represent plant response for studying grid disturbances—admittedly this assumes one is studying a transient
phenomena and wind speed is constant for the duration of the
grid event.
Generic Equivalent Collector System Parameters for Large
WPP for Preliminary Studies: The equivalent collector systems
of 17 WPPs rated between 50 and 300 MW were analyzed.
Using this sample, a set of generic equivalent collector system
parameters were calculated to be used for prospective powerflow and stability studies of WPPs for which little or no information is available yet. An exhaustive sensitivity study based
on EMT simulations has confirmed the adequacy of the generic
equivalent collector system parameters proposed in [16].
5) ABB Experience With Model Validation for a Type-4
WTG Using the Generic WT4 Model: Using documentation in
Siemens PTI PSS™E for the WECC developed WT4 generic
model, ABB developed the model shown below.
Fig. 20. Overall model of the full-converter system.
The WT4 Generic Wind Model comprises of two modules as
follows:
• WT4G: the power converter/generator module;
• WT4E: the electrical control module.
Fig. 19 shows the interaction between these modules. The power
converter/generator module (Fig. 20) calculates the current injection to the grid based on filtered active and reactive power
commands from the electrical control module. Both components
of the injected current are processed under the high/low voltage
conditions by means of a special logic.
The ABB modified model can be represented in different
simulation environments. The original structure of the WECC
model has been adapted for modeling of ABB’s Full Converter
Wind Turbine Drive. The model does not include control (pitch
and power) and mechanical model of the wind turbine that shall
be developed by the WTG manufacturer. The electric grid model
is not included.
The active power command (Pord) is set by an external power
reference (WT control). It is controlled by a proportional-integral (PI) controller with anti-windup limits. The block diagram
of active power controller is shown in Fig. 21.
There are three options of reactive power control. Reactive
power can be controlled by voltage level, by power factor, or by
external reference with two flags, VARFLG and PFAFLG.
Power factor control calculates reactive power reference
based on the active power of the generator. Reactive power
control by voltage level controls reactive power reference by a
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Fig. 24. Model output.
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Fig. 21. Active power control.
Fig. 22. Reactive power control.
Fig. 23. Current limitation control.
PI-controller with anti-windup limits. The block diagram of the
reactive power control is shown in Fig. 22.
The converter currents are limited by logic which is defined
in the current limitation block. There are two modes of current
limitations. One mode is used during normal operation and another is used during low voltage FRT. The total current and active and reactive current limits are defined. In addition, there is
a lookup-table which defines maximum reactive current for certain voltage levels during FRT mode. This is shown in Fig. 23.
A voltage dip is recognized by comparators which compare the
voltage level to predefined voltage limits. Hysteresis is implemented by an S-R flip-flop to avoid hunting.
Fig. 25. Voltage dip for model versus measured response for a three-phase
voltage dip.
The output of converter/generator model is current, and active
and reactive power (see Fig. 24). Rate of change of active current
is rate limited. Reactive current is controlled by the command
which is defined in the electrical control model except during
voltage dips when it is defined by a look-up table.
Thus, a test was performed to compare the simulation results
of the generic model with measured factory tests of the fullconverter system. The results for a three-phase voltage dip are
shown in Figs. 25–28. Clearly, we have a good match. The measurements are from a rated-power factory test of the 2.5-MW
wind turbine cage-induction generator, connected to the distribution network through a full power converter and generator
step-up transformer.
ABB has presented the validation of a detailed DFAG model
also by full-power site test measurements in [19].
The full-converter WTG discussed above has also been successfully validated using the German TR 4 standard methodology (see [4] and Section B-2. One comment, however, is that
there is a risk of having up to a 100% error (in short time periods)
that is not real—the reason is that at the end of the fault (clearance) due to the typically long integration time step (relative to
the actual speed of controls, which is in the kilo-Hz range), it
is difficult to rapidly emulate fault clearing using the generic
model. The action is of course much faster (sub-cycle) in the
case of the real equipment. Adjustment could be made to improve the model. Despite this error, the model still fulfils the
TR4 requirement.
6) Wind Power Facility Modeling Experience in Alberta:
Within the province of Alberta, the Alberta Electrical System
Operator (AESO) is responsible for the safe, reliable, and
economic planning and operation of the Alberta Interconnected
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Fig. 26. Total converter current for model versus measured response for a threephase voltage dip.
Fig. 27. Active converter current for model versus measured response for a
three-phase voltage dip.
Electric System. The Alberta grid has approximately 12 700
MW of generation, in excess of 7900 MW of nominal load and
a peak load of over 9700 MW [20]. At this time, there is one
synchronous interconnection path to other jurisdictions within
WECC and a small DC connection to the east.
Wind Power Facilities (WPFs) first came online in Alberta
in the late 1990s and presently there are eleven transmission
connected WPFs totaling 563 MW. Alberta has a strong wind
resource with an annual capacity factor of approximately 35%.
The AESO’s 2009 long-term plan indicates that wind power
generation could be at least 3500 MW by 2020 [21] and [22].
AESO, in 2004, developed an interconnection technical requirement for wind power facilities. In these requirements are
provisions that require WPFs to provide modeling data and validation tests for the models.
Specifically the 2004 WPF Technical Requirements state the
following:
“The WPF owner shall provide a WTG model with validated data demonstrated by a physical performance test of
at least one WTG for every type/model of WTG used at the
facility.”
“The WPF owner shall provide a voltage regulation model
with validated data demonstrated by a physical perfor-
Fig. 28. Reactive converter current for model versus measured response for a
three-phase voltage dip.
mance test of at least one voltage regulation device used
at the WPF.”
“The WPF owner shall provide all pertinent data to the
AESO to allow the modeling of the WTGs, transformers,
collector system(s) and control systems at the WPF.”
AESO looks for two different pieces of information. One is the
appropriate model for the individual WTG, validated by a type
test. This test could take place at the factory, some other facility,
etc. The other is a voltage regulation model of the entire facility
validated by a test of the facility. Validation of the voltage regulation model has typically been performed by applying a step
change to the reference point and recording the results and presenting the measured and modeled values graphically (see example below).
7) Wind Generator Testing and Model Validation Effort by
GE: This section presents results from a subset of tests performed at a 70-MW wind plant in Canada. The plant is configured as shown in Fig. 29. This plant uses GE 1.5-MW SLE wind
turbine-generators and also employs the GE WindCONTROL
plant management system. The WindCONTROL system allows
coordination of all online turbine-generators for plant-level fast
and smooth voltage regulation at the POI, located contractually
at the 25-kV substation bus.
The intent of testing at this plant is for model validation. Field
tests were performed, data captured, results analyzed, and then
compared to simulation results from a model of the plant and
interconnecting grid built in GE’s PSLF™ simulation software.
Two tests are performed:
1) step stimulus test: using a voltage step reference injection;
2) external physical stimulus test: using plant capacitor bank
switching.
Fig. 30 shows the voltage and reactive power control hierarchy
at the individual turbine-generator level and the plant-wide
level.
Step Stimulus Test: The fastest dynamic response in the
wind plant model is the unit voltage regulator loop. This control
loop senses the terminal voltage of the individual WTG (at 575
volts), compares it to the local dynamically modified voltage
reference, and instructs the generator converter to deliver reactive current to the collector system of the plant.
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Fig. 31. Location of injected stimulus for voltage step test.
Fig. 29. Example wind plant layout for testing.
Fig. 32. Wind turbine-generator level voltage step test response.
Fig. 30. Reactive power and voltage control structure for GE 1.5-MW wind
turbine and windcontrol plant management.
The reactive current in the model is produced by an internal
voltage, E”q, behind a machine reactance. The dynamics of this
fast loop are dominated by the gain, Kvi.
To test the fast regulator loop, a step reference signal is injected into the node feeding the fast voltage regulator in the
WTG converter. Fig. 31 shows the location of the injected step
signal in red. The resulting change in Q and voltage at the terminals of the machine is measured. In this test, the WTG communication with the plant-wide WindCONTROL is shut down,
so Qcmd (VAR command signal from plant regulator) is held
constant for constant Q regulation.
Fig. 32 shows a comparison between measured wind turbine
generator reactive power response and simulated response
using, a GE PSLF™ model for this test. The initial response of
reactive power is extremely fast, rising to full output for this
stimulus in about 200 ms—response similar to that of an SVC.
When the WTG is isolated from the plant level supervisory
control, the gain KQI is reduced and the turbine operates in a
constant VAR mode. This is accounted for both in the actual
response of the WTG control as well as in the model. Fig. 32
shows the results of the voltage step test when one WTG is
isolated. In this case, since Qcmd is held constant, the initial
rapid increase in reactive power output is slowly compensated
for by the WTG reactive control as identified in Fig. 29, and thus
slowly brought back to the initial Q value.
External Physical Stimulus Test: In the case of our example
system, a 10-MVAr capacitor bank, located at the 25-kV wind
plant collector bus, is switched offline as an external physical
stimulus. Fig. 33 shows detailed response to capacitor switching
from the WindCONTROL. The red curve (Q_ACTUAL [kvar])
shows that total plant reactive power initially drops after the
switching action, but the fast autonomous controls on each turbine generator quickly and stably respond to increase reactive
power generated by individual turbines, shown by the orange
curve (Q_TURBINES [kvar]). The WindCONTROL command
(Q_CMD) distributed to the turbines is shown in blue. The response of Q_CMD is dominated by the gains of the voltage
regulator portion of the WindCONTROL, specifically the proportional gain, Kpv, and integral gain, Kiv. The difference between the response of the individual turbines (Q_TURBINES
[kvar]) and the WindCONTROL command (Q_CMD) is due to
the dynamics of the individual wind turbines. Thus, the coordinated response of the wind plant and the individual turbines is
multi-modal: a fast initial response to address severe perturbations as well as a slower refinement. For purposes of this test,
the automatic control of the capacitor bank by WindCONTROL
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Fig. 33. The 10-MVAr capacitor removal response measured from WindCONTROL.
was disabled and manual switching was used as a stimulus to
record individual WTG response.
The green curve in Fig. 34 shows that when the capacitor
is switched offline, the measured voltage at the point of interconnection (or POI) decreases due to reduced reactive power
flowing into the grid. The response of the individual WTGs is
to rapidly increase reactive output to make up for the loss of
reactive power supplied by the shunt capacitor. The plant level
control then responds to this initial under-voltage condition and
attempts to restore the POI voltage by increasing each wind generator’s reactive output by equal amounts until the plant voltage
settles to the control set point determined by the operator. The
lower traces in Fig. 34 shows a gap during the period when the
capacitor bank is online between total plant Q (Q_ACTUAL)
and summation of Q out of each WTG (Q_TURBINES). This
gap represents the capacitive reactance added by the shunt bank.
When the capacitor is switched offline, the gap between Q_ACTUAL and Q_TURBINES closes and all reactive power is supplied solely from the WTGs. The initial loss in plant reactive
power is mitigated within approximately 15 s as each WTG settles to a new, increased level operating point of reactive power.
This new increased Q level for each WTG is the total Q amount
increased out of all units online in the plant, divided by n units
online at the time of the test.
Fig. 35 shows a comparison between these measured values
and the simulation results of the GE PSLF™ model. Model
outputs Qg, Q plant, Qcmd, and Vreg correspond to measured
Q_TURBINES, Q_ACTUAL, Q_CMD, and U_LINELINE, respectively. This plot shows that the model performance adequately represents what is happening in the field. The response
matches closely, with a difference immediately following the
switching operation being due to lower sampling rate in the measurement than in the GE PSLF™ simulation.
III. SUMMARY AND OBSERVATION ON MODELING AND MODEL
VALIDATION FOR WIND POWER PLANTS
The need for modeling and modeling validation should be apparent to the reader, as it is a mandated need in many continents
Fig. 34. The 10-MVAR capacitor removal response—POI variables.
Fig. 35. The 10-MVAr capacitor removal field test versus simulation results.
worldwide. Documents such as [4], [5], [23], and [24] elaborate
more specifically on the need and purpose.
The examples in the prior section have illustrated several
model validation exercises. The examples cover the range of
viable approaches to model validation, which can be broadly
categorized as follows:
1) Calibrating Against Detailed Equipment Models: The
first example illustrates the approach of calibrating stability models (i.e., positive-sequence or what is referred to
as RMS models in Europe) against more detailed manufacturer models which are often three-phase EMT models.
The limitation with this approach is that it does not compare the stability model performance directly with actual
measured response and so there can be cumulative errors
going from measurement comparison to EMT models, and
then EMT model comparison to stability models.
2) Type Test/Staged Tests: In this approach, a “type” of particular design is tested either by a manufacturer, or an independent third party, through a staged test. This can be
done either in the factory (e.g., see ABB example in previous section) or in the field (e.g., see GE or German examples). The staged test can be for example to exercise
the fault-ride through capability of the unit (e.g., German
example, or ABB example) or a particular functionality
such as voltage regulation (e.g., GE example with voltage
reference step-test). This process involves invoking a programmed event (e.g., forcing a momentary drop in voltage)
and recording the response of the unit to the event. Then the
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• One further issue is a need to convert from instantaneous
three-phase to RMS quantities for comparison with stability models. There is some error introduced by this
process—plus the fact that the RMS values may have a
fundamental frequency component under nonsymmetrical
conditions which must be filtered.
The cumulative effect of all these potential sources of error can
lead to as much as 3% to 5% relative error in the calculated real
and reactive power, and measured voltage and currents. As such,
one must be cognizant of these facts and not require a match
between measurement and simulation that is unreasonable.
In the case of converter-based wind turbine generation technologies, i.e., type 3 and 4 WTG, there is the added complication of dealing with modeling unbalanced conditions. This is
particularly challenging for type-3 WTG, in which the protection and control strategy during fault-ride through events can be
quite different among vendors. Thus, it is difficult at present to
capture the dynamics faithfully for all such equipment with the
first generation of generic models. This requires further study.
In the end, it should be noted that dynamic models used for
power system stability simulations are derived after significant
simplifications of actual equipment controls and physics, for it
is neither practical nor appropriate to achieve perfect representation of all dynamics (e.g., down to electromagnetic transients
level). Thus, it is a continuous challenge to provide models that
adequately represent dynamics in the time frame of interest for
stability studies while not making the models and model validation overly complex.
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measured and simulated responses are compared, and the
simulation model may be refined until good agreement is
achieved between the two.
3) Monitoring-Based Model Validation: In this approach,
monitoring equipment is installed at the power plant and
data is gathered based on ongoing performance of and operations of the wind power plant. Data is also captured
(e.g., by digital fault recorders) for events external to the
plant such as faults on the transmission grid, and other
voltage or frequency disturbances grid wide. This measured plant response is then compared to simulated response for model validation. The Hydro-Québec example
in the previous section illustrates this—however, further
work is needed to extend the approach to more publicly
available models. For synchronous generation, this type of
approach has been successfully illustrated [25], even for
meeting reliability standards [26].
As presented, the most notable approaches to model validation
are measurement based. This is true for any equipment. A measurement is made and the recorded data is post-processed, and
then, the measurement is compared to the simulation model to
validate it. Doing so requires some sort of criteria or approach to
identify when the model is considered valid—i.e., how good a fit
between measurement and simulation is “good enough”. There
are two approaches to this requirement. One is to specify the requirement and define it analytically—the example in the prior
section on the work done in Germany clearly illustrates this. Another is to rely on expert engineering judgment (i.e., human decision) to decide on the validity—presently, this is what is done
in the United States in many of the standards such as the generation model validation certification process in WECC, and imminent NERC MOD standards of generator model validation. In
either case, it is important to understand the limitations and errors that are introduced by measurement. These can be broadly
categorized as follows:
• Most measurement potential transformers (PTs) and current transformers (CTs) will have a tolerance of at least 1%
(or more). In addition, errors from the analogue to digital
conversion of the measurements (this can introduce both
phase and magnitude errors, as can also the measurement
PTs and CTs) can occur in recording the data. These can
lead to cumulative errors in the range of 2%–3% in the
measured active and reactive power, and voltages.
• For fast transients (high frequency components), additional
errors can occur due to bandwidth limitations in transducers and measurement equipment.
• The measurement and control equipment used in the wind
power plant controls does not usually have the same accuracy as calibrated measurement equipment—this can lead
to an additional error in the range of 1%.
• When designing equipment, all electrical (and mechanical)
components will have associated design tolerances. Thus,
the generation equipment parameters can easily have tolerances of the order of magnitude of 5%.
• There are aspects of equipment behavior, such as magnetic
saturation and hysteresis, that cannot be accurately modeled nor their effects totally eliminated from measurement.
13
IV. CONCLUSIONS
Through several clear examples, it has been illustrated that
model validation for wind generation is achievable and a
fruitful exercise. The examples have shown various approaches
to model validation. As the technology evolves, it is also clear
that the models too will need to evolve. Many of the examples
have shown the feasibility to validated measurement against the
generic (simplified) first generation of wind turbine generator
models. These generic models are non-manufacturer specific
and public.
Measurement-based model validation is the most fruitful exercise, rather than model comparison between different classes
of models (e.g., benchmarking a stability model versus an EMT
model). In practical terms, at least the voltage, real and reactive power, and bus voltage frequency at the point of common
coupling need to be measured for a wind power plant for model
validation purposes.4 Also, it is prudent to install measurement
equipment such as digital-fault recorders and phasor-measurement units at the interconnecting substation of a wind power
plant to collect such data. In addition to these basic variables,
it would be beneficial to record variables such as the status and
output of ancillary devices (e.g., reactive compensation devices
such as STATCOMs or mechanically switched capacitors) and
4Note: in actuality, three-phase voltage and currents are the measured quantities, real and reactive power and bus frequency are then calculated from the
measured voltage and currents. Most modern digital recording devices are able
to make these calculations internally.
14
IEEE TRANSACTIONS ON POWER SYSTEMS
[7] Y. Kazachkov, J. Feltes, and R. Zavadil, “Modeling wind farms for
power system stability studies,” in Proc. IEEE PES 2003 General
Meeting, Toronto, ON, Canada.
[8] Y. Kazachkov and S. Stapleton, “Does the generic dynamic simulation
wind turbine model exist?,” in Proc. WindPower 2005, Denver, CO,
May 2005.
[9] Y. Kazachkov and R. Voelzke, “Modeling wind farms for power system
load flow and stability studies,” in Proc. IEEE PowerTech 2005, St.
Petersburg, Russia, Jun. 2005.
[10] W. W. Price and J. J. Sanchez-Gasca, “Simplified wind turbine generator aerodynamic models for transient stability studies,” in Proc. IEEE
PES 2006 Power Systems Conf. Expo. (PSCE), Atlanta, GA, Oct. 1,
2006, pp. 986–992.
[11] J. Fortmann, S. Engelhardt, J. Kretschmann, C. Feltes, and I. Erlich,
“Validation of an RMS DFIG simulation model according to new
German model validation standard FGW TR4 at balanced and unbalanced grid faults,” in Proc. 8th Int. Workshop Large-Scale Integration
of Wind Power Into Power Systems as Well as on Transmission Networks for Offshore Wind Farms, Bremen, Germany, 2009.
[12] Verordnung zu systemdienstleistungen durch Windenergieanlagen (systemdienstleistungsverordnung—Sdlwindv). Germany: BMU, 2009.
[13] C.-E. Langlois, D. Lefebvre, L. Dubé, and R. Gagnon, “Developing
a Type-III Wind Turbine Model for Stability Studies of the HydroQuebec Network,” in Proc. 8th Int. Workshop Large-Scale Integration
of Wind Power Into Power Systems, Bremen, Germany, Oct. 2009, pp.
674–679.
[14] C. Larose, R. Gagnon, G. Turmel, P. Giroux, J. Brochu, D. McNabb,
and D. Lefebvre, “Large wind power plant modeling techniques for
power system simulation studies,” in Proc. 8th Int. Workshop LargeScale Integration of Wind Power Into Power Systems, Bremen, Germany, Oct. 2009, pp. 472–478.
[15] J. Brochu, C. Larose, and R. Gagnon, “Validation of single-and
multiple-machine equivalents for modeling wind power plants,” IEEE
Trans. Energy Convers., Apr. 2010.
[16] J. Brochu, C. Larose, and R. Gagnon, “Generic equivalent collector
system parameters for large wind power plants,” IEEE Transactions on
Energy Conversion, submitted for publication.
[17] E. Muljadi, C. P. Butterfield, A. Ellis, J. Mechenbier, J. Hochheimer, R.
Young, N. Miller, R. Delmerico, R. Zavadil, and J. C. Smith, “Equivalencing the collector system of a large wind power plant,” in Proc.
IEEE Power Eng. Soc. General Meeting, Montreal, QC, Canada, Jun.
2006.
[18] E. Muljadi, S. Pasupulati, A. Ellis, and D. Kosterov, “Method of equivalencing for a large wind power plant with multiple turbine representation,” in Proc. IEEE Power and Energy Soc. General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, 2008.
[19] S. Seman, J. Niiranen, R. VirtanenJ-, and P. Matsinen, “Low voltage
ride-through analysis of 2 MW DFIG wind turbine—grid code compliance validations,” in Proc. IEEE PES General Meeting, Pittsburgh,
PA, Jul. 2008.
[20] AESO Future Demand and Load Outlook (2008–2028).
[21] AESO Long-Term Transmission System Plan 2009, Appendix G,
Graph—Scenarios for Wind Power Development in Alberta, Probable
Alberta Wind MW, Expected Scenario, p. 287.
[22] AESO Connection Queue, 2010.
[23] N. Miller, K. Clark, J. MacDowell, and W. Barton, “Experience with
field and factory testing for model validation of GE wind plants,” in
Proc. Eur. Wind Energy Conf. Exhib., Brussels, Belgium, Mar./Apr.
2008.
[24] R. Piwko, N. Miller, and J. MacDowell, “Field testing & model validation of wind plants,” in Proc. IEEE PES General Meeting, Pittsburg,
PA, Jul. 2008.
[25] P. Pourbeik, “Automated parameter derivation for power plant models
from system disturbance data,” in Proc. IEEE PES General Meeting
2009, Calgary, AB, Canada, Jul. 2009.
[26] Tri-State Successfully Implements Power Plant Parameter Derivation Software Tool, EPRI Success Story, Apr.
2010.
[Online].
Available:
http://my.epri.com/portal/server.
pt?Product_id=000000000001020917.
[27] NERC Special Report, Standard Models for Variable Generation, 2010.
[Online]. Available: http://www.nerc.com.
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voltage, real, and reactive power at the terminals of a few representative generating units in the plant. A more clear and thorough definition of measurement is needed. This is presently
being pursued by other groups such as the IEC TC88 WG27
and the NERC IVGTF [27].
It should be understood that there are inherent errors in the
measurement process (up to 3%–5%). Furthermore, there are
dynamics, particularly associated with fast transients, which
are seen in measurements and are not represented in stability
models. Moreover, in the case of unbalanced faults, measurement data needs to be filtered to extract the positive sequence
response for comparison to stability models—this introduces
further complications, for the negative and zero sequence
behavior cannot be faithfully represented in positive-sequence
simulation tools. All these considerations must be fully recognized when embarking on model validation, and they must be
taken into consideration when setting standards for acceptance
of model validation results. That is, when comparing the simulated response to measured response and trying to identify
when a good-enough fit has been achieved, the criteria used
should fully recognize all these issues and potential sources of
error that are unavoidable.
ACKNOWLEDGMENT
The authors would like to thank the chairman of the parent
working group, A. Ellis, for his leadership, as well as other
members and officers of the group E. Camm (WG Secretary), J.
Sanchez-Gasca (Lead of the sister TF), N. Miller (previous WG
chairman), J. Smith (WG Vice-Chair), and many others who attended WG meetings and provided fruitful comments. The authors also would like to thank J. Nygaard Nielsen from Siemens
Wind Power for assisting with the simulations associated with
Fig. 6.
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[2] IEC: IEC 61400-21 ed. 2, Wind Turbine Generator Systems—Part 21:
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Mittel-, Hoch- und Höchstspannungsnetz s, Apr. 30, 2009.
[4] FGW: Technical Guidelines for Power Generating Units. Part 4 Demands on Modeling and Validating Simulation Models of the Electrical
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