The Development of Real-Time Wind Turbine Emulation for Microgrid Research

Richard Davies1, Mark Sumner2, Edward Christopher3

1University of Nottingham, England, eexrrd@nottingham.ac.uk 2University of Nottingham, England, mark.sumner@nottingham.ac.uk

3University of Nottingham, England, Edward.christopher@nottingham.ac.uk

Abstract – A bespoke 9kW wind turbine has been designed and installed. An important aspect of the wind turbine is that operational data can be remotely accessed and therefore applied to simulation and emulation work. The data can be used to develop accurate wind turbine models, improve power flow analysis and investigate system interactions based on real data. To investigate microgrid dynamics and power interactions a microgrid test bed is being constructed which will incorporate emulation systems and in particular a wind turbine emulator based on the operational data from the bespoke turbine. The wind turbine emulator will not use the conventional structure of rotational machines but will employ power converters to replicate the power generated. Use of the system will allow an accurate representation of an actual generation system but within a laboratory test bed environment where it would not normally be possible. Keywords – wind, emulator, microgrid.

I. INTRODUCTION Renewable energy has seen growth for decades

although more significantly in recent years [1, 2, 3]. Possible reasons could be attributed to increased public knowledge of the environmental issues associated with fossil fuel use, government incentives and pressure and that the technology is more widely available and cost efficient [1, 2, 4].

As smaller scale generation in the form of renewables becomes more economically viable residential and commercial installations will continue to grow. This however leads to a change in the understood structure of the electrical utility grid as generation on a smaller scale can be local to the loads. The evolution of this localized structure has led to the concept of the microgrid [5].

Microgrids have a certain synergy with renewable energy systems, due to the flexible installation options and sizes of systems such as wind, solar, geothermal or biomass. A microgrid may offer benefits of a more secure supply or improved protection as well as less dependence on centrally generated energy which is often from fossil fuels [6, 7]. For generation and transmission networks the requirements for system-wide upscaling can be reduced as energy is managed and balanced more economically at local level. The widespread adoption of microgrids does however mean that these microgrids must offer stable and flexible connection options for the end user, and this will become challenging as there is also a requirement for increased adoption of low scale renewable with “Plug and Play” connectivity. Microgrid

research continues globally at various scales and with differing focus and arrangement [6, 8, 9].

A 500kW microgrid test facility is being developed at the University of Nottingham to research and develop management and control strategies for microgrids, as well as providing a test facility for equipment developed for such environments A key requirement of the test facility is to emulate a wide range of envisaged microgrid equipment, including renewable energy sources, energy storage devices and loads such as electric vehicles such that a microgrid’s behaviour can truly be created in real time for the testing of proposed management algorithms. This paper describes the creating of a scalable wind turbine emulator which will be used to represent, in real time, the performance of a wind turbine located at a rural site.

II. TEST FACILITY OVERVIEW The microgrid test facility comprises two, three phase

distribution systems as shown in Fig. 1. The first is a 1MVA 50Hz 415V distribution system connected directly to the supply utility, which acts as the energy source and energy dump. The second is a variable frequency variable voltage distribution system (microgrid) which can either be fed from a motor generator system (with the motor driven through a variable speed drive supplied from the supply utility), or can be supplied by emulated renewable energy systems (ERES).

Each ERES is actually a four quadrant power electronic converter connected between the supply utility and the microgrid as shown in Fig. 2. The utility connection is created by an off-the-shelf commercial “active front end”, and requires only startup and shutdown signals. It automatically looks after power flow control based on the power it receives/supplies through its DC link. The microgrid connection on the other hand is a bespoke power converter: it is programmed to act either as a current source or a voltage source, depending on its overall emulation characteristics. Internally it uses a vector control structure whereby currents and voltages are orientated to the supply voltage vector such that the d axis current represents real power flow, and the q axis current represents reactive power flow. Commands for the d and q axis currents are obtained from a higher level controller running the emulation control.

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Fig. 1. Representation of microgrid test facility.

Fig. 2. Representation of emulation system connections.

For some emulated systems (for example a fuel cell) this control will be in the form of a pre-programmed algorithm derived from literature or from laboratory work. For the emulation presented in this paper, the emulation is controlled using real time data derived from a 9kW wind turbine located at a rural site. This wind turbine system is described in the next section.

III. THE WIND TURBINE The wind turbine makes use of a horizontal axis 3

blade design with the rotational energy passed through a gearbox onto a cage induction machine generator as shown in Fig. 3. The drive system, two commercial power converters operating in back-to-back configuration, delivers the power from the variable speed generator to the grid, and incorporates filtering and protection to achieve G83 compliance. These can be seen on the right hand side in Fig. 4. To maximize the energy capture the wind turbine has two degrees of control, both based on measurements from a Windsonic ultrasonic wind speed and wind direction measurement device mounted at the top of the nacelle. An active yaw mechanism at the base of the nacelle can direct the blades into and out of the wind based on wind direction measurements.

Fig. 3: Wind turbine nacelle during installation.

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Fig. 4. Wind Turbine control box (left) and power circuit (right) during installation.

The yaw motor is a 24V DC motor with a high gear

ratio and is supplied via relays (which change direction of rotation according to command signals) from a 24V DC supply. The second form of control involves adjusting the generator speed according to the measured wind speed. A pre-programmed curve is used within the controller derived from the blade design data, such that the correct generator for maximum power operation is achieved for each wind speed. The control system also deals with situations where the wind speed is greater than the blade ratings. In these situations the generator needs to be controlled in stall mode for a short period of time (to ride through short gusts). If the high windspeed is more sustained, the generator speed is set to zero and the nacelle is yawed out of the wind to provide safe shut down. A mechanical brake can be applied to the shaft when it is stationary. The control and data capture is implemented on a National Instruments CompactRIO. This is an expensive and complex processor but provides for flexibility and expansion if required. The CompactRio communicates with the instrumentation (wind speed/direction, nacelle temperature, gearbox vibration), the yaw drive relays (nacelle orientation), and the power

converters (generator speed reference, generator speed measurement, electrical power). An overview of the control system is shown in Fig. 5.

IV. CONTROL SYSTEM DEVELOPMENT Initial Data Assessment The wind speed and wind direction as measured using

the Windsonic transducer mounted on the nacelle were monitored for an extensive period of time to aid with the development of an optimized wind turbine control algorithm using simulation studies. An example of wind speed and direction captured over a 13 day period is shown in Fig 6. The data has been sampled at a frequency of 1 Hz.

The data was used to aid the development of the wind generator control system. For example, looking at the wind and speed data together with the knowledge that the generator speed control has a bandwidth of approximately 2 Hz, it was decide to filter wind speed measurement and initially sample and hold it for 3s. This allows the generator to respond to speed reference changes and hold the set point value for 10 seconds, providing a compromise between reacting to all changes in wind, and not constantly accelerating and decelerating the system.

In a similar way the sampling for the yaw control was determined. As the yaw mechanism moves at a rate of 90o in approximately 60 seconds (i.e. is very slow), it was decided to filter the wind direction and sample and hold it at a much slower rate. The yaw direction was sampled every 60s such that the final drive could react to most influential fluctuations in wind direction.

System Simulation A full mathematical model of the mechanical and

electrical systems was developed in Simulink, together with a simplified yaw control and generator speed control based on the sampling described previously.

Fig. 5. Representation of the wind turbine system

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Fig. 6. Graph of 13 days wind speed and direction data collected from wind turbine sensors and control platform.

Fig. 7. Wind turbine simulation results, showing wind data with predicted rotor speed and power.

The blades were modeled from data contributed from

the design stage (with full evaluation of power coefficient). It was then possible to calculate energy captured by the blades at varying wind speeds and with varying rotational speeds. This negated the need to calculate the power coefficient (Cp), tip speed ratio (TSR) and evaluate their relationship directly.

A simple representation was used for the vector controlled generator (together with its gearbox). This was considered to be a controlled current source connected to the grid, with the current being directly proportional to power. A scaling factor was introduced at this point to account for system losses, including the gearbox, power converter and generator itself. These combined were assumed to give 75% efficiency.

The reference speed for the generator was derived from a look up table which contained values of optimum generator speed for specific wind speeds. This look up table was created using the design data for the blades, and the look up table incorporated a first order interpolation algorithm. This simulation was used to evaluate the proposed control scheme before experimental deployment. A typical simulation result is shown in Fig 7, which shows the generator speed and generator power created by the simulated control scheme in response to the actual data captured and shown in Fig. 6. The control scheme was then implemented on the experimental wind turbine. Fig. 8 shows the measured wind speed and generator output power from the experimental system for a period of nearly 3 hours.

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Figure 8. Experimental Operation of Wind Turbine.

Fig 8 shows a good representation of experimental data

and displays instantaneous wind speed and output power sampled at 1 Hz. Due to delays in the yaw mechanism operation a difference can sometimes be seen in the captured power for similar wind speeds. At moments of low wind speed (system idle) or acceleration due to a speed reference change the system can be seen to import power from the grid (negative). The general relationship though is one of exporting power to the grid, shown as positive on the graph.

V. WIND TURBINE EMULATION

The wind turbine will be emulated by the power converter shown in Fig. 2 based in the microgrid test facility. The emulator comprises a commercial active front end (AFE), together with a commercial inverter, which are connected together at the DC Link. The AFE controls the DC link voltage and this automatically ensures that power is delivered or taken from the inverter on demand. The inverter is controlled using a similar control approach to the AFE i.e. it employs current controllers in a dq coordinate reference frame, with the d axis aligned to the measured microgrid voltage vector. The q axis current represents reactive power flow and is set to zero at present. The d axis current controls power into and out of the microgrid, and this is set by the higher level emulator control. Data can be captured and stored within the real wind turbine system and used to control the references to the wind emulator in one of two ways.

The first way simply uses the captured power measurements as a direct reference for the emulator such that the emulator is directly duplicating the behaviour of the installed wind system.

The second approach uses the stored wind speed and wind direction data and feeds this to a model of the wind turbine similar to the model developed in Simulink running on the higher level emulator controller. This approach allows the emulation power size to be scaled in a slightly more sophisticated way than simply scaling the power data, such that a more accurate representation of the scaling of the blade size and stalling effects can be accounted for.

The ultimate aim is to continually telemeter measurements from the wind generator site to the microgrid test facility (using web based or GPRS communications) such that the wind emulator can be controlled continually in real time according to contemporaneous data from the real installation. This will be extremely useful for researching microgrid control algorithms that use, for example real-time web based weather forecasting or other available real time data as part of their management strategy.

VI. MICROGRID TEST SCENARIO The development of the 9kW wind turbine is the first

stage of research investigating improved energy management within a small community. This work will

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be based within a specially constructed microgrid test facility which will use real time emulation of the wind turbine, together with real time emulation of the community. For this work the community is the “Creative Energy Homes” (CEH) [10] a combination of six domestic residences which are being used for research into particular aspects of reducing CO2 emissions from different types of houses. The total electrical power fed to the houses is monitored at the distribution point, and can be communicated via a LAN or GPRS based communication system to the microgrid laboratory.

The proposed test system is illustrated in Fig. 9. It shows the wind turbine communicating to the wind turbine emulator in the microgrid test facility. It also shows the data acquisition system at the Creative Energy Homes communicating with the microgrid test facility. Initial testing will look at how energy storage (for example battery or hydrogen based systems) can be used to optimize community energy cost, community energy consumption or community CO2 emissions.

Figure 9. Experimental Emulation of Energy Community.

VII. CONCLUSION A 9kW wind turbine system has been designed,

constructed and tested. It uses an active yaw mechanism and variable rotor speed control for optimal energy capture across a range of wind speeds. The power converters arranged back-to-back give the wind turbine its grid connection and meet G83 requirements.

A 500kW microgrid test facility is being developed to research management and control strategies, equipment behavior and their interactions. The facility will use real-time emulation systems to represent generation and loads. For example, the CEH projects provide data to emulate loading of a small housing estate while the wind turbine provides data to emulate power generation.

The emulation system will incorporate power

converters that derive power from the main grid and feed it onto the microgrid according to the device being emulated. For example one emulator will use wind data processed using a mathematical model to derive the grid power requirements, or will use the actual power data from the wind turbine as the demand reference. The ultimate aim is to use the power data in the laboratory based microgrid facility sent in real time from the wind turbine located in a rural location. The combination of wind turbine and emulator using the real time data therefore offers the opportunity to investigate interactions with additional equipment and in different scenarios in a controlled laboratory environment while being based on actual operational data.

VII. REFERENCES

[1] European Wind Energy Association, “Wind energy and EU climate policy. Achieving 30% lower emissions by 2020,” October 2011.

[2] RenewableUK, “Small and Medium Wind. UK Market report,” April 2012

[3] A. Gupta, D.K. Jain, S. Dahiya, “Some Investigations on Recent Advances in Wind Energy Conversion Systems,” 2012 IACSIT Coimbatore Conferences, IPCSIT vol. 28 (2012) IACSIT Press, Singapore

[4] Dragomir, D. Golovanov, N. Postolache, P. Toader, C. , "The connection to the grid of wind turbines," PowerTech, 2009 IEEE Bucharest , vol., no., pp.1-8, June 28 2009-July 2 2009

[5] Eto, J. Lasseter, R. Schenkman, B. Stevens, J. Klapp, D. Volkommer, H. Linton, E. Hurtado, H. Roy, J., "Overview of the CERTS Microgrid laboratory Test Bed," Integration of Wide-Scale Renewable Resources Into the Power Delivery System, 2009 CIGRE/IEEE PES Joint Symposium , vol., no., pp.1, 29-31 July 2009

[6] Hatziargyriou, N. Asano, H. Iravani, R. Marnay, C, "Microgrids," Power and Energy Magazine, IEEE , vol.5, no.4, pp.78-94, July-Aug. 2007

[7] N.W.A. Lidula, A.D. Rajapakse, “Microgrids research: A review of experimental microgrids and test systems,” Renewable and Sustainable Energy Reviews, Volume 15, Issue 1, January 2011, Pages 186-202, ISSN 1364-0321, 10.1016/j.rser.2010.09.041.

[8] Huang Jiayi, Jiang Chuanwen, Xu Rong, “A review on distributed energy resources and MicroGrid,” Renewable and Sustainable Energy Reviews, Volume 12, Issue 9, December 2008, Pages 2472-2483, ISSN 1364-0321, 10.1016/j.rser.2007.06.004

[9] .N.W.A. Lidula, A.D. Rajapakse, “Microgrids research: A review of experimental microgrids and test systems,” Renewable and Sustainable Energy Reviews, Volume 15, Issue 1, January 2011, Pages 186-202, ISSN 1364-0321, 10.1016/j.rser.2010.09.041.

[10] University of Nottingham, “Creative Energy Homes,” Internet: http://www.nottingham.ac.uk/creative-energy-homes/creative-energy-homes.aspx [June 2012]

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