*Article* **The Choice of Optical Flame Detectors for Automatic Explosion Containment Systems Based on the Results of Explosion Radiation Analysis of Methane- and Dust-Air Mixtures**

**Sergey Khokhlov \*, Zaur Abiev and Viacheslav Makkoev**

Department of Blasting, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia; abiev\_za@pers.spmi.ru (Z.A.); viatcheslav.makkoev@gmail.com (V.M.)

**\*** Correspondence: khokhlov\_sv@pers.spmi.ru

**Abstract:** A review of the existing optoelectron monitoring devices revealed that the design of optoelectron detectors of the mine atmosphere does not sufficiently take into account the factor of external optical interference. This includes any extraneous source of thermal emission: a source of artificial lighting or enterprises. As a consequence, the optoelectron detectors -based safety systems currently installed at mining sites are not able to ensure properly the detection of the ignition source in the presence of optical interference. Thus, it is necessary to determine the working spectral wavelength ranges from methane and coal dust explosions. The article presents the results of experimental research devoted to the methane-air mixture and coal dust explosion spectral analysis by means of the photoelectric method. The ignition of a methane-air mixture of stoichiometric concentration (9.5%) and coal dust of size characterized by the dispersion of 63–94 microns and concentration of 200 g/m<sup>3</sup> was carried out in a 20 L spherical chamber with an initial temperature in the setup of 18–22 ◦C at atmospheric pressure. Then, photometry of the explosion light flux was conducted on a photoelectric unit. Operating spectral wavelength ranges from methane and coal dust explosions were determined. For the methane-air mixture, it is advisable to use the spectral regions at the maximum emission of 390 and 900 nm. The spectrum section at the maximum emission of 620 nm was sufficient for dust-air mixture. It enabled us to select the wavelength ranges for automatic explosion suppression systems' launching references. This will exclude false triggering of the explosion suppression system from other radiation sources. The research results will help to improve the decision-making credibility of the device in its direct design. The results will be used in further research to design noise-resistant optical flame detection sensors with a high response rate.

**Keywords:** explosion; coal dust; methane; explosion suppression; spectral characteristics; explosion pressure; radiation intensity; free radicals

#### **1. Introduction**

Dust and gas explosions are among the greatest disasters in the coal industry and are related with mass fatalities. This is a danger for the entire mining industry, not just coal mining [1]. In this regard, safety improvement during blasting operations in gaseous and dusty mines is of utmost importance [2]. Dust-methane explosion safety upgrades are possible only by a comprehensive approach, including risk management [3–5], development of tools and methods of mine explosion protection, methane emission control [6], coal deposits underground mining technological processes monitoring [7–10].

Among the effective ways to control possible explosions are the automatic ignition prevention systems. The sensors applied in automatic explosion barriers respond to high pressure (outside rods) and temperature, abnormal concentrations of explosive gases, optical parameters and flames [11]. The disadvantage of the sensors, responding to the explosion wave pressure, is their possible actuation because of extraneous acoustic signals. The authors of [12] provide an explanation of the outside rods' inefficiency due to their

**Citation:** Khokhlov, S.; Abiev, Z.; Makkoev, V. The Choice of Optical Flame Detectors for Automatic Explosion Containment Systems Based on the Results of Explosion Radiation Analysis of Methane- and Dust-Air Mixtures. *Appl. Sci.* **2022**, *12*, 1515. https://doi.org/10.3390/ app12031515

Academic Editor: Andreas Fischer

Received: 27 December 2021 Accepted: 28 January 2022 Published: 30 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

spatiotemporal parameters that are insufficient for successful explosion containment. The disadvantage of sensors, responding to the temperature changes, is their sensibility to the dusty mine environment, dust deposits, etc. [13]. Since the ignition process includes lighting, the perfect solution is optical sensors detecting dynamics in infrared and ultraviolet radiation and able to start the automatic prevention system to avoid combustion and explosions of methane-dust-air mixture.

The first thing we consider when estimating occupational injury risks is the air shock wave produced by methane-dust-air mixture's explosion [9]. Therefore, it's clear that an effective suppression system should be started at the very early ignition stages [14–16]. For this reason, the optical sensor's spectral response should be sufficient to start the suppression system.

There is one method [17,18] to measure object temperature without known emittance involvement. However, this method is not effective because of its slow response, due to the need to analyze and process a wide range of the optical spectrum [19]. In this regard, the correct choice of optical sensor is impossible without considering a specific flame and methane-dust-air explosion radiation spectrum.

A number of optoelectron devices (OEDs) have been proposed for use in fire detection and localization systems, including two spectral ratio optoelectron devices. The device uses radiation in three spectral ranges (750 ± 40 nm, 950 ± 50 nm, 1550 ± 12 nm). However, the study does not give a criterion for the choice of these ranges [20].

Moreover, for proper extinguishment within the development ratio with a crosssectional area up to 10 m2, it is necessary to create the explosion-suppressing environment by throwing out at least 30 kg of inhibitor within 15 ms. This throwing rate in case of false alarms can be dangerous for people located in the immediate vicinity to the explosion suppression devices [21].

Therefore, the development of construction and creation of a fast-operating OED control of explosive dust and gas atmosphere, insensitive to dustiness of the intermediate atmosphere and having a high probability of detecting the ignition source at an early stage in the presence of external optical interference, is an urgent scientific and technological task. It has an essential economic importance.

Thus, this research aims to obtain the spectral characteristics of combustion and methane-air mixtures' explosion radiation for the correct choice of input sensors applicable for explosion suppression systems.

#### **2. Materials and Methods**

The research into the flame radiation spectrum and methane, air and dust mixtures' explosion radiation spectrum was carried out by photoelectric method and aimed to obtain data on the nature of radiation energy wavelength distribution. This method enables spectrum recording with automatic dark signal subtraction and spectrograph wavelength calibration [22,23].

Currently it has been established [24,25] that gas radiation, heated by a shock wave, corresponds to "gray body" radiation, provided that the relative spectral energy distribution can be almost identified with any "black body" energy distribution at the temperature T.

The spectral-energy distribution of radiation emitted by a "black body" is described by the Planck formula:

$$B\_{\lambda} = \frac{C\_1 \cdot \lambda^{-5}}{e^{C\_2/\lambda \cdot T} - 1},\tag{1}$$

where *B<sup>λ</sup>* is emitting surface spectral brightness, *λ* is emission wavelength, *C*<sup>1</sup> and *C*<sup>2</sup> are constants, and *T* is emitting surface temperature.

Provided that *C*2/*λ* · *T* >> 1, Formula (1) is reduced to the classic Wien formula, which gives quite an accurate description of shock wave spectral energy distribution for the visible area:

$$B\_{\lambda} = c\_1 \lambda^{-5} \mathcal{e}^{-\frac{\xi\_2}{\lambda \mathsf{T}}}.\tag{2}$$

Formula (2) shows that for the curve reflecting spectral energy distribution of the luminous gases, which are gray emitters, it is necessary to provide its color temperature measurements.

The method of color temperature measurement is based on light intensity comparison from two spectrum areas.

Using Equation (2) we can have the following formula for energy Δ*E*, emitting by the body at the temperature *T*, wavelength *λ* and in the bandwidth Δ*λ*:

$$
\Delta E\_{\lambda, T} = c\_1 \lambda^{-5} \left( e^{-\frac{c\_2}{\lambda T}} \right) \Delta \lambda. \tag{3}
$$

If we know Δ*Eλ*,*<sup>T</sup>* for different wavelengths *λ*<sup>1</sup> and *λ*2, it is easy to calculate the emitter temperature, when the reference source and temperature *Tx* are available.

We have:

$$
\Delta E\_{1x} = \Delta E \lambda\_{1, T\_x} = C\_2 \lambda\_1^{-5} e^{-\frac{C\_2}{\lambda\_1 L\_x}} \Delta \lambda\_{1\_1} \tag{4}
$$

$$
\Delta E\_{2\text{x}} = \Delta E \lambda\_{2,T\_x} = \mathbb{C}\_2 \lambda\_2 \, ^{-5} e^{-\frac{\mathbb{C}\_2}{\lambda\_2 T\_x}} \Delta \lambda\_2. \tag{5}
$$

Dividing Equation (3) by Equation (4) and taking logarithms, we have the following expression for the emitter under study:

$$\ln\left(\frac{\Delta E\_{1x}}{\Delta E\_{2x}}\right) = -5\ln\left(\frac{\lambda\_1}{\lambda\_2}\right) + \ln\left(\frac{\Delta\lambda\_1}{\Delta\lambda\_2}\right) - \frac{C\_2}{T\_x}\left(\frac{1}{\lambda\_1} - \frac{1}{\lambda\_2}\right) \tag{6}$$

and for the reference source:

$$\ln\left(\frac{\Delta E\_{10}}{\Delta E\_{20}}\right) = -5\ln\left(\frac{\lambda\_1}{\lambda\_2}\right) + \ln\left(\frac{\Delta\lambda\_1}{\Delta\lambda\_2}\right) - \frac{C\_2}{T\_0}\left(\frac{1}{\lambda\_1} - \frac{1}{\lambda\_2}\right). \tag{7}$$

From Equations (6) and (7) we have the equation for the source color temperature *Tx* calculation using the available temperature value of the reference emitter *T*0:

$$\ln\left(\frac{\Delta E\_{10}/\Delta E\_{20}}{\Delta E\_{1x}/\Delta E\_{2x}}\right) = C\_2 \left(\frac{1}{\lambda\_1} - \frac{1}{\lambda\_2}\right) \cdot \left(\frac{1}{T\_x} - \frac{1}{T\_0}\right). \tag{8}$$

A general algorithm of the laboratory experiment is the following: in a closed combustion chamber of a 20-L spherical explosion chamber [26–29] shown in Figure 1, we have a mixture of stoichiometric concentration, supplied by a single step by compression with 2 MPa pressure [30,31]. The accidents in coal mines mainly occur because of methane and coal dust explosions [1,32]. It is these two components that were the object of the study. Further, it is flamed with 60 ms delay. The initial temperature in the unit is about 18–22 ◦C at the atmospheric pressure. The tested samples were prepared using the partial pressure method. Then, before ignition, the mixture was stirred by circulation pump, to ensure its homogeneity [33]. A luminous flow produced by the combustible mixture ignition was observed through the explosion chamber watch window.

**Figure 1.** Twenty-liter spherical explosion chamber. The unit layout. 1—Water output, 2—pressure sensor, 3—pressure gauge, 4—dust collector 0.6 dm3, 5—air intake, ignition source, 6—chemical igniters, 7—rebound sprayer, 8—fast-acting valve, 9—water intake, 10—air and resultant outlet.

The results of aerosol ignition of certain concentration, that was produced inside the chamber, as well as the explosion pressure and the explosion pressure rise rate are automatically recorded by the data processing system. This further data analysis enables us to conclude which is the successful ignition mixture (Figure 2).

**Figure 2.** Pressure trend (P, MPa) for the period (t, ms) of dust-gas mixture combustion inside the explosion chamber: Pd—expansion pressure of the combustion chamber; Pex—explosion pressure; td—exhaust valve delay; t1—combustion time; t2—induction time; tv—ignition delay time; Wp breakpoint in the rising part of the pressure curve; dP/dt—pressure rise rate at the explosion.

A pressure gauge with a response time of 0.2 ms is applicable for pressure measurements up to 2 MPa. Timing of pressure and flame radiation spectrum data recording from the ignition moment was controlled by ExTest software. To exclude the influence of the decomposition products of chemical igniters on the test result, the mixture was ignited by flash over. The energy produced from electrical initiation was 1 kJ.

Photometric measurement of light fluxes involved in the Equation (8) was carried out by means of photoelectric unit (Figure 3).

**Figure 3.** Basic diagram of temperature measurement. 1—Explosion chamber watch window, 2 band-lamp, 3—diaphragm, 4—splitter, 5—light filter, 6—photo multiplier, 7—cathode amplifier, 8—oscillograph.

The luminous flux was projected onto the inlet diaphragm of the unit using a rotatable flat mirror and lens. The luminous flux, after passing through the diaphragm, was recorded by photomultipliers using a beam splitting system, the signals of which were recorded from the oscilloscope screen. Two spectral intervals were separated by means of 15 nm bandpass interference filters.

The recording device was calibrated using standard stripe incandescent lamp. Its luminous flux was projected onto the inlet diaphragm of the unit through a hole in a rotating disk (chopper) using a rotary flat mirror and lens.

To measure the absolute radiation intensity, we used photomultipliers powered by high-voltage rectifier with electronic regulation.

For the radiator processes recording, it is necessary to consider not just the receiver's absolute sensitivity, but also the wavelength interval where this sensitivity remains effective.

One of the basic parameters of photodetectors is their time constant of the order of 10<sup>−</sup>8–10−<sup>10</sup> s.

The photomultipliers are characterized by significant photocurrent amplification factor and are well protected from the interferences caused by external electric fields. A relatively high input current of multistage photomultiplier enables to record output signals by cathode oscilloscopes without special broadband amplifiers involvement.

The oscilloscope's beam deviations are proportional to the radiation energy of the selected spectral intervals

$$D = \kappa \Delta E\_1 \tag{9}$$

where *D* is oscilloscope beam deviation, and Δ*E* is defined by Formula (3). Using Equation (8) we have:

$$\ln\left(\frac{D\_{10}/D\_{20}}{D\_{1x}/D\_{2x}}\right) = \mathcal{C}\_2\left(\frac{1}{\lambda\_1} - \frac{1}{\lambda\_2}\right) \cdot \left(\frac{1}{T\_x} - \frac{1}{T\_0}\right) \tag{10}$$

Let us denote the glow signals ratio of the investigated medium for the selected spectrum ranges by α, and the ratio of the calibration signals from a reference source at special color temperature T by β:

$$\mathfrak{a} = D\_{1\mathfrak{x}} / D\_{2\mathfrak{x}} \tag{11}$$

$$
\beta = D\_{10}/D\_{20} \tag{12}
$$

If we substitute Equation (11) and Equation (12) into Equation (10) we'll get a working formula for source temperature calculation:

$$\ln(\beta/\alpha) = c\_2 \left(\frac{1}{\lambda\_1} - \frac{1}{\lambda\_2}\right) \cdot \left(\frac{1}{T\_x} - \frac{1}{T\_o}\right) \tag{13}$$

If the reference source temperature is well known, then the temperature measurement error is calculated by the following formula:

$$\frac{\Delta T\_{\text{x}}}{T\_{\text{x}}} = \frac{\lambda\_1 \cdot \lambda\_2}{c\_2(\lambda\_2 - \lambda\_1)} \cdot \left(\frac{\Delta \alpha}{\alpha} + \frac{\Delta \beta}{\beta}\right) \tag{14}$$

The measurement error is mostly because of the finite thickness of the oscilloscope beam and photoelectronic multiplier noises. According to the calculations, the relative error does not exceed 10%.

#### **3. Results**

As shown in Figure 4a,b the mixture explosiveness assessment was carried out considering pressure values Pmax and the explosion pressure rise rate (dP/dt)max [34]. To show the spectral radiation characteristics, the intensity (I) was recorded and analyzed as it is shown in Figure 5. Parameter I0 is defined as the value at which the radiation intensity deviates from the baseline (I0 is 110% of the initial radiation intensity) [35]. The parameter t0 shows the time necessary to reach the maximum value of radiation intensity.

According to the received experimental data, the ignited methane of stoichiometric concentration in chamber produced maximal explosion pressure Pmax = 0.83 MPa and the rate of pressure rise was 42.05 MPa/s. When the explosive combustion of KS (KC) grade coal dust was registered in the Dzerzhinsky mine site, the maximum explosion pressure in the chamber was 0.79 MPa and the pressure rise rate was 34.62 MPa/s.

The results were analyzed using the application software. According to the obtained data, the graphs of changes in the pressure of methane and coal dust explosion were plotted against the time of explosive combustion of the mixture.

**Figure 4.** (**a**)Pressure-time curve based on CH4 ignition (9.5% Vol.). (**b**) Pressure-time curve based on the results of the coal dust ignition (concentration 200 g/m3, dispersibility 63–94 microns).

**Figure 5.** Maximum intensity of spectral radiation against time.

The results of experimental studies of radiation intensity measurement of methane-air and dust-air mixture explosion carried out by the electron-optical method are presented in the form of a dependence diagram, Figure 6.

**Figure 6.** Spectral energy distribution at maximum radiation of methane-dust-air mixtures.

The relative intensity dependence on the wavelength shows that wavelength distribution of radiation energy differs from the Planck's type of distribution. The graph (Figure 6) clearly shows two peaks at the wavelengths *λ*<sup>1</sup> = 383.5 nm and *λ*<sup>2</sup> = 620 nm. These peaks appeared when the banded spectrum of molecules and radicals overlaid the continuous radiation spectrum of the heated gas.

The peak point in the range of *λ* = 390 nm can be identified with band system of the radical CH radiation [36], and the one in the range of *λ* = 620 nm–with the band system of the molecule *C*<sup>2</sup> radiation (Swan system). The band system of molecule C2 is also in the range of *λ*<sup>1</sup> = 380 nm.

An intense series of bands are observed in the methane combustion spectrum, caused by the OH radical emission in the ultraviolet range of the spectrum *λ*<sup>1</sup> = 306 nm. The maximum radiation in the range of 900 nm should be identified with methane-air continuous combustion spectrum. In this case, the temperature, that was determined by the curve peak in accordance with Wien's displacement law, is *T* = 2610 K, which correlates well with the data received by spectrometric methods [37].

#### **4. Conclusions**

The availability of free radicals, which are considered the active centers of the chain reaction of methane explosion, is necessary for the entire process of explosive transition. Some free radicals, such as OH, H, O, CH3, HO2, CHO, are especially important not only for the explosion process, but also for the explosion suppression (inhibition) [38–40].

The flame spectrum analysis reveals the intermediate compounds formed during combustion and explosion and lets us study their behavior. Nowadays optical spectroscopy is the best method for free radical detection, since this method has no impact on the combustion and explosion process. The comparison of spectrum relative intensity enables us to get data on chemical reactions and involved radicals.

The resulting dependences of energy spectral distribution of explosions of methanedust-air mixtures enable us to determine the central wavelengths of bandpass filters and select the spectral ranges for input sensors and explosion suppression.

Thus, for methane-air mixtures, it is reasonable to use spectral regions at the radiation maximums of 390 nm and 900 nm. This helps to avoid false triggering of the explosion suppression system possibly initiated by other radiation sources. For a dust-air mixture, it is enough to use one spectral region at the radiation maximum of 620 nm.

Thus, it is proved that for the development of an active explosion suppression system in coal mines, particular attention should be paid to the choice of working spectral wavelength ranges for the recognition of the desired signal.

The most important parameter for optoelectron devices is the credibility of the decision. It is a complex parameter and is determined by a combination of the following:


We believe that further research should be devoted to initial combustion detection technology development with its further integration into multifunctional safety systems purposed for successful methane-dust-air combustion and explosion containment in coal mines and for the mines' industrial testing safety.

**Author Contributions:** Development of methodology, Z.A. and S.K.; creation of models, Z.A.; preparation and writing of the initial version, Z.A. and V.M.; provision of research materials, Z.A.; general control over the work, S.K.; mathematical processing, V.M.; data representation, V.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The study was carried out at the expense of a subsidy for the fulfillment of the state task in the field of scientific activity for 2021 No. FSRW-2020-0014.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

1. Goncharov, E.V.; Tsirel , S.V. Geodynamic methods for assessing methane distribution in bituminous coal deposits and measures to intensify methane fluxes during mine gas drainage. *J. Min. Inst.* **2016**, *222*, 803. [CrossRef]


### *Article* **High Spatial and Temporal Resolution Bistatic Wind Lidar**

**Paul Wilhelm \*, Michael Eggert, Julia Hornig and Stefan Oertel**

Physikalisch-Technische Bundesanstalt, Department 1.4 Gas Flow, Bundesallee 100, 38116 Braunschweig, Germany; michael.eggert@ptb.de (M.E.); julia.hornig@ptb.de (J.H.); stefan.oertel@ptb.de (S.O.)

**\*** Correspondence: paul.wilhelm@ptb.de

**Abstract:** The high-resolution bistatic lidar developed at the Physikalisch-Technische Bundesanstalt (PTB) aims to overcome the limitations of conventional monostatic lidar technology, which is widely used for wind velocity measurements in wind energy and meteorology applications. Due to the large measurement volume of a combined optical transmitter and receiver tilting in multiple directions, monostatic lidar generally has poor spatial and temporal resolution. It also exhibits large measurement uncertainty when operated in inhomogeneous flow; for instance, over complex terrain. In contrast, PTB's bistatic lidar uses three dedicated receivers arranged around a central transmitter, resulting in an exceptionally small measurement volume. The coherent detection and modulation schemes used allow the detection of backscattered, Doppler shifted light down to the scale of single aerosols, realising the simultaneous measurement of all three wind velocity components. This paper outlines the design details and theory of operation of PTB's bistatic lidar and provides an overview of selected comparative measurements. The results of these measurements show that the measurement uncertainty of PTB's bistatic lidar is well within the measurement uncertainty of traditional cup anemometers while being fully independent of its site and traceable to the SI units. This allows its use as a transfer standard for the calibration of other remote sensing devices. Overall, PTB's bistatic lidar shows great potential to improve the capability and accuracy of wind velocity measurements, such as for the investigation of highly dynamic flow processes upstream and in the wake of wind turbines.

**Keywords:** wind lidar; Doppler lidar; bistatic; metrology; traceability; wind energy; meteorology

#### **1. Introduction**

Accurate wind velocity measurements are an essential prerequisite for many applications in the field of wind energy and meteorology, such as wind potential analysis [1], the power curve evaluations of wind turbines [2] and atmospheric turbulence analysis [3]. For example, low measurement uncertainties are especially desired for reliable resource assessments of projected wind farms because the wind turbine power output scales with the third power to the wind velocity [4]. Wind met masts with cup and sonic anemometers are necessarily used for these applications [5]; however, they have the disadvantage of being inherently invasive and are thus prone to causing flow distortion effects [6]. Furthermore, taller masts covering hub heights of modern wind turbines are becoming economically less viable. Accordingly, ground-based wind lidar technology has become an alternative to wind met masts in the past few years, providing the distortion-free remote measurement of true wind velocity [7,8].

Conventional *monostatic* wind lidar systems measure the wind velocity component in the direction of a common transmitting and receiving beam, utilising the Doppler shift of scattered light from aerosols passing the transmitting laser beam [9]. Monostatic lidar systems provide reliable results when operated over flat terrain and in undisturbed—that is, homogeneous—flow [10]; however, these systems are not well suited for measurements over complex terrain. This is because the monostatic measurement principle can lead to measurement uncertainties in the order of 10% when operated in inhomogeneous

**Citation:** Wilhelm, P.; Eggert, M.; Hornig, J.; Oertel, S. High Spatial and Temporal Resolution Bistatic Wind Lidar. *Appl. Sci.* **2021**, *11*, 7602. https: //doi.org/10.3390/app11167602

Academic Editor: Anming Hu

Received: 16 July 2021 Accepted: 17 August 2021 Published: 19 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

flow [11,12]. Figure 1 illustrates this problem of the monostatic measurement principle resulting from the calculation of the horizontal velocity based on the difference of varying radial wind velocities measured at different locations and times within a single measurement cycle. The measurement beam within the measurement cycle is tilted in different directions around a circle with a height-dependent diameter of up to 100 m. Due to the dependence of the measurement uncertainty on the flow conditions at the measurement site, monostatic lidar systems are generally not traceable to the SI units. They are thus not permitted to be used as transfer standards for the calibration of other remote sensing devices [13].

**Figure 1.** A monostatic lidar generally introduces a large measurement uncertainty when operating over complex terrain. This is the result of calculating the horizontal velocity based on the difference of varying radial wind velocities measured at different locations and times within a single measurement cycle in which the measurement beam is tilted in different directions.

To overcome the aforementioned limitations and provide accurate and traceable measurements over any terrain, a *bistatic* wind lidar system has been developed at PTB. Figure 2 highlights the difference in operation of both lidar technologies. The conventional monostatic lidar (cf. Figure 2a) uses a rotating prism above a single combined transmitter and receiver unit scanning over a conical contour at different locations and times, with the beam's measurement volume extending up to 30 m in length [14]. In contrast, the bistatic PTB lidar (cf. Figure 2b) uses three dedicated receivers arranged at a radius of 1 m around a central transmitter, with all units focused into a spatially highly resolved, ellipsoid-shaped measurement volume (at a height of 5 m: diameter 300 μm, length 2 mm; at a height of 200 m: diameter 12 mm, length 4 m). This facilitates the simultaneous measurement of the three-dimensional wind velocity with exceptionally high spatial resolution, down to the scale of single aerosols. In consequence, the measurement uncertainty is largely independent of flow conditions at the measurement site.

Generally, the accuracy of lidar measurements is affected by atmospheric phenomena, as the effects of absorption, refraction and dispersion become increasingly significant with long-range measurements. During conditions of haze and only light fog, the laser light is scattered from an increased number of aerosols within the measurement volume, enhancing the signal rate. Depending on the particle density, forward scattering along the optical path can cause a multitude of spurious detections, effectively lowering the measurement data rate while increasing the measurement uncertainty. Ultimately, during conditions of very heavy fog, measurement fails completely due to the intense scattering and absorption of the laser light. Contrastingly, rain affects the measurement only slightly, since raindrop signals can be easily filtered out due to the significantly different vertical

velocity of the raindrops. The impact of atmospheric refraction is difficult to investigate in practice, but it was calculated to be negligible with respect to major factors influencing the total measurement uncertainty. For the signal bandwidths used with the PTB bistatic lidar, the impact of dispersion is expected to be practically negligible.

**Figure 2.** (**a**) A conventional monostatic lidar uses a single combined transmitter and receiver, resulting in a large measurement volume which needs to be multiplexed spatially and temporally by sequentially tilting the beam and sampling the wind speed. (**b**) The PTB bistatic lidar uses three separate receivers (*RX1*, *RX2*, *RX3*) around a central transmitter (*TX*), facilitating the simultaneous measurement of the three wind velocity components with high spatial and temporal resolution.

PTB's high-resolution bistatic lidar facilitates non-invasive wind velocity measurements at a sampling rate of up to 10 Hz (depending on the weather conditions at the measurement height) and is fully traceable to the SI units based upon the set-up's geometry, laser wavelength, and the digital time base used for frequency measurements. The detailed evaluation of the measurement uncertainty is currently in progress. Preliminary results indicate that the measurement uncertainty amounts to less than 1% at sufficiently high rates of detected particles.

#### **2. System Description**

The PTB high-resolution bistatic lidar is mounted on a custom-designed trailer, providing increased mobility and suitability for long-term outdoor use. The hinged cover is closed during field operation but may be opened during development and servicing, as shown in Figure 3. The sensitive optical set-up and the signal processing unit are located under the cover. To prevent vibrations during transport and operation, the optical set-up is mechanically decoupled from the trailer using air suspension. Further, to prevent the mechanical expansion and torsion of the optics carrier aluminium profile, the entire setup is climatised to about 20 ◦C using a 2 kW water-cooling unit. The transmitting laser amplifier is encased separately and water-cooled to reduce the excessive heating of the remaining set-up. Additional fans ensure sufficient air convection, and a dehumidifier prevents condensation forming on the optical windows inside the cover.

The optical receivers are located at a radius of 1 m around the central transmitter in order to provide both sufficient backscattered light intensity (quasi-backwards; i.e., in a quasi-vertical direction) and sufficient resolution for determining the horizontal wind velocity component. Each receiver includes a servo-driven focus lens and a tilting mirror, as shown in Figure 4. The tilting mirror is driven by a servo-piezo ensemble in order to provide sufficient resolution for the accurate positioning of the Gaussian beams at measurement heights of up to 250 m. As the transmitter does not need to be tilted, it only

consists of a focus lens and a fixed mirror. Optically adjusting and focusing all the receivers and the transmitter into the small measurement volume is achieved by means of a specially developed scanning algorithm, successively approximating motor positions based on the received light intensity and measurement height.

**Figure 3.** The PTB high-resolution bistatic lidar is mounted on a custom-designed trailer with a hinged cover (opened here), under which the optical set-up and the signal processing unit (SPU) are located.

**Figure 4.** Principle of the optomechanical set-up: Each receiver consists of a servo-driven focus lens and a tilting mirror which is driven by a servo-piezo ensemble in order to provide sufficient resolution for accurate positioning at measurement heights up to 250 m. As the transmitter does not need to be tilted, it only consists of a focus lens and a fixed mirror.

The PTB high-resolution bistatic lidar uses coherent detection, processing the interference of the transmitted and backscattered light of aerosols carried along the flow [15]. While bistatic lidar technology generally poses the problem of low backscattered signal intensity, the received Doppler spectrum is concentrated into a significantly narrower bandwidth compared to that of conventional monostatic lidar. This is due to the more uniform motion of aerosols within the small measurement volume, allowing the detection of Doppler peaks with an improved signal-to-noise ratio (SNR) [16]. At the receiver, the backscattered light from multiple aerosols with slightly different Doppler frequencies is detected [17]. Correlation techniques are used during signal processing, ensuring that only Doppler frequencies emitted by the same aerosol are evaluated [18]. The measurement height is first roughly determined by the theoretical height, which is set using the optomechanical actuators, and it is finely determined by means of the difference in coherent phases

of both Doppler shifted modulation peaks; due to the used modulation scheme, the phases are periodic over the beam's length.

While the vertical wind velocity component directly generates an absolute Doppler shift to the backscattered light, the horizontal wind velocity component must be computed by means of correlated frequency offsets between all three receiving channels. Overall, due to the sharp angle between the transmitter beam and the receiver beam, the detection of the vertical wind velocity component is orders of magnitude more sensitive than that of the horizontal wind velocity component [18]. With an average beam diameter of 10 mm between heights of 100 m to 200 m, the average transit time of a particle passing the measurement volume at a speed of 10 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> amounts to 1 ms. Both the reference laser's maximum bandwidth and the frequency resolution of the signal processing were chosen accordingly.

The optical set-up is constructed using single-mode fibre optics internally, as shown in Figure 5. A narrow-bandwidth (<1 kHz) laser reference of a wavelength of 1550 nm is modulated using an acousto-optic modulator (AOM), amplified (up to 30 W CW) using an erbium-doped fibre amplifier (EDFA), and coupled into the transmitter beam optics. The received light is fed into fibre couplers, where it is coherently mixed (heterodyned) with the light of the reference laser; the resulting beat frequencies are the Doppler frequencies, converted down into a lower frequency range. Each optical mixing product is fed into a balanced photodetector (PD), where it is converted to an electrical signal that is ready to be processed.

**Figure 5.** The optical set-up is constructed using fibre optics. A narrow-bandwidth laser reference is modulated, amplified and fed into the transmitter beam optics. The light scattered back into the receiving beam optics is fed into fibre couplers, where it is coherently mixed with the reference laser's light. Each optical mixing product is fed into a balanced photodetector, where it is converted to an electrical signal.

The signal processing is performed in two steps: a field-programmable gate array (FPGA) pre-processes the signals at a high data rate, and a CPU post-processes these signals at a lower data rate. Both units are contained inside a common MicroTCA system carrier, transferring data via a high-speed backplane protocol. The FPGA has four analoguedigital converters (ADC) and four digital-analogue converters (DAC) associated with it, all interfaced using a fixed-latency protocol to ensure deterministic phase relationships within the coherent signal chain. The AOM modulation signal is provided using one of the available DACs, and two spectral peaks at 75 MHz ± 12.5 MHz are added to the

transmitted light intensity. These are necessary for determining the Doppler polarity, signal phase and, accordingly, the measurement height by means of spectral signal processing. Three of the ADCs are used for digitising the received signals, as shown in Figure 6. The electrical signals from each balanced photodetector are subtracted and digitised using one dedicated ADC sampling at 300 MHz. Using the FPGA, the signal is mixed with a complex valued 75 MHz reference signal. The signal is then low-pass filtered and subsampled at a rate of 50 MHz. Finally, the signal is transmitted to the CPU, where it is transformed into the spectral domain using a fast Fourier transform (FFT) with a block length of 32,768 samples, taking about 1 ms to complete. Further signal processing, such as filtering and correlating, typically increases the total computing time per sampling block to about 7 ms (which is only about 10% of the theoretically achievable real-time performance).

**Figure 6.** The electrical signals from each balanced photodetector are digitised using one dedicated ADC sampling at 300 MHz. The signal is then mixed with a complex valued 75 MHz reference signal, low-pass filtered and subsampled at a rate of 50 MHz. Finally, the signal is transformed into the spectral domain using an FFT with a block length of 32,768 samples.

#### **3. Validation and Characterisation**

Since the development of PTB's high-resolution bistatic lidar started in 2010, several comparative measurements have been conducted in order to characterise this remote sensing device over a range of different operating conditions. Some of these measurements are described in the following sections.

#### *3.1. PTB Bistatic Lidar/Ultrasonic Anemometer (Vaisala WMT700)*

In 2014, the first comparative measurements between the PTB bistatic lidar and a Vaisala WMT700 ultrasonic anemometer mounted at a height of 10 m were conducted [19]. Being representative of complex terrain, a common measurement site amidst several buildings at PTB was chosen.

Considering the disturbed flow conditions and the distance of about 50 cm between the ultrasonic anemometer and the PTB bistatic lidar's measurement volume, the comparative measurements indicated a very good agreement for both wind speed and wind direction. Orthogonal linear regressions for the 1 min averages of wind speed and wind direction yielded Pearson correlation coefficients of 0.982 and 0.905, respectively.

#### *3.2. PTB Bistatic Lidar/Monostatic Lidar (WindCube)*

In 2014, two comparative measurements between the PTB bistatic lidar and a monostatic lidar (WindCube) were conducted over flat terrain at PTB's antenna testing ground, and over complex terrain amidst several buildings at PTB [20]. At each location, both devices were positioned at a distance of 10 m from each other and set to a measurement height of 100 m. Over flat terrain, both devices showed good agreement with deviations below 1% over a one-hour period of low atmospheric turbulence. However, over complex terrain, both devices showed significant deviations in the order of 15%, as it could be expected for measurements at different locations in turbulent flow and compared against conventional monostatic lidar technology.

#### *3.3. PTB Bistatic Lidar/Cup Anemometers (135 m Wind Met Mast)/Monostatic Lidar (WindCube)*

In 2015 and 2016, two comparative measurements between the PTB bistatic lidar and a 135 m met mast equipped with several cup anemometers were conducted at the

Deutsche WindGuard testing ground in Aurich, Germany [21,22]. In 2015, a cup anemometer mounted on a boom at a height of 100 m was used for reference, and the PTB bistatic lidar was positioned nearby at a distance of about 1 m. In this situation, both devices were exposed to the *disturbed wake flow* of a nearby wind turbine. In 2016, the cup anemometer at the top of the met mast (135 m) was used for reference and the PTB bistatic lidar was positioned next to the met mast at a distance of about 3 m. For this second campaign, monostatic lidar (WindCube) data were also available and were incorporated into the comparative measurements. Wind velocity data were evaluated according to IEC 61400-12-1 [5], sorting horizontal wind speeds into discrete bins with a resolution of 0.5 m · <sup>s</sup><sup>−</sup>1. Generally, large error bars indicate that few measurement samples were available in the corresponding wind speed ranges.

Figure 7 shows the deviation between the PTB bistatic lidar and the boom-mounted anemometer in *disturbed flow* and for 1 saveraging intervals. Here, the PTB bistatic lidar shows deviations below 1% over a large range of wind speeds from 6 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> to 12 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> when averaged over 1 s intervals. Figures 8 and 9 show the deviations between the PTB bistatic lidar and the top anemometer and between the monostatic lidar (WindCube) and the top anemometer in *undisturbed flow* for 10 min and 1 min averaging intervals, respectively. In *undisturbed flow*, for 10 min averaging intervals and within bins in which at least 10 wind speeds were sampled, the PTB bistatic lidar's deviation with respect to the top anemometer reference amounts to less than 0.5% (cf. Figure 8), which is well within the cup anemometer's measurement uncertainty (roughly between ±1% at 4 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> and ±0.7% at <sup>13</sup> <sup>m</sup> · <sup>s</sup><sup>−</sup>1). Under the same conditions, the monostatic lidar shows deviations up to −1.5% (cf. Figure 8). In *undisturbed flow* and for 1 s averaging intervals, the monostatic lidar shows large deviations (cf. Figure 9). This is because it resorts to interpolation for intervals shorter than its native sampling interval, which comprises multiple seconds for a single scan. Contrastingly, under the *same conditions*, the PTB bistatic lidar's deviation only increases significantly beyond 1% at very low or very high wind speeds. Extensive investigations and mathematical simulations showed that this is due to the different dynamic responses of the used sensing devices employed, and also due to the limited correlation of wind velocities sampled at slightly different locations [21].

Overall, owing to its high spatial and temporal resolution, the PTB bistatic lidar has been proven to achieve lower measurement uncertainty than conventional lidar systems, independent of the averaging time.

#### *3.4. PTB Bistatic Lidar/LDA in Wind Tunnel*

In 2018, the PTB bistatic lidar was characterised in the wind tunnel at PTB's Competence Center for Wind Energy (CCW). Being specifically constructed for the validation of the PTB bistatic lidar, the wind tunnel is mounted on a platform at a height of 8 m, allowing the PTB bistatic lidar trailer to be positioned immediately below it to sense its velocity field.

It was shown that the wind tunnel provides a well-defined, homogeneous velocity field suitable for calibrations. The wind tunnel shows a turbulence level of less than 0.35% for flow speeds ranging from 2 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> to 30 <sup>m</sup> · <sup>s</sup><sup>−</sup>1. At distances from the nozzle below 375 mm, where the measurements take place, it shows deviations of the flow velocity below 0.15% · dm−<sup>1</sup> [23].

A laser Doppler anemometer (LDA) was used as a reference standard, as LDA devices have measurement uncertainties below 0.2% [13], which is lower than the measurement uncertainties achievable with cup anemometers (roughly between ±0.7% and ±1%). Figure 10 shows the deviation of the PTB bistatic lidar from the LDA. These first validation measurements of wind speeds ranging from 4 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> to 16 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> showed an average deviation of 0.37% between the LDA and the PTB high-resolution bistatic lidar [24,25].

**Figure 7.** Results of the 2016 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on a met mast boom at a height of 100 m in *disturbed flow* at a measurement height of 100 m, evaluated according to IEC 61400-12-1 using 1 s intervals. The PTB bistatic lidar exhibits absolute deviations below 1% over a large range of wind speeds from 6 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> to 12 <sup>m</sup> · <sup>s</sup><sup>−</sup>1. The PTB bistatic lidar's deviation only increases significantly beyond 1% at very low or very high wind speeds. Extensive investigations and mathematical simulations showed that this is due to the different dynamic responses of the sensing devices employed, and also due to the limited correlation of wind velocities sampled at slightly different locations.

**Figure 8.** Results of the 2016 comparative measurement between the PTB bistatic lidar and the top anemometer and between the monostatic lidar (WindCube) and the top anemometer in *undisturbed flow* at a measurement height of 135 m, evaluated according to IEC 61400-12-1 using 10 min intervals. For bins in which at least 10 values were obtained, the PTB bistatic lidar's deviation is less than 0.5%, which is well within the cup anemometer's measurement uncertainty (roughly between ±1% at <sup>4</sup> <sup>m</sup> · <sup>s</sup>−<sup>1</sup> and <sup>±</sup>0.7% at 13 <sup>m</sup> · <sup>s</sup><sup>−</sup>1). Under the same conditions, the monostatic lidar shows deviations up to −1.5%.

**Figure 9.** Results of the 2016 comparative measurement between the PTB bistatic lidar and the top anemometer and between the monostatic lidar (WindCube) and the top anemometer in *undisturbed flow* at a measurement height of 135 m, evaluated according to IEC 61400-12-1 using 1 s intervals. The monostatic lidar shows large deviations, while the PTB bistatic lidar's deviation only increases significantly beyond 1% at very low or very high wind speeds.

**Figure 10.** Results of the 2018 comparative measurement between the PTB bistatic lidar and an LDA in the wind tunnel at PTB's Competence Center for Wind Energy, showing an average deviation of 0.37% between both devices. In order to verify that the PTB bistatic lidar is free of any angular dependence, an additional sample was recorded at 10 m · <sup>s</sup>−<sup>1</sup> (shown in pink) after the PTB bistatic lidar trailer was rotated 90◦.

#### *3.5. PTB Bistatic Lidar/Sonic Anemometer (CSAT3B) in Turbulent Flow*

In 2019, a comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast was conducted over flat terrain at the site of the Johann Heinrich von Thünen Institut (the German Federal Research Institute for Rural Areas, Forestry and Fisheries) in Braunschweig, Germany [3]. At this height, the PTB bistatic lidar's measurement volume (diameter *d* = 2 mm and length *l* = 50 mm) is comparable to that of the used sonic anemometer. The PTB bistatic lidar was positioned 9 m away from the mast, and both devices sampled wind velocity data at a rate of 10 Hz over a period of about two weeks.

Figure 11 shows the orthogonal linear regressions of the average horizontal wind velocity component *u*, of the fluctuation of the vertical wind velocity component *ww* 1/2, and of the shear stress velocity *u*∗. Table 1 shows the corresponding statistical parameters.

Both devices showed very good agreement, especially for the fluctuation of the vertical wind velocity component, with a mean deviation of only 0.017 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> at an intercept of −0.009 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> and a slope of 0.989. There was also good agreement for the average horizontal wind velocity component *<sup>u</sup>* with an intercept of 0.044 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> and a slope of also 0.989. However, at a slope of 0.973, the shear stress velocity *u*<sup>∗</sup> exhibited a slightly larger deviation from unity. This is demonstrably due to the angular dependence inherent to ultrasonic anemometers.

The dynamics of fully developed turbulence in the inertial subrange (an intermediate range of scales within the underlying energy cascade) can be described by certain similarity laws [26]. Specifically, the ensemble cospectrum (spectrum of the cross-correlation of two orthogonal velocity components) follows a −7/3 power law [27]. Figure 12 shows the ensemble cospectra *Couw* between the wind velocity components *u* and *w* of the PTB bistatic lidar and the CSAT3B sonic anemometer. The PTB high-resolution bistatic lidar is in excellent agreement with the theoretical distribution, showing a drop-off in cospectral power density consistent with the theoretical −7/3 law. However, due to its angular dependence, at higher frequencies, the CSAT3B sonic anemometer deviates significantly from the theoretical distribution.

Overall, although both the PTB bistatic lidar and the employed ultrasonic anemometer have proven to be well suited for measurements in turbulent flow, the PTB bistatic lidar is the more favourable option if high precision is desired. This is due to its distortion-free measurement capability, which is free of any angular dependence.

**Figure 11.** Results of the 2019 comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast in turbulent flow, showing orthogonal linear regressions of the average horizontal wind velocity component *u*, of the fluctuation of the vertical wind velocity component *ww* 1/2 and of the shear stress velocity *<sup>u</sup>*∗.

**Table 1.** Statistical parameters of the 2019 comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast.


**Figure 12.** Results of the 2019 comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast in turbulent flow, showing the ensemble cospectra *Couw* between the wind velocity components *u* and *w* (absolute values) of the PTB bistatic lidar and the CSAT3B sonic anemometer. The dashed line indicates the theoretical −7/3 power law in the inertial subrange.

#### *3.6. PTB Bistatic Lidar/Cup Anemometer (200 m Wind Met Mast)*

In 2020, a comparative measurement between the PTB bistatic lidar and a Thies Clima First Class Advanced cup anemometer mounted on top of a 200 m met mast was conducted near the summit of the Rödeser Berg near Kassel, Germany. Both the PTB bistatic lidar and the top cup anemometer were measuring at a height of 200 m for about three weeks with air temperatures ranging from −2.1 ◦C to 21.1 ◦C at a height of 10 m and from −2.7 ◦C to 17.8 ◦C at a height of 187 m. Additionally, wind direction data were obtained from a Thies Clima First Class wind vane mounted on a boom of the wind met mast at a height of 187 m. In order to enable a reasonable comparison, this particular measurement campaign required the use of filtering operations, removing periods of fog during which the lidar measurements were significantly impaired. Furthermore, to enable an evaluation according to IEC 61400-12-1, effects of turbulence due to the wakes of the nearby wind turbines were filtered out. Because of the low detection rate of particles at the measurement height of 200 m, 10 min averages are used throughout the following evaluation.

Figure 13 shows the overall measurement site, which includes two wind turbines (a third wind turbine located south-east is not shown). The PTB bistatic lidar trailer was positioned next to the met mast at a distance of 4.6 m. All angles stated in this section are referenced to the 0◦ north direction, counting in a clockwise direction (meteorological wind direction). Multiple pairs of oppositely mounted booms are located at different heights on the wind met mast at 145◦ and 325◦ angles. The wind turbines are located at 146◦ and 307◦ angles.

Figure 14 shows the deviation of the PTB bistatic lidar to the top anemometer for *unfiltered* 10 min averaged wind speeds larger than 4 m·s−1. However, these unfiltered samples are problematic in two ways. Firstly, large perturbation effects due to the wakes of the nearby wind turbines are visible around 146◦ and 307◦ angles, and secondly, due to the PTB bistatic lidar's optical measurement principle, fog occasionally causes large, nonsystematic deviations which are clearly visible between 90◦ and 270◦ angles. Thus, to enable a more reasonable comparison, the data were filtered around the angles corresponding to the wakes of the nearby wind turbines, and periods of fog were filtered using an upper limit on the backscattered signal amplitude. Figure 15 shows the deviation between the PTB bistatic lidar and the top anemometer for *filtered* 10 min averages, indicating good agreement between both instruments with absolute deviations below 2.5%.

**Figure 13.** (**a**) Measurement site including two wind energy turbines located at 146◦ and 307◦ angles (a third wind turbine located south-east is not shown). Around the met mast, the areas corresponding to the wakes of the nearby wind turbines are highlighted between 100◦ to 165◦ and 285◦ to 330◦ angles. (**b**) Slightly larger view of the wind met mast and its orientation with respect to the measurement site. (**c**) Met mast with multiple booms mounted at different heights and at 145◦ and 307◦ angles.

Figure 16 shows the deviation between the PTB bistatic lidar and the top anemometer for filtered wind speeds ranging from 4 m·s−<sup>1</sup> to 19.5 m·s<sup>−</sup>1. Here, wind velocity data were evaluated according to IEC 61400-12-1 [5], sorting horizontal wind speeds into discrete bins with a resolution of 0.5 m·s−1. Large error bars indicate that few measurement samples were available in the corresponding wind speed ranges. Again, both the PTB bistatic lidar and the top anemometer show good agreement, with mean deviations below 0.5% over a large range of wind speeds. Figure 17 shows the linear orthogonal regression of the wind directions measured by the PTB bistatic lidar and the wind vane mounted on the met mast. With an intercept of 3.736◦ and a slope of 0.996, both instruments are in excellent agreement regarding the measured wind direction.

Generally, a directional dependence of wind velocity measurements with respect to met mast booms has been extensively covered in the literature and is known as the *mast and boom effect*. Firstly, this effect causes large deviations in the wind velocity within a narrow angular range, when the anemometer is located in the lee of the mast. Secondly, an additional and more or less sinusoidal deviation in the range of ±1% to ±2% (for lattice towers) is induced over the entire angular range [6,28,29]. In order to mitigate the mast and boom effect specifically, this comparative measurement was conducted with respect to the top anemometer located at 200 m, even though there were many periods of fog at this measurement height.

Overall, the directional effects due to the wakes of the nearby wind turbines and the PTB bistatic lidar's sensitivity to periods of fog necessitated the additional filtering of the data obtained in this particular comparative measurement campaign. Nevertheless, both the PTB bistatic lidar and the wind met mast's top anemometer show good agreement with mean deviations below 0.5% over a large range of wind speeds. Furthermore, the wind directions as measured by the PTB bistatic lidar and the wind vane mounted on the met mast are in excellent agreement.

**Figure 14.** Results of the 2020 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on top of a 200 m met mast, showing the directional dependence of the mean deviation between the PTB bistatic lidar and the top anemometer for *unfiltered* 10 min averages. Large perturbation effects due to the wakes of the nearby wind turbines are visible around angles 146◦ and 307◦. Additionally, fog occasionally causes very large, non-systematic deviations which are clearly visible between 90◦ and 270◦ angles.

**Figure 15.** Results of the 2020 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on top of a 200 m met mast, showing the directional dependence of mean deviation between the PTB bistatic lidar and the top anemometer for *filtered* 10 min averages. Perturbation effects due to the wakes of the nearby wind turbines have been reduced by directional filtering. Furthermore, periods of fog were excluded from the data.

**Figure 16.** Results of the 2020 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on top of a 200 m met mast, evaluated according to IEC 61400-12-1 using filtered 10 min averages. Both instruments show good agreement with mean deviations between <sup>+</sup>0.25% and <sup>−</sup>0.5% over a large range of wind speeds.

**Figure 17.** Results of the 2020 comparative measurement between the PTB bistatic lidar and a cup anemometer mounted on top of a 200 m met mast, showing the linear orthogonal regression of the filtered wind directions measured by the PTB bistatic lidar and the wind vane mounted on the met mast.

#### **4. Conclusions and Outlook**

This paper has outlined the design details and theory of operation of PTB's highresolution bistatic lidar. It has also provided an overview of selected comparative measurements. The comparative measurements between the PTB bistatic lidar and the cup anemometers mounted on the met masts show that the measurement uncertainty of PTB's bistatic lidar is well within the measurement uncertainty of traditional cup anemometers (roughly between ±1% at 4 <sup>m</sup> · <sup>s</sup>−<sup>1</sup> and ±0.7% at 13 <sup>m</sup> · <sup>s</sup><sup>−</sup>1) while being fully independent of its site. The comparative measurement between the PTB bistatic lidar and an industry-standard ultrasonic anemometer (CSAT3B) in turbulent flow shows that the PTB

bistatic lidar provides distortion-free measurement capability which is free of any angular dependence and is in excellent agreement with the theoretical −7/3 power law in the inertial subrange.

To summarise, the high-resolution bistatic lidar developed at PTB has been proven to provide spatially and temporally highly resolved remote measurements of the wind velocity at heights ranging from 5 m to 200 m over any terrain. Furthermore, it provides measurement results with exceptionally low measurement uncertainty that are traceable to the SI units. Thus, it is planned to use the PTB high-resolution bistatic lidar as a transfer standard for other wind velocity remote sensing devices in the future. A full measurement uncertainty budget is currently in preparation and it is planned to be addressed in a separate paper. Overall, the PTB bistatic lidar shows great potential to enhance the capability and accuracy of measurements in applications such as wind potential analysis, the power curve evaluation of wind turbines and atmospheric turbulence analysis.

Nevertheless, there is still room for improvement. Adjusting the measurement height is currently slow, requiring multiple scans over an interval of several minutes. Therefore, the optomechanical section is being completely reworked. This is moreover being combined with an improved signal processing unit using the latest radio-frequency system-on-chip (RFSoC) technology. In the future, this will enable the fast and autonomous selection of measurement heights, allowing the PTB bistatic lidar to perform wind profile measurements. The improved signal processing will take the performance closer to real-time, increasing both the spectral resolution and the number of aerosols detectable per unit of time, which is also a requirement for fast beam alignment.

Several desirable functionalities and improvements are still missing from the PTB bistatic lidar at its current stage. Firstly, because of the PTB bistatic lidar's optical measurement principle, its measurement capability is degraded during weather conditions such as precipitation and fog. It is thus desirable to incorporate the automatic detection and mitigation of these conditions based upon the SNR and other spectral characteristics of the received Doppler spectra. Secondly, a thorough investigation of the measurement uncertainty as a function of the SNR and the characteristic distributions of correlated Doppler peaks for varying meteorological conditions is needed in order to improve upon the achieved measurement uncertainty by fine-tuning the digital signal processing chain. These are only some of the aspects that are to be addressed over the course of the coming years in order to provide a fully autonomous remote sensing device that is suitable for use as a transfer standard.

**Author Contributions:** Conceptualization, M.E.; methodology, M.E.; software, P.W., M.E.; validation, M.E.; formal analysis, M.E.; investigation, M.E., P.W.; data curation, M.E., P.W.; writing—original draft preparation, P.W.; writing—review and editing, P.W., M.E., J.H.; visualization, M.E., P.W., S.O.; supervision, J.H.; project administration, J.H.; funding acquisition, J.H., P.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi), funding codes 0325416B (*WindLidarDSP*) and 0325945 (*PTB Wind*).

**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to third party agreements. Data were obtained from Deutsche WindGuard GmbH, the Karlsruhe Institute of Technology, the Carl von Ossietzky University Oldenburg and the Fraunhofer Institute for Energy Economics and Energy System Technology.

**Acknowledgments:** We thank Deutsche WindGuard GmbH (Varel, Germany), the Karlsruhe Institute of Technology (Garmisch-Partenkirchen, Germany), the Carl von Ossietzky University Oldenburg (Oldenburg, Germany) and the Fraunhofer Institute for Energy Economics and Energy System Technology (Kassel, Germany) for making the joint measurement campaigns possible.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.

#### **Abbreviations**

The following abbreviations are used in this manuscript:


#### **References**

