Wind Turbine Radar Interference Studies by Polarimetric ... - IEEE Xplore

I. INTRODUCTION

Wind Turbine Radar Interference Studies by Polarimetric Measurements of a Scaled Model F. KONG Y. ZHANG, Member, IEEE R. D. PALMER University of Oklahoma

Wind turbines can cause interference to nearby radars due to strong backscattering. As a recently recognized type of radar interference, wind turbine radar signatures need to be fully studied. The scaled measurement of a wind turbine model has been proposed to characterize wind turbine radar signatures. The radar wind turbine testbed (RWT2 ) has been developed for such a purpose. Polarimetric radar signatures derived from measurements reveal unique features that can be exploited to help identify wind turbines from desired radar targets.

Manuscript received March 22, 2012; revised July 13, 2012; released for publication November 1, 2012. IEEE Log No. T-AES/49/3/944599. Refereeing of this contribution was handled by R. Narayanan. This work was supported by the National Weather Service—Radar Operations Center under NOAA Grant NA17RJ1227. Authors’ addresses: F. Kong and Y. Zhang, School of Electrical and Computer Engineering, University of Oklahoma, 120 David L. Boren Blvd., ARRC, Norman, OK 73072, E-mail: ([email protected]); R. D. Palmer, School of Meteorology, University of Oklahoma, Norman, OK 73072.

c 2013 IEEE 0018-9251/13/$26.00 °

As the wind power industry develops rapidly, an increasing number of wind farms are being built around the world, where many utility-scale wind turbine generators have been installed to efficiently harvest wind energy. These extremely large, manmade structures have raised concerns about their electromagnetic compatibility (EMC) with current radar networks [1—8]. Wind turbines built too close to radars have the potential to jam radar receivers due to the significant amount of backscattered electromagnetic (EM) energy. Even at the ranges from several kilometers to several tens of kilometers, these structures can cause clutter effects. Unlike ground clutter, however, which is essentially stationary from pulse to pulse, wind turbines feature complicated micro-Doppler signatures primarily due to the blade rotation. Thus, conventional ground clutter filtering techniques have failed to mitigate this type of interference. The antenna radiation pattern can also be distorted as a result of partial beam blockage [2]. Therefore, the radar waveform integrity can be compromised in the time, frequency, and spatial domains by wind turbines within the radar vicinity. In the past decade electromagnetic interference (EMI) due to wind turbines on air surveillance radar [3—5], weather radar [6, 7], and military radar [8] has been investigated. It has been proven that the interference does exist and can severely affect radar performance. It is extremely difficult to quantitatively model radar variables of a wind turbine due to its large electric scale, complex shape, and multiple degrees of freedom. In addition various terrain and atmospheric conditions can cause multi-path effects and anomalous propagation, which make it extremely difficult to predict the impact of wind turbine radar interference. The lack of effective means to evaluate wind turbine interference (WTI) on current radar networks and mitigate the clutter effects has postponed installation of many wind farms, stalling the expansion of renewable energy development. Efforts have been made to address the problem using complementary approaches. Wind turbine designers and manufactures have been investigating the stealth turbine technology, which applies radar cross section (RCS) reduction techniques in the designing phase, including tapering the tower and nacelle to avoid specular reflections [9] and partially treating rotor blades with radar absorbing material [10]. These attempts have indeed reduced the backscattered RCS, but the cost is high, and the compromise of the turbine structural integrity needs to be further studied. On the other hand some effort devoted to mitigation of WTI from the radar aspect has also been made. For air surveillance radar, where airplanes usually can be distinguished from wind turbines by

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 49, NO. 3

JULY 2013

1589

altitude, concurrent beam processing and enhanced CFAR (constant false alarm rate) detection algorithms are planned for future system upgrades [11]. The methodology behind this is radar line of sight (LOS) avoidance, which may work for cooperative aircrafts but could lead to loss of track for low-altitude noncooperative targets. The gap-filling concept [12] has been investigated to make up radar coverage loss due to WTI, but the implementation relies on terrain proofing, and it also has significant cost constraints. Unlike air surveillance radar the targets of interest for weather radar are various forms of precipitation, such as rain, snow, hail, etc., which are often spatially inseparable from wind turbines for low-elevation scans. The difficulty in separating wind turbines and weather radar returns in both the spatial and Doppler domains has prompted ideas of combining information from both domains. Range-Doppler processing [13] exploits the difference between wind turbine and weather radar return signals in the range-Doppler domain, alleviates Doppler spectral components identified as WTI, and reconstructs weather signals based on the spatial continuity assumption. The adaptive spectrum processing (ASP) technique [14] makes use of the clustering nature of weather signals and adaptively constructs bandpass filters for mitigation of WTI. All of these mitigation schemes, however, have limitations in practical operations due to high cost, loss of coverage, or complicated distributed targets. The lack of effective measures to evaluate and mitigate WTI is due to insufficient knowledge of wind turbine radar signatures. Although EM simulations [15—17] and actual field measurements [4, 18, 19] have been carried out in efforts to characterize wind turbine radar signatures, there have been challenges in both approaches. The major concern about EM simulations is accuracy. Due to the large electric scale, a large amount of calculations have to be approximated, and many scattering mechanisms are not considered in order to make computation tasks manageable. Reported results [15, 16, 20] are qualitatively comparable with actual measurements, but they are not accurate enough to provide a quantitative match, which is the key to effective mitigations. The wind turbine, as a noncooperative target, rotates and yaws according to wind conditions, whose control system is not easily accessible under normal circumstances. As a result field measurements have difficulties in aligning measured data to instantaneous motions of the wind turbine and, thus, are unable to provide EM simulations with accurate parameters. In addition neither approach can characterize wind turbine radar signature fluctuations in terms of both rotation and yaw due to either computational or measurement difficulties encountered in practice. 1590

Apparently, the complete control of the wind turbine is needed to bridge EM simulations with measurements and also to characterize radar signature fluctuations of the wind turbine with its motions. For such a purpose the scaled measurements are performed in the time domain on a wind turbine model; the results are presented in the following sections. A motor-driven wind turbine model and a laboratory-based dual-polarized pulse-Doppler radar comprise the hardware platform of the radar wind turbine testbed (RWT2 ). The software platform has also been developed to control and synchronize radar operations with wind turbine model motions. Complete measurements in terms of all possible rotation and yaw positions have been made for the first time. Furthermore, the unique polarimetric and micro-Doppler signatures derived from the measurements are discussed. II. SCALED MODEL TIME-DOMAIN MEASUREMENTS A. Motivation As has been discussed in the previous section, the general purpose of performing radar measurements on a scaled model is to circumvent the challenge of gaining control of the full-size wind turbine. The RWT2 has been developed for complete radar measurements as the wind turbine rotates and yaws as in Fig. 1. The model is small enough to fit into the anechoic chamber, where the quiet EM environment ensures that the radar returns are all from the wind turbine model so that EM simulations can focus on modeling the wind turbine structure itself without worrying about the environment. The indoor measurement is also easily repeatable and highly cost-effective compared with field measurements. As the wind turbine model was developed, some initial measurements have been made in the frequency domain using a vector network analyzer (VNA). Previous work experimented with different EM simulation approaches to match network analyzer measurements [16]. However, the single-tone measurement provides no match to actual pulse-Doppler radar systems and is susceptible to reflections from the wall and ceiling of the chamber due to limited space. Stepped frequency measurements have been attempted to overcome this problem. But, the blades have to be stopped at each measurement due to the inherent long cycle time of the VNA, which makes it infeasible to perform the all-rotation all-yaw measurement within a realistic timeframe. And, this drives the development of the laboratory-based radar [21]. This study further explores the polarimetric radar signatures and some unique radar observations of the wind turbine in terms of both rotation and yaw. B. Scaled Wind Turbine Model A typical wind turbine consists of the tower, nacelle, hub, and rotor blades as shown in Fig. 1.


JULY 2013

Fig. 1. GE-1.6M, a typical three-blade wind turbine structure: it has rotor diameter of 82.5 m and hub height of 80 m. Vane on top of hub senses wind direction, based on which, yaw mechanism turns blade into wind for maximum efficiency. Blade position μ and aspect angle ' are defined as follows: μ is angle from yaw axis to reference leading edge, and ' is angle from radar LOS to rotor axis. Blade angle ® is angle formed by two edges of same blade.

The thick edge of the blade is the leading edge; the thin edge is the trailing edge. Since the three-blade structure is the most common design, a scaled three-blade laboratory model was developed as shown in Fig. 2(a). The model has a generic shape and two degrees of freedom, i.e., rotation and yaw. A servomotor was installed inside the nacelle to control blade rotation. With the nacelle cover removed, the motor is shown in Fig. 2(b). The entire model was mounted on a rotary stage, shown in Fig. 2(c), to mimic the yaw motion. Both the motor and the rotary stage can be remotely controlled without interrupting ongoing measurements. Detailed information about the scaled model can be found in [16]. C. Laboratory-Based Pulse-Doppler Radar The laboratory radar has been designed to mimic functionalities of the actual pulse-Doppler radar but for an indoor measurement. The system transmits a narrow pulse at X-band to achieve fine range resolution. It is designed with waveform reconfigurable capability to adjust pulsewidth and pulse repetition frequency (PRF). The range resolution can be as high as 0.9 m, and the range gating has been applied to separate the wind turbine model from the reflection coming from the wall and ceiling of the chamber. General specifications of the system are listed in Table I. Note that the nominal peak power is in simultaneous dual-polarized mode, when horizontally (H) and vertically (V) polarized waves are transmitted within the same pulse. It is half in the alternative dual-polarized mode, when H and V polarized waves interleave from pulse to pulse. It is

Fig. 2. Scaled wind turbine model. (a) Front view of model: hub height is about 1 m, and rotor diameter is 0.7 m. (b) Servomotor within nacelle controls speed, position, and moving directions of blades; power and communication cables go through hollow tower and connect to power supply and motor controller. (c) Rotary stage that yaws entire model. TABLE I System Specifications for the Laboratory Radar System Parameters

Value

Operating frequency Peak power Antenna gain Transmit pulsewidth PRF System bandwidth

10.5 GHz 126 maw 12 dB 6—200 ns 95—24400 Hz 100 MHz

worth noting that, with the current system frequency of 10.5 GHz and wind turbine model rotor diameter of 0.7 m, the scaled model measurement corresponds to an equivalent full-scale frequency at VHF (very high frequency) band, which is well below frequencies where most air surveillance and weather radar operate.

KONG, ET AL.: WIND TURBINE RADAR INTERFERENCE STUDIES BY POLARIMETRIC MEASUREMENTS

1591

Fig. 3. RWT2 system. (a) Scaled model is placed about 3.5 m away from antennas, where bottom of model is covered with EM absorbers to avoid undesirable reflections from rotary stage and base. Graphical user interface shows radar in scanning mode with power and radial velocity displays. (b) Top view of inside of RF enclosure; all components are solid state. Printed circuit board at lower left corner is mixed-signal design to synchronize trigger clocks with sampling clock of data acquisition card. Narrow-pulse modulator is also integrated on the same board.

Therefore, it is not the intention of this study to map scaled model measurements to any specific full-scale wind turbine radar measurements, but rather to characterize radar signatures of the wind turbine with respect to its rotation and yaw through a series of controlled laboratory measurements, which cannot be done in field campaigns [4, 18—20]. Figure 3(a) shows the measurement setup, where a pair of dual-polarized open-ridge horn antennas are mounted on a motor for transmitting and receiving, respectively. The motor itself is mounted on top of a tripod and is controlled by the computer to switch between scanning and spotlight modes. Measurements in the following sections are in spotlight mode since it is the best way to perceive the continuous motions 1592

of the wind turbine using radar. The direct coupling of the transmitted signal into the receiver can be significantly high in radar returns. However, because of range gating capability, the coupling can be easily removed in the time-domain measurement. In the alternative dual-polarized mode, the direct coupling is higher in copolarized mode and can be used to distinguish the copolarized from cross-polarized measurements. Due to the limited chamber size, the wind turbine model can be set no more than 4 m away from radar antennas, which is clearly less than the far field requirement: pL2 (1) Rm = ¸ where L is the largest dimension of the radar target/antenna, ¸ is the radar wavelength, and p determines the largest phase variations of incident/scattered spherical wave over the entire radar target/antenna. p = 2 results in ¼=8 maximum phase variation, which is acceptable for most RCS measurements [22, 23]. However, actual wind turbines affecting radar operations are mostly not found in the radar far field. For example, the Vestas V47 wind turbine in a previous field campaign [18] has the largest dimension of tower and blade combined of 89 m. The far field requirement in this case is greater than 158 Km, which is much further than the radar LOS, but the wind turbines are located 35 to 40 Km away from the radar. Wind turbines can be found from several kilometers to several tens of kilometers within radar vicinity. The range dependency of wind turbine RCS is discussed in [24], which is beyond the scope of this study. The analog transceiver is shown in Fig. 3(b). All parts inside the enclosure except the RF switch are commercial off-the-shelf components; however, because the switch control input is a narrow pulse with a sharp edge, it needs to be connected to the pulse generator by a short transmission line to reduce signal integrity loss. The direct conversion is applied in the receiver design, where the RF signal is down-converted directly to baseband without IF stage. The baseband inphase and quadrature signals of both H and V channels are digitized and stored in real time by the 16-bit digital acquisition system at a sampling rate of 200 MHz. The key feature of the RWT2 is the automation. The motor controllers of the scaled model are synchronized with radar operations by designed software programs, which automatically queue the blade rotation, data acquisition, and yaw so that measured radar data can be aligned with the motor position readings. III. WIND TURBINE RADAR RETURN FLUCTUATIONS AND STATISTICS RCS is the standard measure of how much EM energy is captured by the radar target and scattered


JULY 2013

TABLE II Comparison of Measurement Time

Single Position Single Rotation

All Rotation & all Yaw

Frequency domain

2.24 (s)

56 (min)

14 (days)

Time domain

0.66 (ms)

3 (s)

18 (min)

Note: The time needed to measure single position in the frequency domain includes the VNA cycle time, as well as the time it takes for the blade to increment and establish the next position. For the time domain measurement, it simply is PRT of the radar.

Fig. 4. Comparison of rotation fluctuation measurements from VNA and laboratory radar: wind turbine model is set at same position with same aspect angle. Power level has been properly shifted for comparison.

to the radar receiver, which only depends on target characteristics and radar parameters. However, for the wind turbine—radar interaction, it has been justified that RCS is not the appropriate measure due to the violation of the far field criterion. However, the same form of definition can still be used to obtain the “calibrated” form of radar returns: ¾=

4¼Rr2 Pr 1 ¢ 2 P Gr ¸ t Gt ¢L 4¼Rt2 4¼

(2)

where ¾ is the calibrated radar return power; Pr is the received power, which is the direct radar measurement, Gr and Gt are the gain of the receiving and transmitting antenna, respectively; ¸ is the radar wavelength; Rr and Rt are the range from target to the receiving and transmitting antenna, respectively; Pt is the transmitted power; and L is the system loss. In the approximate mono-static configuration as shown in Fig. 3(a), the transmitting and receiving antennas have the same pattern and are considered colocated since their separation is much smaller than the target range; thus, Gr = Gt = G, and Rr = Rt = R. In this case the radar return power can be formed as ¾ = Pr

(4¼)5 R 4 ¾ = Pr 0 2 2 ¸ G Pt L P0

(3)

where ¾0 and P0 are the RCS and received power of a calibration target. A 12-in metallic sphere has been used for this purpose, whose RCS is ¾0 = ¼r2 (r is sphere radius). It is worth noting that the diameter of the sphere is large with respect to the wavelength but small compared with the measurement range. Obviously, the radar return of a wind turbine fluctuates as it spins and yaws. The fluctuation due to blade rotation is referred to as the rotation fluctuation, and the fluctuation caused by yaw is referred to as the yaw fluctuation. The former is reported in several field campaigns [4, 18—20] and

in our previous work [16]. The rotation fluctuation measured from the same aspect angle, using VNA and laboratory radar respectively, is shown in Fig. 4. The agreement with VNA measurements validates the new time-domain approach. However, it is important to realize the difference between the two: the VNA measures in the frequency domain and then transforms frequency measurements into time domain, while the laboratory radar measures directly in the time domain. The blades stop after each frequency sweep in VNA measurement due to the inherent long cycle time, but they can keep in motion in the time-domain measurement. This significantly reduces measurement time and makes it feasible to perform all-rotation, all-yaw measurement in a much more realistic timeframe. The estimated time consumptions of these two approaches are compared in Table II. Since the yaw motion occurs much more slowly in the real world, the laboratory measurements are taken on a stop-spin-go basis, i.e., measurements are taken when the wind turbine blades spin at discrete yaw positions. The blade position μ (0± · μ < 360± ) and the aspect angle ' (0± · ' < 360± ) have been defined in Fig. 1 to better describe the radar return fluctuations in terms of wind turbine motions. The time-domain scaled model measurements are shown in Fig. 5. Both HH and VV polarization show similar variations, and the most prominent is the periodicity of rotation fluctuation. As the blade revolves for one complete cycle, six significant peaks with quick oscillations in amplitude are observed at most aspect angles except around ' = 0± (360± ) and ' = 180± . Along μ these peaks occur around 0± , 60± , 120± , 180± , 240± , and 300± , i.e., multiple times of 60± , when one of the blade edges is perpendicular to the radar LOS and aligns with the tower (vertical in measurements since the radar elevation angle is 0± ). It is, therefore, believed that this alignment results in constructive in-phase coherent integration of the backscattered field, which results in high return power. The blade rotation quickly breaks the in-phase lineup, and individual scattering centers shifting in ranges comparable with radar wavelength cause significant oscillations due to coherent field integration. Thus, the peak drops rapidly and starts oscillating. It is


1593

Fig. 5. Fluctuation of return power in terms of blade position and aspect angle: ' increments are in 2± , and blades rotate at 20 r/min starting from μ = 0± at each aspect angle. Radar PRF is 1525 Hz, and return power is in decibels.

Fig. 6. Short-time statistics in terms of rotation and yaw. (a) Short-time mean in decibels. (b) Short-time variance in decibels. (c) Decorrelation time in terms of blade rotation (degrees) corresponding to where correlation coefficient drops to e¡1=2 . All statistics are calculated coherently within a sliding window with a width of 5± . Statistics of only HH polarization are shown. Though statistics are still shown in sequence of μ, it can be of any sequence for actual radar operations.

worth noticing that the peaks corresponding to the leading edge in vertical position occur almost exactly at μ being multiple times of 60± , but earlier for peaks corresponding to the trailing edge. This phase advance is found to be similar to the blade angle ® defined in Fig. 1, and it may exist because the trailing edges turn to vertical earlier as a result of the blade shape. Also, attributed to the blade structure, the two-dimensional (2-D) fluctuation is asymmetric about ' = 180± . In fact the same point on the blade that passes through the vertical position above the nacelle at (μ, ') will turn to be vertical below the nacelle at (μ ¡ 60± , 360± ¡ '). As ' approaches 0± (360± ) or 180± , the specular reflection from the front/back of blades starts to dominate, and the two peaks resulting from the two edges of the same blade merge into one as is shown in Fig. 5. The periodicity of the rotation fluctuation can be used to distinguish WTI from desired radar targets. However, the dwell time during which the wind turbine is continuously illuminated needs to be 1594

long enough to capture this periodicity. Though we made the radar in spotlight mode for this study, it is impractical for operational scanning radars. Assuming that the wind turbine is far from the radar and that the beam width of the wind turbine is small compared with the azimuth resolution of the radar antenna, the swept angle by the wind turbine blade within the dwell time is then: ª (4) £=! − where ! is wind turbine rotation rate, Ã is antenna beam width, and − is radar scan rate. For example, if the wind turbine spins at 15 r/min and if the radar scans at 18± =s with 1± beam width, then £ = 5± . With such a short observation window, WTI is found to have diverse statistics from scan to scan as is shown in Fig. 6. The mean varies as the blade rotates, which indicates the nonstationarity of WTI radar variables. The variance reach peaks at the same positions where the return power is highest. This large variance is one of the radar signatures that may help identify


JULY 2013

Fig. 7. Mean and standard deviation of return power in terms of aspect angle: both mean and deviation at each aspect angle are estimated across entire cycle of rotation. Result shown is from measurements of HH polarization.

WTI. With the large variance the decorrelation time at such positions is expected to be lower as is shown in Fig. 6(c). In fact less than 0:5± decorrelation time (angle) is observed at these positions. Many large, modern wind turbines have variable rotation rate, so it is reasonable to assume a uniformly distributed random blade position for every radar visit from scan to scan. The statistics of the rotation fluctuation thus characterize the yaw fluctuation since the latter is the slow variation over a relatively long time. The mean and standard deviation of the rotation fluctuation are shown in Fig. 7. The mean clearly has three major peaks at ' = 90± , 180± , and 270± , which correspond to the three aspect positions shown in Fig. 7. As the wind turbine yaws to 90± or 270± , one side of the nacelle is perpendicular to the radar LOS, which results in specular reflections, which then also causes the mean to rise. The peak at ' = 180± , however, is caused by the reflection from the back of the nacelle. The standard deviation is also highest at ' = 180± , which can be attributed to the reflection from the backside of the blades. Therefore, the dominant scattering mechanism is specular reflection, and proper design of the nacelle shape that reduces normal incidence may significantly reduce the interference to radars. Due to the motions the return power of a wind turbine fluctuates from pulse to pulse. It is therefore necessary to model the probability density function (pdf) of the fluctuations in order to evaluate the impact of WTI on radar performance. Since the aspect angle is determined by wind direction, which normally varies within limited range, it is assumed ' = '0 § ¢', where ¢' describes the variation. The measurement pdf estimates, along with Rayleigh,

Swerling IV, and Gamma fittings, are shown in Fig. 8(a). The mode, corresponding to the peak of the pdf, and the root mean square error (RMSE) are shown in Fig. 8(b) and (c), respectively, for comparison. The RMSE is defined as s Z 1 § e= jpm (¾) ¡ pf (¾)j2 d¾ (5) § 0 where § is the dynamic range of measurements and pm (¾) and pf (¾) are the measurement and fitted distribution, respectively. Both Rayleigh and Swerling IV are single-parameter distributions and are given in (6) and (7), respectively, where ¾¯ is the mean. 2¾ ¡¾2 =¾¯ e ¾¯ 4¾ pR (¾) = 2 e¡2¾=¾¯ ¾¯ ps (¾) =

pG (¾) =

¾k¡1 ¡¾=μ e : ¡ (k)μk

(6) (7) (8)

The Gamma distribution is determined by the shape parameter k and scale parameter μ as in (8), where the former is constrained to k > 1 during the fitting because of non-zero modes in the measurement distribution. Swerling IV is equivalent to Gamma with k = 2. The comparison shows that Gamma fitting has the best overall performance. However, when the actual mode of measurements is close to zero, all models have difficulties approximating the actual distribution. IV. DUAL-POLARIZED RADAR SIGNATURE For polarimetric radars an orthogonal pair of polarized EM waves is transmitted and received,


1595

Fig. 8. The pdf fittings. (a) Comparison of pdf fittings, where probability density is mapped to different colors. Aspect deviation ¢' = 6± , and rotation fluctuation samples are decimated by 10 to decorrelate measurements. Maximum likelihood estimation has been used for all fittings. (b) Comparison of mode, where most frequent value occurs. (c) Comparison of RMSE for different distribution fittings.

which are used to measure the polarized signatures of radar targets. The dual-polarized (HH and VV) radar signatures of the wind turbine have been measured and derived in the laboratory. The return power of different polarization has been shown in Fig. 5, and Stokes parameters have been introduced to describe the polarization state of the wind turbine: 1596

QHV = EH EH¤ ¡ EV EV¤ UHV =

EH EV¤

¡ EV EH¤

VHV = ¡i(EH EV¤ ¡ EV EH¤ )

(9) (10) (11)

where EH and EV are scattered field in horizontal and vertical polarization, respectively; QHV is the degree of polarization; and UHV and VHV are the shape


JULY 2013

Fig. 9. Dual-polarized signatures in terms of rotation and yaw. (a) Degree of polarization QHV . (b) Cross-correlation coefficient ½HV . Both have been averaged on sliding window of 5± .

Fig. 10. Time-variant Doppler spectrum. (a) Spectrogram from scaled model measurements, HH polarization. (b) Actual radar observation of commercial wind turbine, HH polarization. The y-axis is in time, whose scale is proportional to blade rotation. It is not converted to blade position due to unknown motions of wind turbine in actual radar measurements.

parameters of the polarization ellipse, from which the cross-correlation coefficient is derived as q 2 + V2 UHV HV ½HV = : (12) ¤ 2jEH EV j The cross-correlation coefficient ½HV is a normalized measure of the correlation between HH and VV polarization states. QHV and ½HV derived from laboratory measurements are given in Fig. 9. It has been shown in Fig. 5 that HH measurements are much higher than VV. As a result QHV is typically greater than zero in Fig. 9(a), which indicates that vertically polarized radar may be less susceptible to WTI. The cross-correlation coefficient in Fig. 9(b) shows similar periodicity as the return power fluctuations. It is clear that ½HV has relatively low values where the return power peaks occur, which could be a result of scattering phase difference at these positions.

V.

MICRO-DOPPLER OBSERVATIONS For radar targets oscillatory motions caused by any structural components are called micro motions. The Doppler modulation due to micro motions can be extremely complicated and is often referred to as the micro-Doppler effect [25]. The major micro motion of a wind turbine is the blade rotation. Different parts of the blade have different radial velocities, scattering amplitudes, and phases, thus resulting in the time-varying Doppler spectrum. The spectrogram that displays the temporal evolution of the spectrum is shown in Fig. 10(a). The central zero-Doppler line is attributed to the stationary parts of the wind turbine, i.e., tower and nacelle. The periodic flashes are the key feature of the spectrogram. As has been discussed in Section III, when a blade is at vertical positions, the return power reaches a peak, where the signal oscillates rapidly. The oscillations indicate complex frequency


1597

components, which correspond to a Doppler flash. The flashes are the worst case scenario as far as radar interference is concerned because of the continuous spectral contamination. In addition to the periodicity, the flashes are found to be alternating signs about every 60± as a result of the blade moving in opposite directions when it is turning to vertical up and down. The spectrogram of a full-size wind turbine radar measurement [18] is shown in Fig. 10(b) for comparison. Though the two share some similarities, it is clear that the flashes in the latter are aliased due to limited PRF. The fact that the return power peaks coincide with Doppler flashes can be interpreted with a simple rotation model. In the polar coordinate where the blade rotates as shown in Fig. 1, the complex amplitude of scattered field from an arbitrary point (r, μ) on the blade is Eb (r, μ) = A(r, μ)ej2kr sin μ

(13)

where A(r, μ) is the scattering amplitude and k is wave number. Assuming that all major scattering centers are along a straight line, then the overall complex scattering amplitude of the blade is Z L Eb (μ) = A(r, μ)ej2kr sin μ dr (14) 0

where L is blade length. Simplifying the scattering center amplitude to be uniform and rotation invariant, i.e., A(r, μ) = 1, then ¯ ¯ ¯ sin(kL sin(!t)) ¯ ¯: jEb (μ)j = ¯¯ (15) k sin(!t) ¯

studies. The time-domain scaled model measurement significantly simplifies the measurement procedure and makes the complete radar measurement of the wind turbine over its motions possible. The RWT2 system, comprised of a wind turbine model and a laboratory-based dual-polarized pulse-Doppler radar, has been deliberately developed for such a purpose. By precisely matching radar measurements with wind turbine motions, radar variable fluctuations in terms of both rotation and yaw have been obtained for the first time. The periodicity and short-time statistics of wind turbine radar returns have been studied, and the fluctuation pdf has been modeled. The dual-polarized derivations imply that wind turbines have low cross-correlation coefficient when a blade turns to vertical position, where the return power peaks, short-time variance peaks, and minimum decorrelation time also occur. At these positions the micro-Doppler signatures reveal that the spectrum has complex high-frequency components in the form of Doppler flashes, which conventional ground clutter filters cannot remove. Hopefully, some of the radar signatures revealed in this study can provide some implications for signal processing algorithms to mitigate WTI in this worst case scenario. REFERENCES [1]

Since μ = !t, where ! is the rotation rate Eb (t) =

sin(kL sin(!t)) ikL sin(!t) : e k sin(!t)

(16)

[2]

Thus, the instantaneous Doppler frequency is 1 d[kL sin(!t)] !kL = cos(!t) 2¼ dt 2¼ or fd (μ) = (!kL=2¼) cos(μ). And the scattering amplitude is ¯ ¯ ¯ sin(kL sin(!t)) ¯ ¯: jEb (μ)j = ¯¯ k sin(!t) ¯ fd (t) =

(17) [3]

(18)

It is clear that the highest positive Doppler frequency and the amplitude maximum are reached at μ = 0± when the blade moves toward radar; the highest negative Doppler frequency with another amplitude peak occurs at μ = 180± when the blade moves away from the radar, where, in both cases, the blade is perpendicular to the radar LOS. This also makes sense as the blade has the largest velocity projection in the radial direction at these positions. VI. SUMMARY EMI caused by wind turbines on current radar networks has become a concern according to recent

1598

[4]

[5]

[6]

Brenner, M. Wind farms and radar. JASON, MITRE Corp., McLean, VA, Technical Report JSR-08-125, 2008. Belmonte, A. and Fabregas, X. Impact analysis of wind turbines blockage on Doppler weather radar. 2010 Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP), Barcelona, Spain, Apr. 12—16, 2010, pp. 1—4. Perry, J. and Biss, A. Wind farm clutter mitigation in air surveillance radar. IEEE Aerospace and Electronic Systems Magazine, 22, 7 (2007), 35—40. Poupart, G. J. Wind farms impact on radar aviation interests–Final report. QinetiQ, U.K., Technical Report DTI PUB URN 03/1294, 2003. Tognolatti, P. and Orlandi, A. Analysis of the EMI impact of an array of wind generators on the performances of secondary surveillance radar system. Proceedings of the 2008 International Symposium on Electromagnetic Compatibility (EMC Europe), Hamburg, Germany, 2008, pp. 1—4. Vogt, R. J., et al. Impacts of wind farms on WSR-88D operations and policy considerations. Presented at the 23rd Conference on Interactive Information Processing Systems (IIPS), San Antonio, TX, 2007.


JULY 2013

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

Vogt, R. J., et al. Recent efforts to improve estimates of and mitigate potential wind turbine clutter impacts on WSR-88Ds. Presented at the 27th Conference on Interactive Information Processing Systems (IIPS), Seattle, WA, 2011. U.S. Department of Defense Report to the congressional defense committees: The effect of windmill farms on military readiness. U.S. Department of Defense, Washington, D.C., 2006. Pinto, J., Matthews, J. C. G., and Sarno, C. Stealth technology for wind turbines. IET Radar Sonar & Navigation, 4, 1 (2010), 126—133. Rashid, L. S. and Brown, A. K. Radar cross-section analysis of wind turbine blades with radar absorbing materials. Proceedings of the 2011 European Radar Conference (EuRAD), Manchester, U.K., Oct. 12—14, 2011, pp. 97—100. Drake, P. Overview of Raytheon wind farm mitigation techniques and test results. Presented at the CNS/ATM Annual Conference, Orlando, FL, June 13—16, 2011. Aarholt, E. and Jackson, C. A. Wind farm gapfiller concept solution. Proceedings of the 2010 European Radar Conference (EuRAD), Paris, Sept. 30—Oct. 1, 2010, pp. 236—239. Nai, F., Palmer, R. D., and Torres, S. M. Range-Doppler domain signal processing to mitigate wind turbine clutter. Proceedings of the 2011 IEEE Radar Conference (RADAR), Kansas City, MO, 2011, pp. 841—845. Kong, F., Zhang, Y., and Palmer, R. Wind turbine clutter mitigation for weather radar by adaptive spectrum processing. Proceedings of the 2012 IEEE Radar Conference (RADAR), Atlanta, GA, 2012, pp. 0471—0474. Lok, Y. F., Wang, J., and Palevsky, A. Simulation of radar signal on wind turbine. Proceedings of the 2010 IEEE Radar Conference (RADAR), Washington, D.C., May 10—14, 2010, pp. 538—543. Zhang, Y., et al. Using scaled models for wind turbine EM scattering characterization: Techniques and experiments. IEEE Transactions on Instrumentation and Measurement, 60, 4 (Apr. 2011), 1298—1306.

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

Morlaas, C., Fares, M., and Souny, B. Wind turbine effects on VOR system performance. IEEE Transactions on Aerospace and Electronic Systems, 44, 4 (2008), 1464—1476. Isom, B. M., et al. Detailed observations of wind turbine clutter with scanning weather radars. Journal of Atmospheric and Oceanic Technology, 26, 5 (May 2009), 894—910. Kent, B. M., et al. Dynamic radar cross section and radar Doppler measurements of commercial General Electric windmill power turbines, Part 1: Predicted and measured radar signatures. IEEE Antennas and Propagation Magazine, 50, 2 (Apr. 2008), 211—219. Buterbaugh, A., et al. Dynamic radar cross section and radar Doppler measurements of commercial General Electric windmill power turbines, Part 2: Predicted and measured Doppler signatures. Presented at the Antenna Measurements Techniques Association (AMTA) Symposium, St Louis, MO, 2007. Kong, F., et al. Wind turbine radar signature characterization by laboratory measurements. Proceedings of the 2011 IEEE Radar Conference (RADAR), Kansas City, MO, May 23—27, 2011, pp. 162—166. Kouyoumjian, R. G. and Peters, Jr., L. Range requirements in radar cross-section measurements. Proceedings of the IEEE, 53, 8 (1965), 920—928. Dybdal, R. B. Radar cross section measurements. Proceedings of the IEEE, 75, 4 (1987), 498—516. Greving, G., Biermann, W. D., and Mundt, R. Radar and wind turbines — RCS theory and results for objects on the ground and in finite distances. Proceedings of the 2011 Microwaves, Radar and Remote Sensing Symposium (MRRS), Kiev, Ukraine, Aug. 25—27, 2011, pp. 321—326. Chen, V. C. The Micro-Doppler Effect in Radar. Norwood, MA: Artech House, 2011.


1599

Fanxing Kong received his B.S. and M.S. degrees in electronic engineering from Harbin Institute of Technology, China in 2004 and 2006, respectively. He is currently a Ph.D. candidate in the School of Electrical and Computer Engineering, University of Oklahoma. He works as a graduate research assistant in the Advanced Radar Research Center. His research interests include RF system design, signal processing, and the interference of wind turbines to radar.

Yan Zhang (S’03–M’04) received his B.S. and M.S. degrees in electrical engineering from Beijing Institute of Technology (B.I.T.), China in 1998 and 2001. He was a research assistant at the Environmental Remote Sensing Laboratory of the University of Nebraska, Lincoln from 2001 to 2004. From 2004 to 2007 he was a research scientist with Intelligent Automation, Inc., Maryland. He currently serves as an associate professor in the School of Electrical and Computer Engineering and Advanced Radar Research Center (ARRC), University of Oklahoma. His research interests include see-and-avoid intelligent radar sensing, RF/microwave systems, diversified radar systems, and high-speed array-processors-based serial data links.

Robert D. Palmer was born in Fort Benning, GA on June 3, 1962. He received his Ph.D. in electrical engineering from the University of Oklahoma, Norman in 1989. From 1989 to 1991 he was a JSPS postdoctoral fellow with the Radio Atmospheric Science Center, Kyoto University, Kyoto, Japan, where his major accomplishments were the development of novel interferometric radar techniques for studies of the lower and middle atmosphere. From 1993 to 2004 he was a member of the faculty with the Department of Electrical Engineering, University of Nebraska, Lincoln, where his interests broadened into areas including wireless communications, remote sensing, and pedagogy. He is currently the Tommy C. Craighead Chair with the School of Meteorology, University of Oklahoma (OU), Norman, where he is also an adjunct professor with the School of Electrical and Computer Engineering. He serves as Associate Vice President for Research and Director of OU’s interdisciplinary Advanced Radar Research Center (ARRC), which is the focal point for radar research and educational activities on the Norman campus. Since coming to OU his research interests have been focused primarily on the application of advanced radar signal processing techniques to observations of severe weather, particularly related to phased-array radars and other innovative system designs. He has published widely in the area of radar remote sensing of the atmosphere, with an emphasis on generalized imaging problems, spatial filter design, and clutter mitigation using advanced array/signal processing techniques. Prof. Palmer is a member of URSI Commission F, the American Geophysical Union, the American Meteorological Society, and has been the recipient of several awards for both his teaching and research accomplishments. 1600


JULY 2013