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Analysis of renewable-energy systems using RPM-SIM simulator


IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 53, NO. 4, AUGUST 2006

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Analysis of Renewable-Energy Systems Using RPM-SIM Simulator
Jan T. Bialasiewicz, Senior Member, IEEE, and Eduard Muljadi, Senior Member, IEEE
Abstract—Renewable-energy systems that are under development need a simulation-based analysis to ensure system stability, power quality, and reliability. Such an analysis may reveal design modi?cations that need to be made before the system is implemented in the ?eld. The modular simulator RPM-SIM, available on the National Renewable Energy Laboratory Web site (http://wind.nrel.gov/designcodes/simulators/rpmsim/), facilitates a low-cost application-speci?c study of the dynamics of the wind–solar–diesel hybrid power systems. This paper discusses the principal modules of the simulator and provides several examples of a simulation-based analysis of the renewable-energy systems. These examples illustrate the importance of a simulation-based study of the grid-connected and stand-alone or autonomous systems. The RPM-SIM is open-ended and can be easily expanded. Index Terms—Modeling and simulation of power systems, peak-power tracker (PPT), renewable-energy systems, stiff grid, water pumping, weak grid, wind-farm aggregation.
Fig. 1. Principal modules of the RPM-SIM included in a single-line diagram of a generic renewable-energy system.

I. I NTRODUCTION N RENEWABLE-ENERGY systems, several energy sources can be combined to supply the main load. On an interconnected grid, the system’s frequency is dictated by the grid’s frequency. The synchronous generator or other reactive-power supports in the power system regulate the voltage. Renewable-energy sources are often used in remote areas where an interconnected grid is not available. In such cases, a diesel generator (DG) or an inverter can be used to regulate the frequency and voltage. In other renewable-energy systems (with or without DGs), an inverter can be used to store and retrieve energy in a battery bank (BB) or a ?ywheel energy-storage system. When an interconnected grid is not available, the master function of the frequency and voltage control can be interchanged between the DG and the inverter. However, a proper control system must be designed for such applications to take full advantage of the wind or solar resource. The system must maintain power quality, as measured by the electrical performance. Each new system should be simulated before it is implemented, to ensure stability, power quality, and reliability. This process can con?rm that a particular control strategy

I

Manuscript received July 7, 2005; revised May 5, 2006. Abstract published on the Internet May 18, 2006. J. T. Bialasiewicz is with the Department of Electrical Engineering, University of Colorado, and Health Sciences Center, Denver, CO 80217-3364 USA (e-mail: jan.bialasiewicz@cudenver.edu). E. Muljadi is with the National Wind Technology Center, National Renewable Energy Laboratory (NREL), Golden, CO 80401 USA (e-mail: eduard_ muljadi@nrel.gov). Digital Object Identi?er 10.1109/TIE.2006.878320

results in the desired system performance and may reveal that design modi?cations are needed. Bialasiewicz et al. [1]–[3] used VisSim1 to develop a modular simulation system called the RPM-SIM, to facilitate a low-cost application-speci?c study of the dynamics of the wind–solar–diesel hybrid power systems. A system con?guration can be set up easily with a library of the power system and available renewable-energy source modules. Some simulation studies require that the modules be modi?ed or that specialized modules be included, but such modi?cations can be made rather quickly. Many researchers have recognized the need for a simulation tool that facilitates the analysis and design of the hybrid power systems. Jeffries et al. [4] are among those who have developed dynamic models of the wind–diesel systems. Of all the simulation tools, the RPM-SIM seems to have the largest selection of modules and control strategies. It is the ?rst dynamic hybridsystem simulator with a symbolic graphical user interface. Fig. 1 is a single-line diagram of a generic renewable-energy system that shows the principal modules of the RPM-SIM. All the elements of the simulated system are connected to one module, which is called the point of common coupling (PCC). The other principal modules are the DG; the alternating-current wind-turbine (ACWT) module, with the induction generator and the wind-speed time series as the input; the rotary converter (RC) with the BB; the inverter with the photovoltaic (PV) array (which may be replaced by the BB, the ?ywheel energystorage system, or a small wind turbine); the village load (VL); and the dump load (DL). R + jX represents the transmission line impedance, and PFC represents the power-factor-correcting capacitors.
1 VisSim

is the trademark of Visual Solutions.

0278-0046/$20.00 ? 2006 IEEE

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 53, NO. 4, AUGUST 2006

The d?q axis convention and synchronous reference frame are used in all electrical simulations. In electric machine and power-system analysis, the transformation from three phase quantities—a, b, and c—into the d?q axis is commonly used. Known as Park’s transformation, this technique was pioneered by Park [5] and Stanley [6]. In 1965, Krause and Thomas [7] generalized the d?q transformation for an arbitrary reference frame. Park’s transformation has the unique property of eliminating all the time-varying inductances from the voltage equations. Ong [8] also uses this transformation to dynamically simulate the electric machinery. The basic modules of the simulator are introduced in the following sections. We then present several simulation case studies that illustrate the performance and usefulness of the simulator and show that certain aspects of the power-system operation can be effectively investigated by simulation. II. RPM-S IM M ODULES DG Module: This includes models of the diesel engine and the synchronous generator, the engine-speed-control block, and the voltage regulator. The engine-speed-control block generates the fuel/air ratio to keep the frequency constant. The voltage regulator determines the ?eld current of the synchronous generator that is needed to keep the voltage constant under varying load conditions. The user can also set the minimum diesel power as a required percentage of the rated value. ACWT Module: This simulates the two-step conversion of wind power to electrical power: 1) wind power is converted to mechanical power and 2) electrical power is obtained from the induction generator that is connected to the line. The wind speed, which constitutes the input signal to the ACWT module, is represented in a time series. A permanent-magnet generator is used for a small wind turbine. A furling mechanism can be added to control the wind turbine in a high-wind-speed region. DL Module: This is composed of parallel resistive loads. The principal purpose of the DL is to keep the diesel-generated power above a user-prescribed fraction of its rated power. It can also (under special circumstances) be used to control the frequency. Either of the control strategy dynamically determines the number of the DL elements to be connected. VL Module: This generates the q and d components of the utility load current. The user declares the rated real-power consumption and the power factor and can choose between the ?xed load and the load pro?le. The load information is placed in a data ?le. RC/BB Assembly: This consists of a BB, a dc machine, and a synchronous machine. The RC/BB assembly can be set to operate in the synchronous-condenser mode, to provide or absorb the reactive power by setting to zero the battery reference power and maintaining a zero shaft torque and zero real-power output. The functionality of the RC is similar to the inverter, except that the response of an RC is much slower. PV Array: This is commercially available in modules. The PV modules are used to build an array, and their current–voltage (I –V ) characteristics are considered as I –V characteristics of the elementary PV-array unit. A single solar cell is introduced as this elementary unit. Consequently, when setting up the sim-

Fig. 2. Power coef?cient and the coef?cient of thrust as functions of the TSR.

ulation with the commercial PV arrays, the user must declare the number of modules in one row or connected in series and the number of module rows connected as a PV unit. Inverter: This can work in either the master or the slave mode. In the master mode, the inverter controls the system’s frequency and voltage when the DG is disconnected. The power exchange is determined by the system’s power balance. In the slave mode, the user speci?es the real and reactive power required to be generated or absorbed. The voltage and the frequency control are handled by the DG or by the grid. The transfer from the slave mode to the master mode is determined based on the control strategy designed by the power-plant designer or operator. III. S IMULATION C ASE S TUDIES A. Performance of Power Systems With Small Wind Turbines An example of a battery-charging wind-turbine system with a permanent-magnet synchronous generator and a three-phase diode recti?er that charges a battery is used to analyze the steady-state performance and dynamics of such system. The wind-turbine aerodynamic power is shown in the following: Paero = 0.5 ρACp (TSR)V 3 where ρ (1)

the air density (in kilogram per cubic meter); A the swept area of the blades; power coef?cient Cp (Fig. 2) a function of the tip-speed ratio (TSR); and V the component of the wind speed (in meter per second) normal to the rotor plane. Fig. 2 also shows the coef?cient of thrust Ct used in the model to represent the force that is perpendicular to the plane of rotation. The tip-speed ratio is TSR = ωR V (2)

where ω the rotor angular velocity (in radian per second); R the blade rotor radius (in meter).

BIALASIEWICZ AND MULJADI: ANALYSIS OF RENEWABLE-ENERGY SYSTEMS USING RPM-SIM SIMULATOR

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Fig. 3.

Transients of the system variables illustrate the operation of a battery-charging system with the PPT.

Thus, the power delivered by the wind turbine depends on the wind speed and the rotor angular velocity or its equivalent in revolution per minute. This angular velocity is directly related (through the system’s equations) to the revolution per minute of the generator. Substituting V from (2) into (1) gives Paero = 0.5 ρACp (TSR)(R/TSR)3 ω 3 . (3)

This equation shows that, when the TSR is constant, the wind power generated by the wind-turbine rotor is proportional to the angular velocity or to the equivalent revolution per minute cubed. The per-phase ac voltage Vph at the terminal output of the generator is calculated from the following: πVB Vph = √ 3 6 (4)

block as a simple variable gain Kdc . It uses the current value of Pmax (which corresponds to the current value of revolution per minute) as a reference and compares this value to the actual ac power provided by the wind-turbine generator. The power error is driven to zero by a performance indicator controller, which at its output generates the required gain Kdc of the voltage recti?er. Because the dc bus voltage VB is constant, the gain Kdc controls the phase voltage of the synchronous generator Vph . This relationship is shown in the following: πVB Vph = √ Kdc . 3 6 (6)

To turn the PPT off, we set Kdc = 1. Fig. 3 illustrates the response of the battery-charging system with the PPT to a ramp wind speed. B. Wind Electric Water-Pumping Systems The system that is considered and analyzed with the RPMSIM consists of a wind turbine, a permanent-magnet synchronous generator, an induction motor, and a water pump. The wind turbine is directly coupled to the generator; the generator is electrically coupled to the induction motor; and the induction motor is mechanically coupled to the water pump.

in which VB is the battery voltage. In the normal-wind-speed region, the wind turbine is controlled to produce the maximum power Pmax , which is achieved for Cp max at TSRmax . Then, from (3), one obtains Pmax = 0.5 ρACp max (R/TSRmax )3 ω 3 . (5)

The peak-power tracker (PPT) is a dc–dc converter that controls the output power of the wind turbine. We simplify this

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Fig. 4. Torque versus rotor speed of the induction motor: stiff grid and weak grid comparison.

The energy captured by the wind turbine is converted into electrical energy by a synchronous generator that operates at a variable frequency. The output frequency of the generator is directly proportional to the rotor speed of the wind turbine. The generator transmits electrical energy directly to the induction motor via a three-phase wire without a power converter. In the centrifugal water pump, the output torque varies as the square of its rotor speed. The rotor speed of the induction motor depends on the output frequency of the synchronous generator and the slip of the induction generator. The rotor speed of the synchronous generator determines the electrical output frequency. The rotor speed of the induction motor is slightly less than its synchronous speed. The speed difference between the synchronous speed and the rotor speed of the induction motor determines its slip. The torque–speed characteristic of the induction motor is affected by the parameter Ls , which represents the inductance of the synchronous generator and of the line feeder that connects the wind-turbine generator to the water pump. For the same frequency, the torque–speed characteristic is represented by two curves. Larger Ls corresponds to a smaller slip at a maximum torque but a lower peak torque. Fig. 4 illustrates the effect of Ls on the torque–speed characteristics of the system, where the label stiff indicates a smaller Ls and the label weak indicates a larger Ls . In Fig. 4, the water-pump torque and the induction-motor torque are represented on the same graph, which shows that, in a stiff grid, the wind-turbine water-pump system is stable, whereas on a weaker system, the wind-turbine water-pump is unstable. Fig. 5 depicts the family of curves of the torque–speed characteristics of an induction motor connected to an in?nite bus of variable frequency, where the voltage is varied at a constant volt per hertz. Intersections of the torque–speed characteristic of the water pump and the induction motor correspond to the operating conditions of the system. In Fig. 5, the variation in the frequency, which is caused by the variation of the generator speed as the wind-speed changes, is shown with the

Fig. 5. Torque–speed characteristics of the induction motor as frequency changes.

corresponding curves of the torque–speed characteristics of the induction motor. When the wind speed is low, the operating frequency is f1 , and the operating torque of the water pump and the induction motor is at the operating point T1 . As the wind speed increases, the torque–speed curve moves to f2 , and f3 and the operating points move from T1 to T2 and then to T3 . As the frequency increases, the operating slip also increases, which increases the operating loss. Eventually, it indicates an upper limit where the motor torque can no longer carry the water-pump torque (curve f4 ) when the torque of the water pump is higher than the peak torque of the induction motor. When this occurs, the operating point moves to T4 , and the operating speed drops from nearly 1800 r/min to about 1200 r/min, which corresponds to a slip of 0.33 at a very low torque. This operation will result in very large currents and high losses, and if no action is taken, the stator winding of the induction motor and the generator may overheat and cause a permanent damage. Muljadi et al. [9] propose to avoid this problem by controlling the rotor speed of the generator before it reaches the frequency f4 . This can be done in the small wind turbines by furling or controlled stall, as analyzed by Muljadi et al. [10]. The wind-turbine water-pumping system was analyzed to investigate the stiff- and weak-grid cases. The wind speed was varied linearly in a ramp fashion to a certain higher limit. In this case, the rotational speed of the generator increases, which increases the frequency of the generator output. Although the entire system is implemented in the simulation, the discussion will be limited to the permanent-magnet-generator–inductionmotor interaction. Fig. 6 shows the simulation results of a stiff system. As the wind speed increases, the generator speed increases, the induction-motor water-pump speed increases to the higher limit of the wind speed, and the system is stable. Fig. 7 shows the simulation results for a weak system (with a larger Ls ). As discussed, the torque capability of the generator

BIALASIEWICZ AND MULJADI: ANALYSIS OF RENEWABLE-ENERGY SYSTEMS USING RPM-SIM SIMULATOR

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induction motor cannot support the water-pump torque. Thus, the induction motor is in a high-slip high-current operation mode (see Fig. 7). This must be terminated by the system protection to avoid damaging the insulation of the generator and the motor windings. C. Aggregation Impact of a Large Wind Farm on a Power Grid In a large wind farm, many wind turbines feed power into the power grid at the PCC. The type of turbine, control algorithm, wind-speed ?uctuation, and tower shadow affect the power ?uctuations at each wind turbine. The power measurement from a single wind turbine usually shows a large ?uctuation of the output power. Because many turbines are connected, the power ?uctuation from one turbine may cancel the power ?uctuation of another, which effectively smooths out the power ?uctuation of the overall wind farm. As the wind-energy technologies progress, the wind turbines become larger, so that fewer turbines are needed to deliver the same power. Manufacturers are currently producing multimegawatt wind turbines. Thus, the power ?uctuation of an individual wind turbine will have a greater impact on the power network, especially on a weak grid. Many researchers have investigated various aspects of the electrical power systems in a wind farm. Smith and Brooks [11] and Thiringer et al. [12] investigated wind farms with variable-speed wind turbines, whereas Muljadi et al. [13] and Hansen et al. [14] considered wind farms with ?xed-speed wind turbines under various conditions. This paper focuses on the aggregation impact on the windfarm output at the PCC. We assume the same wind-turbulence intensity and the same impedance of the transmission line for each wind turbine. The real- and reactive-power ?uctuations and the voltage ?uctuations at the PCC of a wind farm are measured. Ideally, every wind turbine on a wind farm should be modeled. Unfortunately, a large wind farm can have more than 100 wind turbines. All the turbines cannot be represented simultaneously because the computing time will be prohibitive. The following assumptions are made to closely represent a real wind farm without simulating each turbine. 1) A large wind farm (200 turbines) is divided into several groups of wind turbines. 2) The wind speed is uniform for each group of wind turbines. 3) The groups are arranged in sequence. 4) The interest is in long-term simulation, so the start-up of each turbine is not a major concern. 5) All the turbines are exposed to the same time-series wind speed with an average speed of 18.7 m/s and turbulence level of 19.7%. The time-series wind speed shifts by 1 min for each group. 6) The contribution of each group is chosen randomly. For example, a wind farm with three groups of turbines may be proportioned as 35% from the ?rst group, 25% from the second group, and 40% from the third group.

Fig. 6. Generator power and the normalized speed of the generator and induction motor for a stiff grid.

Fig. 7. Generator power and the normalized speed of the generator and induction motor for a weak grid.

induction motor is signi?cantly reduced (see Fig. 4). When the water-pump torque increases above the available peak torque of the induction generator, the water pump loses synchronization with the generator and ?nally settles at a much lower speed (represented as point T4 in Fig. 4). Thus, while the generator speed reaches 1.25% because of speed limit (furling of the wind turbine), the motor speed drops to about 40% because the

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Fig. 8. Three groups of wind turbines feed the same transmission line at the PCC.

Fig. 9. Real-power output of a wind farm in the WF1G and WF16G con?gurations.

7) This concept of grouping is repeated for the various numbers of groups, but the total number of wind turbines in the wind farm is maintained at 200. 8) Eventually, the impact of the wind-turbine distribution will be evaluated by comparing the ?icker and the voltage ?uctuations based on the groupings (one group only and 16 groups). Each wind turbine is represented by an induction machine and a stall-controlled wind turbine with a rated power of 225 kW. The wind turbine is operated at a ?xed frequency, and the tower shadow of the wind turbine is set to reduce the wind speed by 20% every time a blade passes in front of the tower. The duration of time a blade passes the tower is represented by an arc of 10% of the 120? for a three-bladed turbine. The tower shadow is repeated every 120? ; thus, in one rotation, three pulsations, commonly known as the 3P effect, are created by the tower shadow. The electrical output power of the wind turbine is connected to a PCC and is then transmitted to an in?nite bus. The shortcircuit capacity of the wind farm is 212 MV · A. The wind speed used is a time-series wind speed. This means that, for each group, the wind speed applied to one group is time shifted with respect to the other. The time shift can be calculated by dividing the distance between the centers of the two groups by the average wind speed. This simpli?ed assumption is made to simulate the aggregation impact of the wind turbines on a large wind farm, from the power-system perspective. The assumption might not be accurate from an aerodynamic perspective or for a long-term observation; however, for a 10-min simulation, it is close to what one might ?nd in a large wind farm. Fig. 8 shows an example of the wind-farm aggregation. In the example, the wind turbines are divided into three groups. The wind will arrive ?rst in group 1, then in group 2, and last in group 3. In this paper, the wind farm is aggregated into 16 groups of wind turbines (called WF16G), and each group

is fed by a different wind speed time series. As a comparison, the same wind farm can be conservatively aggregated into a single group of turbines that consists of 200 turbines fed by a single wind-speed time series. This type of simulation is a nonaggregated simulated wind farm called WF1G. In the case investigated (WF16G), the time shift of the wind speed between groups is 15 s. The electrical output power of the groups is fed into the same PCC on the power grid. The purpose of this time shift is to simulate the spatial distribution of the turbulence and gust fronts. A real spatial distribution of the turbulence would have much less correlation, while this simpli?cation freezes the turbulence and assumes a perfect correlation, but only introduces the effect of time sifting the frozen wind time series. This is a conservative assumption with respect to the averaging bene?ts of the spatially distributed turbulence. Real turbulence is likely to yield less correlation and, hence, more averaging between the dynamic power output. Fig. 9 compares the time-series output-power variations at the PCC for the WF1G and WF16G. The output of the wind farm WF1G is an ampli?cation of the output of one turbine because, in a WF1G, each wind turbine is synchronized. The amplitude of the ?uctuations is signi?cantly reduced in the trace for the WF16G. The wind-turbine aggregation de?nitely makes the collective power ?uctuations at the PCC smoother because it cancels the effect among the wind turbines. Fig. 10 compares the voltage variations of the WF1G and the WF16G. The range of the voltage ?uctuations apparently corresponds to the range of the real-power ?uctuations. The voltage ?uctuation for the WF1G is much larger than that for the WF16G. IV. C ONCLUSION This paper presents several justi?cations for the simulations in the design process of autonomous and grid-connected

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Fig. 10. Comparison of the voltage variation at the PCC for the WF1G and WF16G con?gurations.

renewable-energy systems. It emphasizes the availability of a simulation tool that can be easily adapted to meet the speci?c needs of a system that is under development. The use of such a tool allows design errors to be corrected before the system is implemented. It is also very useful in developing new design principles, including new control strategies and the choice of the best or acceptable system’s structure to obtain the required power quality. Many of these design aspects are illustrated with the case studies presented in this paper. R EFERENCES
[1] J. T. Bialasiewicz, E. Muljadi, R. G. Nix, and S. Drouilhet, “Renewable energy power system modular simulator RPM-SIM user’s guide,” NREL, Golden, CO, Tech. Rep. NREL/TP-500-29721, Mar. 2001. [2] J. T. Bialasiewicz, E. Muljadi, S. Drouilhet, and R. G. Nix, “Hybrid power system with diesel and wind turbine generation,” in Proc. Amer. Control Conf., Philadelphia, PA, 1998, vol. 3, pp. 1705–1709. [3] J. T. Bialasiewicz, E. Muljadi, and R. G. Nix, “Simulation-based analysis of dynamics and control of autonomous wind-diesel hybrid power systems,” Int. J. Power Energy Syst., vol. 22, no. 1, pp. 24–33, 2002. [4] W. Q. Jeffries, J. G. McGowan, and J. F. Manwell, “Development of a dynamic model for no storage wind/diesel systems,” Wind Eng., vol. 20, no. 1, pp. 27–38, 1996. [5] R. H. Park, “Two-reaction theory of synchronous machines-generalized method of analysis—Part I,” AIEE Trans., vol. 48, no. 2, pp. 716–727, Jul. 1929. [6] H. C. Stanley, “An analysis of induction motor,” AIEE Trans. (Suppl.), vol. 57, pp. 751–755, 1938. [7] P. C. Krause and C. H. Thomas, “Simulation of symmetrical induction machinery,” IEEE Trans. Power App. Syst, vol. PAS-84, no. 11, pp. 1038–1053, Nov. 1965. [8] C.-M. Ong, Dynamic Simulation of Electric Machinery Using Matlab/ Simulink. Englewood Cliffs, NJ: Prentice-Hall, 1998. [9] E. Muljadi, L. Flowers, J. Green, and M. Bergey, “Electrical design of wind-electric water pumping,” Trans. ASME, J. Sol. Energy Eng., vol. 118, no. 4, pp. 246–252, Nov. 1996. [10] E. Muljadi, T. Forsyth, and C. P. Butter?eld, “Controlled stall versus furling control for small wind turbine power regulation,” in Proc. Windpower Conf., Bakers?eld, CA, Apr. 27–30, 1998, pp. 5–14. [11] J. W. Smith and D. L. Brooks, “Voltage impacts of distributed wind generation on rural distribution feeders,” in Proc. IEEE/PES Transmiss. Distrib. Conf. Expo., Atlanta, GA, Oct. 28–Nov. 2, 2001, vol. 1, pp. 492–497. [12] T. Thiringer, T. Petru, and C. Liljegren, “Power quality impact of a sea located hybrid wind park,” IEEE Trans. Energy Convers., vol. 16, no. 2, pp. 123–127, Jun. 2001. [13] E. Muljadi, Y. Wan, C. Butter?eld, and B. Parsons, “A study of a wind farm power system,” in Proc. 39th AIAA Aerosp. Sci. Meet. Exhibit, Reno, NV, Jan. 14–17, 2002, pp. 361–370. [14] A. D. Hansen, P. Sorensen, L. Janosi, and J. Bech, “Wind farm modeling for power quality,” in Proc. IEEE IECON, Denver, CO, Nov. 29–Dec. 2, 2001, pp. 1959–1964.

Jan T. Bialasiewicz (M’86–SM’87) received the M.S. degree from Warsaw University of Technology, Warsaw, Poland, and the Ph.D. and D.Sc. degrees from Silesian University of Technology, Gliwice, Poland, all in electrical engineering. Since 1985, he has been with the Electrical Engineering Department, University of Colorado, and Health Sciences Center, Denver. He is also a Professor with the Polish–Japanese Institute of Information Technology, Warsaw. In 1997, he was a Visiting Professor with the Faculty of Electronics, Warsaw University of Technology. In 2005, he was a Visiting Professor with the Catalonia University of Technology, Barcelona, Spain, and a Visiting Professor with the Queensland University of Technology, Brisbane, Australia. For over ten years, he has been cooperating with the researchers of the National Renewable Energy Laboratory’s National Wind Technology Center, Golden, CO. His research interests include control theory, modeling and identi?cation of dynamic systems, renewable-energy systems, and theory and applications of wavelets. He is the author of two books and numerous research publications. Dr. Bialasiewicz is an Associate Editor of the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS and a Registered Professional Engineer in the State of Colorado.

Eduard Muljadi (S’83–M’84–SM’94) received the Ph.D. degree in electrical engineering from the University of Wisconsin, Madison. From 1988 to 1992, he taught at California State University, Fresno. In June 1992, he joined the National Renewable Energy Laboratory, Golden, CO. His current research interests are in the ?elds of electric machines, power electronics, and power systems, in general, with emphasis on renewable-energy applications. He holds two patents in power conversion for renewable energy. Dr. Muljadi is member of Eta Kappa Nu and Sigma Xi. He is involved with the activities of the IEEE Industry Applications Society (IAS) and IEEE Power Engineering Society (PES). He is currently a member of the Industrial Drives Committee, Electric Machines Committee, and Industrial Power Converter Committee of the IAS and a member of the Working Group on Renewable Technologies and the Dynamic Performance of Wind Generation Task Force of the PES.


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RPM Sensor Using Accelerometers A Statistical ...The conclusion summarizes the modeling analysis ...systems for rotary machinery, revolutions per ...
...of Power Converter Applications in Renewable Energy Systems.unkown
November 2001 NREL/CP-500-30563 RPM-SIMBased Analysis of Power Converter Applications in Renewable Energy Systems Preprint Jan T. Bialasiewicz ...
...of Power Converter Applications in Renewable Energy Systems.unkown
November 2001 NREL/CP-500-30563 RPM-SIMBased Analysis of Power Converter Applications in Renewable Energy Systems Preprint Jan T. Bialasiewicz ...
...of power converter applications in renewable energy systems.unkown
//www.researchgate.net/publication/3931476 RPM-SIM-based analysis of power converter applications in renewable energy systems CONFERENCE PAPER FEBRUARY 2001...
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