Understanding IEC standard wind turbine models ...

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Understanding IEC standard wind turbine models using SimPowerSystems

Das, Kaushik; Hansen, Anca Daniela; Sørensen, Poul Ejnar Published in: Wind Engineering DOI: 10.1177/0309524X16642058 Publication date: 2016 Document Version Publisher's PDF, also known as Version of record Link to publication

Citation (APA): Das, K., Hansen, A. D., & Sørensen, P. E. (2016). Understanding IEC standard wind turbine models using SimPowerSystems. Wind Engineering, 40(3), 212–227. 10.1177/0309524X16642058

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642058 research-article2016

WIE0010.1177/0309524X16642058Wind EngineeringDas et al.

Original Article

Understanding IEC standard wind turbine models using SimPowerSystems

Wind Engineering 2016, Vol. 40(3) 212­–227 © The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0309524X16642058 wie.sagepub.com

Kaushik Das, Anca D Hansen and Poul E Sørensen Abstract This article describes and exemplifies the IEC 61400-27 generic wind turbine models through an interactive multimedia learning environment - Matlab SimPowerSystems. The article aims help engineers with different backgrounds to get a better understanding of wind turbine dynamics and control by easily conducting different study simulations in the SimPowerSystems platform.

Keywords IEC, wind turbine, dynamic models, generic models, SimPowerSystems

Introduction Renewable energy resources, such as wind and solar, are considered highly promising in the face of growing concerns for the environment, energy conservation and sustainable development. Wind energy will increasingly make large contributions to the electricity market throughout the world. It is expected that wind energy will meet 15.7% of the European electricity demand by 2020 (230 GW), and by 2030 this will increase to 28.5% (400 GW) (EWEA, 2011). In order for the industry to sustain continuous development, it needs professionals and experts in this field. With this aim in mind, it is essential to build up learning methodologies regarding wind power for engineers from varied backgrounds. This article aims to aid engineers by both explaining and providing a scientific understanding and guidance for generic standard wind turbine (WT) models. Wind energy is a multidisciplinary area, as can be seen by the non-exhaustive list shown in Figure 1. To name a few of the many broad disciplines, wind engineering involves: mechanical engineering pertaining to the blades, structures and gear box; meteorology pertaining to weather and climatic effects on wind; aerodynamic studies such as wake effects; and electrical engineering addressing the generation, transmission and integration of WTs into the power system. In this context, engineers with different backgrounds need to learn and understand the functionalities and control of wind power generation with the help of simple generic models. Generally, dynamic models for WTs are proprietary models, which are very detailed and complicated. In this light, simple generic WT models are needed to help the engineers towards a better understanding of WT dynamics and control. A number of publications (Altin et al., 2014; Lindgren et al., 2012; Margaris et al., 2012; Zeni et al., 2012; Zhao et al., 2013, 2015) have already touched upon the topic of wind turbine generic standard models. The main focus of this article is on understanding the characteristics and performance of the generic models described in the IEC 61400-27 standard (IEC, 2015), with focus on both fixed-speed wind turbines and fully-rated converter-based variable-speed wind turbines. In this article, several sub-models of the IEC 61400-27 generic model, such as the aerodynamic, mechanical and generator models as well as the aerodynamic and electrical control of a WT are reviewed, explained and exemplified from a learning perspective point of view through an interactive learning process. Interactive learning with multimedia tools, like Matlab/Simulink, is becoming increasingly popular in education because of its advantages in providing a positive impact in understanding and developing expertise on a subject. Generic WT models presented in this article are developed in Matlab/Simulink and incorporated into the power system model developed in Matlab, namely the Wind Energy, Technical University of Denmark, Denmark Corresponding author: Kaushik Das, Wind Energy, Technical University of Denmark,RISØ, Roskilde, Denmark. Email: [email protected]

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Figure 1.  The multidisciplinary nature of wind energy engineering.

SimPowerSystems toolbar. SimPowerSystems is a modern tool that can be used to rapidly build models that simulate power systems. SimPowerSystems (MathWorks, 2016) uses the Simulink environment and allows users to perform different types of studies such as power flow analysis, frequency analysis and transient analysis. SimPowerSystems also provides component libraries of application-specific models, including models of common AC and DC electric drives, flexible AC transmission systems, and renewable energy systems. The developed models can also be used to obtain an understanding of the WTs’ dynamics and control. The purpose of this article is to explain and provide a deeper insight into generic WT models for short-term power system stability studies. This article is structured as follows. The second section deals with the need for WT generic models. The third section explains WT types. The fourth section deals with generic standard WT models. The fifth section describes the possible extensions of the generic models in order to account for dynamic features relevant for integrating active power control capabilities in WTs. A summary and concluding remarks are then presented in the final section.

Need for generic standard wind turbine models Generic models are parameterized simplified models that include the most relevant characteristics for this study. The advantages of generic models lie in easier validation, easier parameter tuning, faster error detection and easier portability of models. Furthermore, generic models make it easier to standardize and generalize the models and procedures of modeling and validation for different users irrespective of the functionalities of the models or background of the users. The need for generic models for WTs has become more evident in the last few years because of reasons such as: the increase of wind power penetration into a power system, the increase in the number of manufacturers in different geographic locations, the variety of grid code requirements for different networks and the increase in research and education activities regarding WTs. Generally, dynamic models for wind turbine generators (WTGs) are developed by manufacturers, consultants and academia mostly as proprietary user-defined models. There have been different types of wind turbine models dedicated to different types of studies. Akhmatov (2003) shows the dynamic behavior of electric power systems with high penetration of wind power. Miller and Sanchez-Gasca (2003) and Miller et al. (2003) discuss the dynamic modeling of specific GE 1.5 MW and 3.6 MW WT for stability studies. Jauch et al. (2004) presents a simulation model for an active stall fixed-speed WT. Muljadi and Ellis (2010b) discuss WECC generic WT models. Fortmann et al. (2010) provides a Matlab/Simulink model for a doubly-fed induction generator. Asmine et al. (2011) and Sørensen et al. (2012) describe validation methods for WT models. Advanced simulation tools are used intensively by the wind energy industry at the moment to provide advanced models for wind turbines with detailed insight into the structural or electrical design and

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Figure 2.  Classification of power system stability according to the IEEE/CIGRE Joint Task Force on Stability Terms and Definitions. (©IEEE 2004).

control. For example, HAWC2 is an aeroelastic simulation code, which provides an advanced model for the flexible structure of the wind turbines based on a multibody formulation (Larsen and Hansen, 2007). Matlab SimPowerSystems, DIgSILENT and EuroStag are dedicated electrical power system simulation tools used to model the dynamic behavior of power systems and to analyze the assessment of power quality and the wind turbine grid integration. Hansen et~al. (2004) describe modeling dynamic WT models using DIgSILENT PowerFactory. Discussion of different tools like HAWC, DIgSILENT, Saber, Matlab/Simulink for modeling WTs is performed by Iov et~al. (2005). These models are generally very detailed, complicated and often confidential. The high level of detailing of such models may not always be required, for example in stability studies of large power networks with large integration of WTs. Furthermore, the data exchange for proprietary models between different user communities such as manufacturers, system operators, researchers, developers, consultants, students and academicians is often not possible. This aspect dictates the need for generic, standard, non-proprietary models for WTs and wind power plants (WPPs) for the benefit of experts from a vast range of wind energy science and technology domains. Despite having a large amount of existing and planned WT deployment, a standard generic model for any specific study is not universally accepted. For example, models commonly provided for interconnection studies are not adequate for use in other types of studies such as general transmission planning studies (Ellis et al., 2011b) or stability studies (Altin et al., 2014; Keung et al., 2009; Margaris et al., 2012; Miller et al., 2003) where public, non-proprietary, documented and validated models are needed. Presently two different groups are developing generic models for dynamic studies of wind power generation. As indicated in the literature (Behnke et al., 2007; Ellis et al., 2011a; Muljadi and Ellis, 2008, 2010a; WECC Renewable Energy Modeling Task Force, 2010), a wind generation modeling group of the Western Electricity Coordinating Council (WECC) and the IEEE Working Group on Dynamic Performance of Wind Power Generation have developed generic WTG models for the purpose of dynamic power system studies. The International Electrotechnical Commission (IEC) has published a standardization work, IEC 61400-27 (IEC, 2015), to define standard, public dynamic simulation models for WTs and WPPs. The standard for WT models is composed of both modeling and validation subgroups. IEC 61400-27 models are mainly suitable for fundamental frequency positive sequence response simulations during short term events in the power system, such as short circuits (low voltage ride through), loss of generation/loads and typical switching events (Akhmatov, 2003; Michalke and Hansen, 2010; Sørensen et al., 2011). These events can influence the power system stability, which typically can be categorized into rotor angle stability, frequency stability or voltage stability based on the effects of these events on generator rotor angles, system frequency or system voltages respectively. The power system stability can be altered very fast, in the time-scale of seconds to minutes (referred to as short-term stability) or hours (long-term stability) (Kundur et al., 2004). The generic WT models of IEC standard are mainly focused on short-term stability studies as shown by the shaded regions in power system stability classifications in Figure 2. The potential users of IEC standard are expected to be as follows.

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1. Transmission system operators (TSOs) and distribution system operators (DSOs), who are end users of the models performing power system stability studies as part of the planning as well as the operation of the power systems. 2. WT manufacturers, who are typically required to provide WT models to the owner. 3. WPP owners, who are typically responsible for providing WPP models to TSO and/or DSO prior to commissioning. 4. Developers of power system simulation software, who can implement standard WT models in software libraries. 5. Education and research communities. The development of generic models addresses the particular characteristics and performances of the most utilized WT concepts on the market today. Both the WECC and IEC generic models are intended for short-term power system stability studies, assuming that wind speeds are constant during the simulations. These models are therefore not intended for studies with wind speed variability, or for studies where information on the available power is primordial in reflecting the WTs’ limits in providing grid support (like inertial response, temporary active power overproduction and primary response from WTs). Additional adjustments and extensions of the Type 4 IEC generic model are for example described in Hansen et~al. (2014) in order to reflect the dynamic features of wind turbines relevant for active power and grid frequency control capability studies.

Types of wind turbines Four WT configurations (Types 1–4) are the most commonly applied WT concepts in the industry (Hansen, 2012). They correspond to a specific combination of speed control and power control capability of wind turbines. Type 1. This configuration denotes the fixed speed wind turbine (FSWT) with an asynchronous induction generator (typically squirrel cage) directly connected to the grid via a transformer as shown in Figure 3. Since the induction generator always draws reactive power from the grid, this configuration uses a capacitor bank for reactive power compensation (Hansen, 2012).

Figure 3.  Type 1 wind turbine (Hansen, 2012).

Type 2. This configuration corresponds to a limited variable speed wind turbine (speed range typically 0–10% above synchronous speed) with variable generator rotor resistance (OptiSlip) (Hansen, 2012), as shown in Figure 4.

Figure 4.  Type 2 wind turbine (Hansen, 2012).

Type 3. This configuration uses a doubly fed induction generator, where the stator is directly connected to the grid and the rotor is connected through a back-to-back power converter as shown in Figure 5 (Hansen, 2012). Typically, the speed range comprises synchronous speed –40% to +30%.

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Figure 5.  Type 3 wind turbine (Hansen, 2012).

Type 4. This configuration corresponds to the full scale variable speed wind turbine, with the generator connected to the grid through a full-scale frequency converter as shown in Figure 6. The frequency converter performs the reactive power compensation and a smoother grid connection for the entire speed range (Hansen, 2012). Type 4 WTs use either synchronous generators or asynchronous generators. Some Type 4 WTs use direct drive synchronous generators, and therefore have no gearbox. Two generic WT models are proposed in the IEC 61400-27 standard for the Type 4 WT configuration, i.e. Types 4A and 4B. The main difference between these two types is the 2-mass mechanical model included in Type 4B WTs to reflect that the drive train oscillations are excited during grid faults.

Figure 6.  Type 4 wind turbine (Hansen, 2012).

Generic modeling aspects and results The generic modular structure of WT models, shown in Figure 7, depicts a modular structure for the Types 1 and 4B WT models. The signals are listed and described in Table 1. The main difference between the Types 1 and 4B WT models is that Type 1 WT has an aerodynamic block while Type 4B has the control block. Therefore, the mechanical block for the Type 1 WT model has an input of paero from the aerodynamic block while the Type 4B WT model gets paero from the control block. Since the Type 1 WT is a fixed-speed WT, it does not involve a control block. 1. Electrical equipment block: consists of a shunt capacitor for Type 1 WTs and circuit breaker and transformer model for Type 4B WTs. •• Shunt capacitor: standard fundamental frequency capacitor model in the simulation tool is used. •• Circuit breaker: standard circuit breaker model in the simulation tool is used. The circuit breaker model must open the circuit breaker when it receives the FOCB flag. •• Transformer: standard transformer model in the simulation tool is used. Grid protection block: includes protection against over and under voltage, and against over and under frequency. 2. Aerodynamic block: modeled through a constant aerodynamic torque model assuming a short time period of events under study, e.g. voltage dips. 3. Mechanical block: modeled as a two mass model. The aerodynamic power input and air gap power input to this block are obtained from the control block and the generator block, respectively. Air gap power is the power transferred electromagnetically across the air gap between the stator and rotor of the generator. 4. Generator block: for Type 1 WTs, a standard asynchronous generator model in the simulation tool is used. For Type 4B WTs, the generator system is modeled via a static generator component, including a current limiter, which has as inputs the current command signals generated by the control block. 5. Control block: consists of several sub-blocks like current limiter, P control, and Q control:

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Figure 7.  Generic modular structure of wind turbine models (IEC, 2015).

Table 1.  Signals in the generic modular structure of WT models. Signal

Description

uWT i WT f sys FOCB paero pWT q WT ugen i gen pag ωWTR ωgen pWTref x WTref   ipcmd iqcmd ip max iqmax iqmin

Wind turbine terminal (WTT) voltage WTT current phasor Power system grid frequency Open-circuit-breaker flag Aerodynamic power WTT active power WTT reactive power Generator voltage phasor Generator current phasor Generator (air gap) power WT rotor rotational speed Generator rotational speed WTT active power reference WTT reactive power reference or delta voltage Reference, depending on WT control mode Active current command to generator system Reactive current command to generator system Maximum active current Maximum reactive current Minimum reactive current

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Figure 8.  Voltage control mode (Q control model of Type4B (IEC, 2015).

•• Current limiter: based on current command signals, voltage and generator speed, maximum and minimum limiting current signals are generated which are further input to active power P control and reactive power Q control blocks. •• P control block: generates current command signal for active power based on the active power reference signal from the WPP controller and generator speed. It also generates the aerodynamic power signal. •• Q control block: generates current command signal for reactive power. There are 4 different Q control modes (IEC, 2015): voltage control, reactive power control, open loop reactive power control and power factor control. To illustrate the control capabilities of the turbine, the voltage control mode of the Q control model as exemplified in Figure 8 is described in more detail. The parameters of this model are defined in Table 2. Figure 8 shows that during normal operation, the reactive component of current command signal to generator, iqcmd is equal to iqbase (base value of iqcmd). iqbase is generated when there is change in voltage and is controlled using the voltage PI controller. The voltage droop block calculates the voltage at a point located at the serial impedance distance (rdroop + jxdroop ) from the wind turbine terminal (WTT). If voltage at the WTT is to be controlled then rdroop and xdroop are set to zero. During fault conditions, a signal is generated (denoted as FLVRT ) to represent that voltage is low (lower than uqdip) i.e. the WT should operate in low voltage ride through (LVRT) mode. The value of the FLVRT signal and corresponding current command signals for different operation conditions are given in Table 3. A simple test power system (as shown in Figure 9) is modeled in Matlab SimPowerSystems to simulate the dynamic response of WTs during a short circuit. Table 4 gives the values of the parameters for different equipment in the network. Figure 9 depicts the modular structure of the considered network where WT is connected. Notice that the network consists of an external grid which is represented by a Thevenin equivalent, two step-up transformers, the collection cable, circuit breaker, capacitor (only for Type 1) and WTG. Modeling a power system in a tool like SimPowerSystems provides the flexibility for learners to conduct various studies, and this increases their understanding of the learning process.

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Das et al. Table 2.  Parameters for Q control (IEC, 2015). Symbol

Description

rdroop x droop umax umin uref0 Tufilt KPu KIu udb1 udb2 Kqv uqdip Tpost iqmax iqmin iqh1 iqpost

Resistive component of voltage drop impedance Inductive component of voltage drop impedance Maximum voltage in voltage PI controller integral term Minimum voltage in voltage PI controller integral term Bias in voltage reference uWTref = uref0 + ∆uWTref Voltage measurement filter time constant Voltage PI controller proportional gain Voltage PI controller integration gain Voltage dead band lower limit Voltage dead band upper limit Voltage scaling factor for LVRT current Voltage threshold for LVRT detection in q control Time period, post fault reactive power is injected Maximum reactive current injection Minimum reactive current injection Maximum reactive current injection during voltage dip Post fault reactive current injection

Table 3.  Current command signal and FLVRT values for different operating conditions (IEC, 2015). Operating condition

FLVRT

iqcmd

Normal operation Voltage less than uqdip Voltage rises higher than uqdip again

0 1 2 for Tpost time period

iqbase iqbase + iqv iqbase + iqpost  

Figure 9.  Modular structure of implemented network.

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Table 4.  Network parameter values. Equipment

Parameter

Value

Unit

50 kV grid equivalent     Transformer1                 Transformer1                 Feeder             Capacitor bank  

Nominal voltage Source resistance Source Inductance Capacity Primary voltage Secondary voltage Primary resistance Primary inductance Secondary resistance Secondary inductance Magnetization resistance Magnetization inductance Capacity Primary voltage Secondary voltage Primary resistance Primary inductance Secondary resistance Secondary inductance Magnetization resistance Magnetization inductance Positive sequence resistance Zero sequence resistance Positive sequence inductance Zero sequence inductance Positive sequence capacitance Zero sequence capacitance Length Nominal voltage Capacitive reactive power

50 2.516 0.0264 16 50 10.5 0.4052 0.02436 0.017869 0.0010745 2824.1 62.165 2 10.5 960 0.2756 0.00526 0.0023038 4.40E-05 996 21.932 0.7568 0.413 1.42E-03 3.32E-03 1.58E-06 5.01E-09 1 960 0.85

kV Ω H MVA kV kV Ω H Ω H Ω H MVA kV V Ω H Ω H Ω H Ω/km Ω/km Ω/km Ω/km Ω/km Ω/km km V Mvar

Type 1: Wind turbine model The Type 1 WT model includes an aerodynamic and a mechanical model. The aerodynamic torque model assumes that the aerodynamic torque is constant. The mechanical model is modeled as a two mass model as shown in Figure 10. The two masses of the mechanical model represent the inertia constants of generator H WTR and rotor H gen , respectively. The model also includes the shaft stiffness kdrt and the drive train damping cdrt . The parameters of the model are generally in per unit (per unit value of any quantity is defined as the ratio of the quantity to its base value) unless otherwise specified. The mechanical model depicted in Figure 10 should be modified in case the inertia of the generator is embedded in the generator model provided by the simulation tool (as is the case when the SimPowerSystems Asynchronous Machine model is used). Figure 11 shows the modified mechanical model without considering the inertia constant of the generator, H gen. The modified mechanical model of Figure 11 is therefore used for the Type 1 WT model. Note that the output of the modified mechanical model is the mechanical torque, Tm, instead of the generator speed, ωgen , because of the input requirement of the asynchronous generator model in the SimPowerSystems tool. The mechanical torque reflects the difference in WT rotor speed and generator speed experienced by the shaft. Notice that Pag is not input in the model shown in Figure 11, since the dynamics of the generator are already included in the asynchronous generator model. In order to analyze the dynamic characteristics of the WT, a three phase to ground temporary short circuit fault is simulated for 0.1 s at the grid end of the 10.5 kV feeder in the network shown in Figure 9. The voltage magnitude at the WTT, mechanical rotor speed, active and the reactive power outputs of the WT are shown in Figure 12. In order to simulate the resolution of the measurement devices, the signals are passed through a low pass filter of time constant 0.01 s. It can be observed from Figure 12 that the voltage goes down and stays low for 0.1 s until the fault is cleared. Notice that even during the transient period the change in rotor speed is small. This is expected since the Type 1 model is basically a

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Figure 10.  Two mass model (IEC, 2015).

Figure 11.  Modified mechanical model.

FSWT. The active power output of the WT is reduced during the fault and increases as soon as the fault is cleared. During the fault, the impedance seen by the generator is very low. As a result, the current output is very high. Since the voltage is almost zero, the power output during the fault is also very low. As soon as the fault is cleared the voltage rises back, whereas the current in the generator cannot change very fast due to the inductance of the circuit. Therefore, the active power output increases quite a lot and oscillates back to the steady state. The damping of this oscillation is decided by the resistances in the circuit and damping of the generator. It can be seen that since the WT consists of an induction generator, it consumes reactive power during the fault. The reactive power consumption increases after the fault is cleared in order to produce increased active power.

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Figure 12.  Filtered voltage, rotor speed, active power output and reactive power output of a Type 1 wind turbine.

The torsional oscillation in the rotor speed can be observed due to the use of a two mass mechanical model (not a single mass model). During a grid fault, there is an unbalance between the electrical and aerodynamic torque. As a result, the drive train of the WT acts like a torsion spring that gets untwisted. As long as the fault persists, the generator rotor keeps accelerating. After the fault is cleared, the rotor tries to return back to its initial value. Due to the torsion spring characteristic of the turbine drive train, this results in the oscillation of the rotor. The observed torsional frequency from the rotor speed in Figure 12 is around 1 Hz. Generally the torsional oscillation is typically between 0.2 to 4 Hz. The frequency of the drive train oscillation can be calculated from equation (1) (Hansen and Michalke, 2007)

f osc =

1 2π

kdrt (1) J eq

where J eq is the equivalent moment of inertia given by



J eq =

J WTR η 2 J gen J WTR + η 2 J gen

(2)

kdrt = shaft stiffness constant J WTR = moment of inertia of rotor in kgm2 J gen = moment of inertia of generator in kgm2 1: η = ideal gear ratio The general expression between the inertia constant, H, and moment of inertia, J, is



1 Jω 2 2 (3) H= MVA

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Das et al. Table 5.  Parameter values for Type 1 wind turbine. Parameter

Value

Unit

Generator type Capacity Nominal voltage Frequency Stator resistance Stator inductance Rotor resistance Rotor inductance Mutual inductance Generator inertia constant, Hgen Pole pairs Initial generated power Rotor inertia constant, HWTR Drive train damping, c drt Drive train stiffness, kdrt

Squirrel cage induction generator 2.3 960 50 0.01109 0.13865 0.01 0.12513 4.4368 2 2 1 4.225 0.8 140

  MVA V Hz p.u. p.u. p.u. p.u. p.u. s   MW s p.u. p.u.

p.u. values are computed considering WT nominal values as base values.

ω = rotor speed in rad/s MVA = MVA rating of the machine Replacing the value of J from equation (3) in equations (2) and (1)

f osc =

1 2π

kdrt (4) H eq

where

H eq =

2 H WTR H gen H WTR + H gen

(5)

In the simulation, the WT rotor has an inertia constant of 4.225 s and the inertia constant of the induction generator is 2 s. The eigenfrequency calculated based on equation (4) is equal to 1.14 Hz. It can also be observed from Figure 12 that the oscillation in rotor speed is also quite close to this value. The values of the parameters used for the Type 1 WT are shown in Table 5.

Type 4B: Wind turbine model The generator system in the Type 4 WT is modeled via a static generator component, including a current limiter. The static generator is typically used in any kind of static (not rotating) generator modeling. In Type 4 WTs the response, seen from the grid side, is determined by the full converter attached to the generator allowing for the use of the static generator component. A static generator model can typically support both a current source and a voltage source model. In the present implementation, a current source model is used. A two mass mechanical model as shown in Figure 10 is again used for Type 4B with a natural frequency in torsion of 1.7 Hz. As illustrated in Figure 7, the Type 4B WT model includes the mechanical model, generator, control, and electrical equipment block. A 2.3 MW Type 4B WT is simulated in the network shown in Figure 9. The values of the parameters for Type 4B WT used are shown in Table 6. The initial power generation of the WT is 1.47 MW + 0 MVAR. To understand the dynamic characteristics and control of the WT, a three phase to ground temporary short circuit (fault resistance=1Ω) is simulated for 0.1 s at 0.5 s. The network has a resistive load of 1 MW at the WTT node, see Figure 9. The voltage at the WTT node, active power generation and reactive power generation of the WT are shown in Figure 13. Notice that this type of WT is able not only to stay connected during the fault, but also provides increased reactive power

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Table 6.  Parameters for a Type 4B wind turbine. Parameter

Value

Unit

Generator inertia constant, Hgen Initial generated power Rotor inertia constant, HWTR Drive train damping, cdrt Drive train stiffness, kdrt

0.725 1.47 4.2250 0.8 140

s MW s p.u. p.u.

p.u. values are computed considering WT nominal values as base values.

Figure 13.  Filtered voltage, reactive power output, reactive current command signal and FLVRT signal of Type 4B WT.

support. It also provides reactive power support post fault period for Tpost = 1 s. The reactive component of the current command signal, iqcmd , as well as the associated FLVRT signal are shown in Figure 13. It can be observed that FLVRT is 1 as long as the voltage magnitude is less than uqdip (0.8 in this study). FLVRT is equal to 2, for a Tpost period after the voltage is recovered. It is interesting to observe the similarity between voltage magnitude and iqcmd curves, which signifies the strong coupling between the voltage magnitude and reactive power control of the WT.

Issues and extensions for other purposes It is important to note that the IEC WT standard models (IEC, 2015) should be adjusted in order to make them suitable for reflecting wind turbines’ dynamic features, relevant for active power and grid frequency control capability studies. IEC WT generic models are not intended for studies with wind speed variability or for studies where information on the available power is primordial in reflecting the limits of providing ancillary services (e.g. inertial control, primary control) by the WTs in specific situations (Hansen et al., 2014). As illustrated in Figure 14, the adjusted model consists mainly of an aerodynamic model, pitch control, mechanical model, generator system (modeled via a static generator), electrical control system and a protection system. It should be noted that this model is basically an extension of the IEC Type 4B WT. Therefore, the mechanical model and generator

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Figure 14.  Modified model of Type 4B wind turbine to represent the power support capability of modern wind turbines.

Figure 15.  Change in pitch angle, rotor speed and generator speed for a change in active power setpoint for Type 4B WT.

system of this extended model are the same as that of the IEC Type 4B WT discussed in the previous section. Besides the electrical control system, which includes the active and reactive power control loops, there is also a maximum power point tracking (MPPT) control and a frequency control, which generate the power reference signal for the electrical control. This kind of adjustment of standard IEC models is required for different kinds of stability studies such as frequency stability, rotor angle stability, small signal stability etc.

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To promote a deep understanding of the WT active power capability, i.e. to follow an imposed active power desired set point, an adjusted model has been implemented and simulated as shown in Figure 14. The following sequence is followed. For the first 5 s, the power reference is set to rated power (i.e. 1 p.u.). The power reference is then stepped down to 0.875 p.u. and then the 15 s power reference is stepped back up again to 1 p.u. Figure 15 shows the power set point and measured power, together with the pitch angle, shaft torque and generator speed. Notice that in order to decrease power output, pitch angle is increased and vice-versa. Generator speed maintains its steady-state value although there are small transients during changes in power. Notice that the shaft torque oscillations are highly damped, implying that drive train is not mechanically stressed.

Conclusion This article presents the cornerstones of an interactive learning process for engineers on generic WT models for short-term dynamic studies. Through the multimedia environment described in this article, i.e. SimPowerSystems, engineers from varied backgrounds can have a deep understanding of intricate technical details about the modeling of WTs. The article illustrates how one can get a deeper understanding of the generic WT models by easily conducting different study simulations in the SimPowerSystems platform. This article explains the IEC generic models along with the implementation details of these models in Simulink, and looks into the extension of these generic models to make them suitable for different studies. The article shows how the generic models can be emphasized by using an appropriate simulation platform, i.e. SimPowerSystems, to provide engineers with modeling and simulation skills. These are an important part of a deeper understanding of the generic models, which might otherwise be difficult to assimilate. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n0. 283012. This work has been done as a part of Innovative Tools for Electrical System Security within Large Areas (iTesla) project.

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