Augmenting the signal processing–based mitigation techniques for removing wind turbine and radar interference

Abstract

The interference between wind turbine generators and radar is now being considered as one of the major deterrents towards seeking clearance for new wind energy project. The windfarm developers have to seek clearance from Civil Air Traffic Control and Defence Department certifying that the windfarms will not create interference with radars. The tower of wind turbine generator along with blades presents a large radar cross section to radars, thus creating static clutter; moreover, rotation of wind turbine generator blades creates Doppler ambiguities which confuse radar operators. Many radar designers have proposed mitigation techniques to overcome this issue; however, each technique has its own limitation. The study takes a two-pronged approach to address the issue of wind turbine generator static clutter due to tower and blades and the resolution of Doppler ambiguities through signal processing–based mitigation techniques. In addition, the study also suggests the use of micro-Doppler techniques for signature identification of wind turbine generator blades for eliminating their effect during the radar signal processing. The article presents a step-by-step mitigation technique to resolve the wind turbine generator and radar interference issue.

Keywords

Windfarm clutter radar mitigation Doppler Kalman filter

Introduction

The global installed capacity for wind energy has reached more than 560 GW by 2018 (International Renewable Energy Agency, n.d.). With a shift from fossil fuel–based fuels towards use of renewable energy, wind energy holds the key towards the paradigm shift in use of sustainable energy. The use of wind energy, however, presents a critical technical impediment due to interference of wind turbine generator (WTG) blades with radars (Perry and Biss, 2007). There have many instances where military have voiced their opinion, and the issue of windfarm interference is now being considered a constraining factor in achieving wind energy targets. A number of wind energy projects in United States were halted due to concerns over wind energy interference with radars. Between year 2008 and 2010 military and Air Traffic Control (ATC) concerns on wind turbine interference contributed to delaying of 20,000 MW of wind energy projects in United States (Auld et al., 2013). The effect of WTG on radars is different and specific for each case depending upon radar design parameters like frequency of operation, pulse repetition frequency (PRF), wavelength, antenna characteristics (phased array/dish) and signal processing techniques.

Although many mitigation techniques and theories have been put in place for reduction of interference between WTG and radars, however each has inherent limitations. The mitigation techniques have been suggested by modifying the radar parameters, processing techniques, redesigning WTGs with reduced radar cross section (RCS) and siting the radars and WTGs away from line of sight (LOS) to each other (Auld et al., 2013; De La Vega et al., 2013; Deshmukh et al., 2019; Gallardo-Hernando et al., 2010; Nai et al., 2011; Perry and Biss, 2007; Sergey et al., 2008; Uysal et al., 2014). The use of shaping and stealth techniques has been suggested resulting in reduction of RCS; however, they are expensive and cannot resolve Doppler ambiguities due to WTG blades. Although theoretically use of stealth material like radar absorbent paints can reduce the RCS, however use of stealth techniques is not practical due to various reasons and cost being one of them. Techniques like moving target detector (MTD), control of radar parameters like sensitivity time control (STC), constant false alarm rate (CFAR) can optimize the radar performance but cannot resolve completely the WTG interference issues (Danoon et al., 2014; Matthews et al., 2007). The study is based on ground-based radars and makes an attempt to cover the major aspects of surveillance, tracking and modern multifunctional radars employed by military. The article suggests a step-by-step mitigation process which employs radar signal processing along with use of micro-Doppler techniques in wind turbine sector.

Identifying the static clutter for radar at site

Selecting the WTG clutter zone

The present day multifunctional radars are designed to carry out the functions of search, track and can even support track information for weapon systems. The current generation radar systems employ Active Phased Array system with Transmit Receive Modules in place of conventional devices like magnetron, klystron or travelling wave tubes (TWTs). These radars can be designed to function as ATC radars, primary surveillance radars or multifunctional radars employing very low side beams, flexibility to move the beams electronically, digital beam forming and processing techniques. These capabilities if effectively employed can be used to mitigate the WTG clutter effects.

The effect of WTG clutter will depend upon the design characteristics of the radars. The WTG clutter shall also be impacted by the type of beam forming systems employed. Designers have attempted radars with very low side lobes and side lobe blanking (SLB) techniques to avoid ground clutters. For military radars, SLB also acts as electronic counter counter measure (ECCM) technique. The basic design features like increased bandwidth, receiver dynamic range and STC which can reduce the sensitivity at closer ranges limit the returns from WTG and prevent the receiver from getting saturated.

The target while being tracked with radar will be affected with the presence of WTGs only when the target enters the area which is affected by WTG clutter. The requirement is to implement effective filtering techniques during that particular instant to mitigate the effect of WTGs. The radar detection and signal processing techniques can be augmented to process the target and drop unwanted false targets/clutter from available returns.

The radar processing techniques allow the radar scope to be configured into sectors by the operator as per the operational requirements. The sectors can be based on the requirement of low emission zones, no emission zones, sectors requiring quick target verification, sectors requiring longer time on target (TOT). In this technique, the radar scope will be configured for special processing techniques for the sector impacted with WTG clutter as shown in Figure 1. The techniques of range azimuth gating (RAG) and track initiation inhibition have been attempted, and it has been found that these techniques carry the risk of losing the track (Sergey et al., 2008). The study suggests the application of signal processing techniques in the WTG clutter zone along with micro-Doppler processing techniques for mitigating the effect on radars in the chosen sector.

Figure 1.

Sector classification for WTG clutter.

Clutter mapping

The RCS presented by WTG is large. The WTG clutter will compromise of clutter from the stationary tower and rotating wind turbine blades which will also create the problem of Doppler frequency shift. There is also the likelihood ground clutter due to urban structures, terrain, mountains, trees and so on. Although most of these structures are likely to produce permanent Echo, but their reflection will also largely depend upon atmospheric conditions. A clutter map formation algorithm for primary radar is given (Reznicek et al., 2016). Once radar has been deployed at a site near to wind farms, the clutter map from the WTG and other permanent ground structures can be prepared and the radar thresholds can be optimized.

The ground clutter maps can be prepared by analysis of clutter received from each resolution cell. The total area covered by the radar is divided into range azimuth cells as shown in Figure 2. The size of the resolution cell will depend upon the radar design parameters. Each cell will measure the power levels and classify the received echoes as permanent clutter, which will also include clutter from static portions of WTG like the WTG towers. Processing techniques like moving target indicator (MTI) will be employed to cancel the identified permanent echoes like WTG towers. Formation of clutter maps can be a standard operating feature after the radars are switched on every day.

Figure 2.

Range azimuth gates.

MTI for removal of WTG tower clutter

Whereas WTG clutter created by rotation of WTG blades is an issue which needs to be addressed, however static clutter mitigation is also an important issue as this can lead to loss of target return which may get hidden behind the clutter spectrum. In a WTG, the tower of the wind turbine produces more clutter than WTG blades. The clutter power spectrum is concentrated at zero frequency and is periodic depending upon the PRF as shown in Figure 3.

Figure 3.

Target and clutter return in clutter power spectrum.

MTI is a default feature in most modern radars and is likely to cancel the static WTG tower clutter. The employment of radar processing techniques for ground systems particularly for military radars varies depending upon the intended role. For example, some ground-based military weapon systems employ multifunctional radars which are capable of both surveillance and tracking. On the other hand, some designers may prefer employing low-frequency radars for search and high-frequency radars for tracking. The MTI for some of radars is configurable where the operator can select the Doppler frequency thresholds which are required to be cancelled. The choice of the PRF has to be carefully selected so that the unambiguous range of the radar is large enough to meet the radar’s operational requirement. In order to avoid Doppler ambiguities, the radar should ideally have high PRF. However, for long range radars, it is difficult to be both Doppler and range unambiguous. The problem is sometimes resolved using multiple PRFs.

The MTI filter frequency response is designed in such a way so as to have zero output at f = 0 and maximizing the filter output return so that the moving aircraft/target is not filtered. The MTI can be designed to cancel the clutter due to WTG stationary part like tower. The MTI is designed using delay line cancellers. The Doppler frequency is given by f_d

f_{d} = 2 V / ƛ

(1)

For a Doppler radar operating with a frequency of 8 GHz, the wavelength is 0.0374 m. For WTG blade rotation between 1 and 100 m/s, the Doppler frequency will be between 54 and 5347 Hz. Delay line cancellers are employed for subtracting the response from the MTI output by delaying the output in reference to PRF. Delay line cancellers can also be designed with feedback loops which are known as recursive filters having a feedback loop for shaping the frequency response of the filter (Mahafza, 2016). The radar using MTI employs Doppler filter banks. The detection threshold is calculated as a function zero filter where clutter output is placed. By designing the MTI output response so as to filter out the zero Doppler clutter, the issue of stationary clutter can be mitigated. The maximum blade tip speed for a 1-MW WTG will be approximately 100–200 mile/h, these Doppler returns will not be filtered by MTI (Lemmon et al., 2008). Hence, multiple processing techniques need to be implemented for mitigating the WTG radar interference issues. Specific signal processing techniques for filtering the dynamic clutter of WTG blades are proposed in next section.

Augmenting tracker-based signal processing techniques

Create ‘no automatic plot to track conversion zone’

Once detection has been made by radar, the formation of a track is the result of measurements and estimation of target attributes. The characteristics of measurements will be in the form of azimuth, velocity, range and height and depend upon the sensor capabilities. Once a measurement is made or an event is generated, the data are associated as to determine if they are from the same track/source. Measurements are associated and co-related for generation of plots and conversion to tracks. Measurements can be associated with new track or a current track within the predefined detection area called as Gate. Gating is done for measurement/observation, association and verification. Algorithms like nearest neighbour (NN)/global nearest neighbour (GNN) are used for data. The GNN algorithm has a better performance over NN algorithm for target tracking in WTG areas (Sergey et al., 2008). In our approach, the first step is the formation of WTG clutter zone. This is the sector which is affected with WTG clutter. This sector is selected in the radar as the area of No plot to track conversion zone. This means that automatic track correlation/association algorithm will not take place in this sector, instead different tracker-based track correlation and verification algorithms are recommended for the sector.

Enhanced tracking filter for WTG clutter zone

The tracker of the modern day radar systems relies on estimation theory. Most of the surveillance/tracking radars rotate with fixed rate of revolution, restricting the TOT due to the field of view. Moreover, tracking multiple targets at the same time makes the radars dependent on estimation techniques. The trackers are designed to handle manoeuvring targets, multiple targets, accelerating targets, taking turns or targets through clutter using estimated prediction-correction in recursive filters (Karabayir et al., 2016; Svanström, n.d.).

Kalman filtering technique uses Bayesian estimation principles to predict the values of state variables from measurements which are corrupted because of noise. The filter predicts the state variable values based on the available measurements, the noise variance and system dynamical model. The filter can be used as an estimator of the state of dynamic system (Svanström, n.d.)

X_{k} = A_{k} - 1 X_{k} - 1 + B_{k} - 1 U_{k} - 1 + W_{k} - 1

(2)

where $A_{k} - 1$ defines the state described by $X_{k} - 1$ at time k− 1 and state $X_{k}$ at time k; $B_{k} - 1$ relates the control in put vector $U_{k} - 1$ ; and $W_{k} - 1$ is process noise.

The measurement vector $Z_{k}$ is related to the state of the system

Z_{k} = H_{k} X_{k} + V_{k}

where $V_{k}$ is noise and $H_{k}$ is observation matrix.

The system noise covariance is

Q_{k - 1} = E (W_{k - 1} W_{k - 1}^{T})

(3)

where E is the expected value and the measurement

P_{kIk} = E [(X_{k} - {\hat{X}}_{KIK}) {(X_{k} - {\hat{X}}_{KIK})}^{T}]

(4)

where the estimate ${\hat{X}}_{KIK}$ is called as posteriori estimate; ${\hat{X}}_{KIK - 1}$ is priori estimate.

The Kalman filtering process involves prediction of state matrix and error covariance. The Kalman gain is calculated with reference to measurement, and the estimate is computed. With the calculation of error covariance, the recursive cycle is repeated again.

The Kalman filter can be used for prediction and removal of noise as shown with the help of simulation results in Figure 4. The Kalman filter is, however, not suitable for tracking complex scenarios as required in scenario under discussion. Enhanced filters such as the interacting multiple model (IMM) tracker can be employed for the WTG zone. The IMM uses two or more different models to predict the target state. As compared to decision-based models, IMM technique uses parallel filters estimating based on selected models. The final estimate is created based on the sum of all estimates which is a weighted combination and produces reliable estimate.

Figure 4.

(a) Target track plan; (b) target with induced noise; and (c) target noise filtered by Kalman filtering.

The models can be constant velocity, turn model or singer model. The selection of models to be used will depend upon the type of application/expected target profiles. For the purpose of estimation, state equations are used to describe different modes of operation. A Markov transition matrix defines the probability for the target in the modes of operation. The model probabilities are updated after each measurement (Wilson, 2001).

Once the target enters the clutter zone, the IMM filter will be used for track estimation. Depending upon the kind of radar which is deployed near the wind clutter zone, suitable IMM filter can be implemented. In case an ATC radar is near the clutter zone, the IMM filter will be deigned differently than when a military radar because the military radars will be designed to counter highly manoeuvring targets which are likely to make rapid manoeuvres as compared to civil traffic. A civil ATC IMM estimator as discussed in Bar-Shalom et al. (2001) can employ a constant velocity model along with a manoeuvring model with coordinated turn. A constant velocity model is defined by

x (k + 1) = ⌊ \begin{matrix} 1 T 0 0 \\ 0 1 0 0 \\ 0 0 1 T \\ 0 0 0 1 \end{matrix} ⌋ x (k) + ⌊ \begin{matrix} \frac{1}{2} T^{2} & 0 \\ T & 0 \\ 0 & \frac{1}{2} T^{2} \\ 0 & T \end{matrix} ⌋ v (k)

(5)

x = [\begin{matrix} ξ & \overset{\cdot}{ξ} & η & \overset{\cdot}{η} \end{matrix}]'

where x is the target state and T is sampling time; $ξ$ and $η$ are coordinates; and $v (k)$ is noise for coordinating turn model $Ω$ is turn rate (Bar-Shalom et al., 2001)

x = [\begin{matrix} ξ & \overset{\cdot}{ξ} & η & \overset{\cdot}{η} & Ω \end{matrix}]'

x (k + 1) = ⌊ \begin{matrix} 1 & \frac{\sin Ω (k) T}{Ω (k)} & 0 & \frac{- 1 - \cos Ω (k) T}{Ω (k)} & 0 \\ 0 & \cos Ω (k) T & 0 & - \sin Ω (k) T & 0 \\ 0 & \frac{1 - \cos Ω (k) T}{Ω (k)} & 1 & \frac{\sin Ω (k) T}{Ω (k)} & 0 \\ 0 & \sin Ω (k) T & 0 & \cos Ω (k) T & 0 \\ 0 & 0 & 0 & 0 & 1 \end{matrix} ⌋ x (k) + [\begin{matrix} \frac{1}{2} T^{2} & 0 & 0 \\ T & 0 & 0 \\ 0 & \frac{1}{2} T^{2} & 0 \\ 0 & T & 0 \\ 0 & 0 & T \end{matrix}] v (k)

(6)

The IMM for wind turbine clutter region has to be designed specifically keeping in view the kind of target aircrafts which are anticipated to be intercepted by the radar in the vicinity of wind turbine zone. For applying mitigating techniques for ATC radar, a suitable combination of constant velocity model and coordinating turn model can be used. The choice between the type of models and number of models for IMM estimator depends upon manoeuvring profile of the likely target expected to be tracked. Two IMM estimators are simulated with constant velocity model and coordinated turn model with small linear accelerations for IMM1 and a higher turn rate for IMM2. The simulation comparison is done for a target trajectory defined in Figure 5. Simulation between Kalman filter and IMM estimator shows that the performance of IMM estimator is better than Kalman filter as shown in Figure 6. The IMM filtering process will involve predicting the covariance for all the selected models. The initial model probabilities are to be carefully chosen, mixed and updated.

Figure 5.

Manoeuvring target track.

Figure 6.

RMS error comparison with different filtering techniques.

A part from algorithms like GNN as discussed, scan-to-scan correlation for plot to track confirmation is employed. The association and confirmation algorithm will check the target past kinematics for preventing false association of WTG blades with targets inside the WTG clutter zone. The quick verification over the WTG clutter zone is avoided. The study also suggests employing micro-Doppler techniques for mitigation of WTG clutter as an additional method which can be employed to ensure removal of WTG Doppler returns.

Mitigation using radar micro-Doppler in WTG sector

The Doppler frequency shift states that when a target is moving with a velocity the returned signal will have a shifted frequency which will depend upon its wavelength and relative velocity between the tracked target and the radar. The shift in frequency will also depend upon whether the target is moving towards the radar or moving away from the radar. If the target has any structure which has rotational motion or vibrations, then these vibrations induce frequency modulations generating side bands with the Doppler frequency which is called as micro-Doppler effect. By calculating the Fourier transform, the frequency shift can be calculated which will provide an estimate of velocity dispersion due to the micro-Doppler effect (Chen, 2011; Parker et al., 1992). For explaining the concept, the rotor blade of helicopter can be compared to WTG blades. The time domain signature of the blades is given by equation (7) (Chen, 2001)

s (t) = \sum_{k = 0}^{n - 1} Lsinc (\frac{4 π}{λ} \frac{L}{2} \cos β \sin (Ω t + θ o + k 2 \frac{π}{N}))

(7)

where $Ω$ is the rotation rate; $β$ is the azimuth angle of rotor; L is the length of the WTG blade; and $θ o$ is the angle of scatter of WTG blade.

The ground-based radars operate anywhere from low frequency to medium frequency for long range (mostly surveillance radars). The tracking radars/weapon control radars usually operate in X-band or higher bands. The MATLAB simulations are carried to cover wide range of frequencies from 100 MHz, 2 GHz and 8 GHz to evaluate the WTG blade returns. The WTG in our simulations is assumed to be having three blades, the blade length is taken as 35 m. The micro-Doppler for three cases is calculated with blade length of 35 m and rotation rate of WTG from 20 to 45 r/min. The carrier frequency used for radar in simulations is starting from 100 MHz, 2 GHz and 8 GHz. The selection of 100 MHz is done to cover some of the long range radars which are still operated in very high frequency (VHF) band. The 2 GHz is the most commonly used frequency for surveillance and multifunctional radars, and 8 GHz and beyond is mostly employed by tracking radars.

The wind tip velocity will be offering the speeds and Doppler frequency shifts which are similar to the aircraft. The WTG blade returns are shown with different wavelength. The time domain signal of the WTG blades is shown in Figures 7 and 8. The WTG blades have flashes for which the rate is determined with the rotation rate of WTG blades. The duration is determined by the blade length, wavelength and the r/min.

Figure 7.

(a) Amplitude and frequency spectrum of radar carrier frequency at 100 MHz for 20-r/min blade rotation rate. (b) Amplitude and frequency spectrum of radar carrier frequency at 2 GHz for 20-r/min blade rotation rate. (c) Amplitude and frequency spectrum of radar carrier frequency at 8 GHz for 20-r/min blade rotation rate.

Figure 8.

(a) Amplitude and frequency spectrum of radar carrier frequency at 100 MHz for 45-r/min blade rotation rate. (b) Amplitude and frequency spectrum of radar carrier frequency at 2 GHz for 45-r/min blade rotation rate. (c) Amplitude and frequency spectrum of radar carrier frequency at 8 GHz for 45-r/min blade rotation rate.

The time domain signal of radars with different wavelengths and with known rotor blade lengths provides specific time domain returns. The rotor blade returns have flashes, and the rate of flashes will depend upon the rotation of the rotor. The length of the blade and wavelength will determine the duration of flash. The Doppler spread for each WTG sector can be modelled and fed in the radar signal processor unit to identify and undertake filtering of WTG signals.

Discussion and mitigation methodology

Unlike other mitigation techniques suggested to mitigate the radar and WTG clutter interference issues, this technique suggests step-by-step process for removal of static clutter followed by dynamic clutter removal by applying selective mitigation technique for the chosen sector. The first step is to reduce the effect of total static ground clutter. A method of mapping the permanent ground clutter was shown by undertaking clutter mapping. The static clutter will be eliminated using the MTI. This will also help in removing the effect of WTG tower. In the second phase, the mitigation techniques for removing the interference effect of WTG blades are undertaken.

The Range Angle Gating is applied for the WTG affected sector, and the signal processing techniques are utilized for selecting the WTG affected zone as the No Automatic Track initiation zone. The normal tracker filter for the sector is changed to special IMM filter. The Kalman filter works on the minimum mean square state error estimate. The limitation of Kalman filtering is that with manoeuvring targets, as the target changes its dynamics by undertaking manoeuvre, the Kalman filter state estimator can cause track loss. This is due to the incorrect modelling of target dynamic behaviour, which can be handled using IMM filter. The article suggests use of IMM for the WTG clutter zone. A fixed period smoothening approach using IMM is suggested. The selection of manoeuvre detection model will be based on expected target trajectories. Use of association and correlation algorithms will be undertaken for plot to track conversion. In addition, the article suggests use of micro-Doppler techniques to capture the WTG returns. These can be modelled and stored in the radar computer to be filtered during the target processing. The complete process is described in the process flow chart in Figure 9.

Figure 9.

Windfarm clutter mitigation process flowchart.

Conclusion

The requirement of WTG to co-exist with military, ATC and meteorological radar is an inescapable reality. Due to increasing population and growth of cities, it will not be always possible to locate the radars and windfarms away from LOS to each other. The article suggests a novel approach by utilizing multiple signal processing features available within radars for mitigating the radar and WTG interference issues. The study supports the mitigation technique with the help of simulation. The study targets the WTG sector with specific signal processing techniques using MTI for cancelling the static clutter from WTG by first making a clutter map for the deployed region and subsequently using MTI for cancelling the static clutter. For handling the clutter from WTG blades, the study suggests not to undertake automatic track initiation for the WTG sector and initiate the separately designed IMM filtering technique. The selection of IMM models will depend upon the type of radar which is being deployed near the WTG clutter. In addition, the use of micro-Doppler returns from WTG rotating blades is also considered as an additional input for identifying the WTG blade clutter, which can be filtered from the target.

Footnotes

Declaration of conflicting interests

The author(s) declares that there is no conflict of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

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