FI20245491A1

FI20245491A1 - Acquisition of more diverse sensor data

Info

Publication number: FI20245491A1
Application number: FI20245491A
Authority: FI
Inventors: Ville Nurmi
Original assignee: Lakiasiaintoimisto Ville Nurmi
Priority date: 2024-04-18
Filing date: 2024-04-18
Publication date: 2025-10-19
Also published as: WO2025219651A1

Abstract

Disclosed is a method for obtaining sensor data, the method comprising: obtaining, using a first sensing device, a first sensor data from one or more sensors of the first sensing device, wherein the first sensing device is associated with a first wind turbine located at a first location, receiving the obtained first sensor data by a central computing entity, receiving, by the central computing entity, a second sensor data obtained by a second sensing device located at a second location, the second location being different than the first location, and combining, by the central computing entity, the first sensor data and the second sensor data.

Description

OBTAINING MORE VERSATILE SENSOR DATA

FIELD

The present disclosure relates to obtaining sensor data and more precisely, obtaining versatile sensor data provided by a plurality of sensing devices.

BACKGROUND

Renewable energy such as wind power is of great interest as they help to produce green energy and alleviate reliance on fossil fuels. As producing wind power requires wind turbines to be placed at locations that combined cover large — areas, which may be away from centres of inhabitation, it may not be easy to gain knowledge of the environment adjacent to the wind turbines. Yet, having knowledge of the environment may be desirable such that the operation environment of the wind turbines can be monitored.

BRIEF DESCRIPTION

The scope of protection sought for various embodiments is set out by the independent claims. Dependent claims define further embodiments included in the scope of protection. The embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the disclosure.

According to a first aspect there is provided: a method for obtaining sensor data, the method comprising: obtaining, using a first sensing device, a first a sensor data from one or more sensors of the first sensing device, wherein the first

AN 25 — sensing device is associated with a first wind turbine located at a first location, 3 receiving the obtained first sensor data by a central computing entity, receiving, o by the central computing entity, a second sensor data obtained by a second

I sensing device located at a second location, the second location being different - than the first location, and combining, by the central computing entity, the first > 30 sensor data and the second sensor data. 2 According to a second aspect there is provided a system comprising, a

O first wind turbine comprised in a first wind farm, the first wind turbine being associated with a first sensing device, and a second sensing device located away from the first wind farm, and a central computing entity, wherein the system further comprises means for performing a method according to the first aspect.

According to a third aspect there is provided computer program product comprising instructions, which, when executed by a system comprising one or more computing devices, cause the system to perform a method according tothe first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is described in detail by means of specific embodiments with reference to the enclosed drawings, in which:

Figure 1 illustrates examples of wind turbines and wind farms.

Figure 2 illustrates examples of sensing devices.

Figure 3 and figure 4 illustrate block diagrams according to exemplary embodiments.

Figure 5 illustrates an exemplary embodiment of a computing device.

DETAILED DESCRIPTION

Electricity can be understood as a basic necessity for societies. As electricity is to be available for societies to function properly, production of electricity is of great interest. As the reliance on fossil fuels is to be reduced, renewable energy sources are being developed further. Wind is an example of a renewable energy source that can be utilized to alleviate reliance on fossil fuels.

Wind power can be produced by wind turbines. A plurality of wind turbines, that forms a group of connected wind turbines, may be understood as a wind farm.

Figure 1 illustrates an example of a wind farm 100. The wind farm comprises thus

N 25 aplurality of wind turbines that are configured to produce electricity from wind.

N An individual wind turbine, such as the wind turbine 120 may be

S understood as a device that is configured to convert kinetic energy of wind into

D electrical energy. Wind turbines may vary in terms of their size. The size may be = determined at least partly based on the purpose of the wind turbine. For example, ” 30 if the wind turbine 120 is aimed at charging batteries, or provide power to > relatively small devices with low power consumption, the size may be smaller 3 than if the wind turbine is part of a wind farm that is to produce electricity to a

N grid. The axis of the wind turbine 120 may be horizontal or vertical. Thus, the wind turbine 120 may have blades 125 that rotate either around vertical or — horizontal axis. Yet, in some examples, the wind turbine 120 may be bladeless as well. The wind turbine 120 also comprises a wind tower 127.

The wind turbines illustrated in the wind farm 100 are horizontal-axis wind turbines having three blades each, though other types of wind turbines could be included in addition, or instead of such wind turbines. The wind turbines in this example have a main rotor shaft and an electrical generator on top of a tower. The tower may be understood to be comprised in the wind turbine. The tower may be mounted on a foundation. The tower may also be connected to an electric grid. The tower may also have one or more units for connectivity, which may be wired or wireless connectivity thus allowing data connectivity to the wind turbine. This may be beneficial in case the wind turbine is to be controlled remotely.

The wind farm 100 is located on land, in an area without inhabitation.

The wind farm may be surrounded by a forest in which case the wind turbines may extend above the treetops. Alternatively, the wind farm 100 could be located in aland area without forests, such as in a desert. Additionally, or alternatively, at least some of the wind turbines could be located on top hills for example to obtain better exposure to wind. It is to be noted that the wind farm may comprise a few wind turbines, tens of wind turbines, several hundreds of wind turbines, or any other amount that is suitable. In some examples, the land in between individual wind turbines may be used for example for agricultural purposes. The wind turbines comprised in a wind farm may be located in any suitable formation. In the wind farm 100 the wind turbines are clustered, but in some other examples the wind turbines could be for example along a line, in one row or in multiple rows. Such line could follow for example geographical formation or other any — other suitable line defined for example based on geographical characteristics. < In figure 1 there is also another example of a wind farm, the wind farm

N 150. In this example the wind turbines are located in two lines along a shore 155. 5 The shore 155 may be for example a shore to a sea or to an ocean. Alternatively, = the wind farm 150 could be an offshore wind farm such that the wind turbines — 30 are located in water. The wind turbines could then be for example floating wind

E turbines, or they could be mounted to a mounting structure like wind turbines — located on the land. The wind farm 150 could extend along the shore for a few x kilometres, for tens of kilometres or for hundreds of kilometres for example.

N In a wind farm, such as wind farm 100 or 150, at least some individual

N 35 wind turbines may optionally be connected to each other such that data may be exchanged between them.

As the wind turbines in a wind farm may cover an extensive land area that may be far away from inhabitation areas, the wind turbines thus are located in places from which sensing data may not be easily obtained. For example, as the wind towers of wind turbines may extend above treetops, the wind turbines provide visibility that may otherwise be difficult to obtain. One way to obtain such visibility could be to utilize satellites, but then again, satellites orbit around the Earth and thus following one certain location could be challenging.

Additionally, visibility from satellites is subjected to cloud conditions. Wind turbines may thus offer a location for sensing devices that obtain visibility both above treetops but also closer to the wind turbine, which may allow sensing the surrounding environment and detecting for example movements of the land that could happen during heavy rain falls and/or behaviour of wild animals. Some wild animals could even be strong enough to cause harm to the wind turbine in which case sensing such activity could be beneficial. In case the wind farm is located along a shore, or the wind farm is an offshore wind farm, visibility toward the sea or an ocean could be gained that could otherwise be difficult to gain. This would allow monitoring movements of the sea or the ocean such that for example exceptional waves could be detected before they reach the shore.

Various devices, which may be referred to as sensing devices, may be used for obtaining sensor data. Sensor data may be understood as data provided by one or more sensors comprised in, or connected to, a sensing device. Thus, there may be different types of sensing devices and sensors. Figure 2 illustrates some examples of sensing devices that may be used to obtain sensor data.

A satellite 210 that orbit around the Earth may comprise one or more imaging devices, which may be considered as sensing devices, that are configured < to produce satellite images, which may be considered as sensor data. The satellite

N 210 may in some examples be dedicated to producing such images in which case 5 it may be understood as an imaging satellite. Images obtained from satellites may = be utilized for various purposes such as for meteorology, oceanography, fishing, — 30 agriculture, biodiversity conservation, forestry, land scape, geology, cartography

E and so on. Resolution of images obtained using satellite imaging may be defined — using different types, such as spatial resolution, spectral resolution, temporal x resolution, radiometric, and/or geometric resolution. The resolution of the

N satellite imaging is dependent on the imaging device(s) used as well as the

N 35 altitude of the orbit of the satellite.

Another type of sensing device is a radar 220. A radar may be understood as a system configured to determine a distance based on transmitted radio waves that are received again after they have reflected from an object. The output received from a radar 220 may thus be considered as sensor data.

Depending on the types of radio waves used, the distances that can be measured 5 canvary from a few kilometres to hundreds of kilometres. Therefore, radars may be utilized for example to detect and track for example motor vehicles, ships and aircrafts. Additionally, radars can be used to track weather formations and terrain. A radar, such as the radar 220, may comprise a transmitter that is configured to produce electromagnetic waves such as microwaves or radio waves, an antenna configured to transmit the produced electromagnetic waves and a receiving antenna to receive those electromagnetic waves that are reflected back. The transmitting and receiving antenna may be the same antenna, or they may be different, depending on the implementation of the radar 220. The antenna may also be rotated, and thus configured to transmit the produced electromagnetic waves to multiple directions, or the antenna may be configured to transmit the electromagnetic waves to one certain direction. The antenna may then comprise for example a phased antenna array to allow transmission of a beam of electromagnetic waves such that the direction of the beam can be controlled. This may allow better and stronger targeting towards one direction of interest. The radar may also comprise a computing device that is configured to process the received electromagnetic waves and optionally also perform analysis such that an object and/or its distance may be determined. The analysis may be performed using any suitable computer software. It is also to be noted that in some examples the analysis may comprise using one or more trained machine learning models to identify one or more objects based on the received < electromagnetic waves.

N The electromagnetic waves transmitted by the antenna may also be 5 referred to as radar signals. As the radar signals are reflected back by an object, = the signals not only reflect, but may also scatter. Given that the radar is often used — 30 in circumstances introducing high noise levels to the signals that are reflected

E back and then detected, signal processing has great significance in terms of — interpreting and making correct determinations based on the received radar x signal that have been reflected back to the radar. How well radar signals are

N reflected back may be dependent on the material of the object from which they

N 35 — arereflected. For example, as most metals are good in terms of their electrical conductivity capabilities, they tend to reflect the signals well. Also, sea water and wet ground reflect the radar signals well.

Cameras may also be understood as sensing devices and the images obtained from the cameras may be understood as sensor data. There are many different types of cameras for different purposes and thus the images they produce may vary. An example of a camera that could be used as a sensing device is a surveillance camera 230, which may be used to for example monitor different activities, such as traffic or movements of wild animals, and/or to gather information. The surveillance camera 230 may provide video data and/or still images. It may be configured to produce data continuously, at certain intervals, and/or when there is a pre-determined triggering event to capture image data.

The surveillance camera 230 may further be configured to move around, which allows to expand its field of view.

It is to be noted that also other types of cameras could be utilized to obtain sensing data. For example, there may be cameras that are used to gather visual information from an airborne vehicle such as a helicopter or a drone. There may also be cameras that are configured to produce visual data based on temperatures, such as temperatures on different surfaces. Such cameras form an image based on infrared radiation they receive from an object. Different temperatures are illustrated in different colours. As for example animals have different temperatures at different parts of their bodies, an animal can be detected even in low-light conditions, which is beneficial as not all animals move during daytime. Also, an animal has surface that typically has different temperature than the surrounding environment and thus the animal, or any other suitable object, such as a vehicle, can be detected from the surrounding environment. < As yet another example of a sensing device, a lidar 240 can be used to

N obtain sensor data. Lidar, which may be understood to refer to laser imaging, 3 detection, and ranging, may be used to determine ranges by transmitting laser - beams towards a target object, and measuring the time for the reflected laser — 30 beams to return to a detector that is co-located with the transmitter. Like radar, a

E lidar may operate towards one fixed direction, or it may rotate and operate — towards multiple directions. When a lidar operates towards multiple directions, x the scanning may be understood as 3D scanning. In this example, the lidar 240 is

N a multi-dimensional lidar that is configured to produce the laser beam 242, which

N 35 isthen directed using a mirror 244 such that it rotates as illustrated by the arrow 246 and thereby performs a scanning sweep of the environment. The reflections detected during the scanning sweep are then processed using any suitable image processing method such that an illustration of the environment is obtained.

Lidars may target various objects. The objects may be made of various materials, such as metal or non-metal, rock, chemical compounds, and/or aerosols. Lidars may be used for various purposes, for example, in geography, archaeology, geology, forestry and atmospheric physics.

When observing the environment for various purposes, such as determining phenomena in nature or monitoring traffic, various sensing devices may be utilized, and the various sensing devices have different types of capabilities thus producing different types of information as their output, which can be considered as sensor data. To be able to utilize the potential of different sensing devices, the sensing device are to be placed to locations allowing them to obtain sensing data from the target environment that is of interest. As wind farms emerge, the wind turbines may be utilized for placing sensing devices to locations that otherwise might be difficult to get to and/or to be able to obtain sensing data from viewpoints and/or fields of view that otherwise might be difficult to obtain.

For example, by placing a sensing device approximately on top of the wind tower of a wind turbine, that allows a viewpoint that may be difficult to obtain even from an airborne vehicle. Also, by using the wind tower as a mounting structure for the sensing device, the sensing device may be statically placed for example above treetops. Additionally, or alternatively, there may be one or more additional sensing devices mounted on the wind turbine, for example lower on the wind turbine, and the one or more other sensing devices may be of the same type of sensing devices or of different types. The sensor data obtained from one or more sensing devices that are mounted on one or more wind turbines may < then be received for example by a server computing device, or a group of

N computing devices acting as back-end servers. As the wind turbines may already 3 have data connectivity capabilities, those data connectivity capabilities may then - be utilized for receiving the sensing data as well. Thus, sensing data obtained from the location of one or more wind turbines may be utilized as such or it may

E be combined with data from other sensing devices as well. — Figure 3 illustrates a block diagram according to an exemplary x embodiment of obtaining sensing data from multiple sensing devices mounted on

N wind turbines, or alternatively, placed in proximity of the wind turbines such that

N 35 the sensing device may be identifiable in the context of an individual wind turbine. For example, the individual wind turbine may share its mounting structure and/or connectivity means with the sensing device. Whether the sensing device is mounted on the wind turbine or placed in its proximity, the wind turbine and the sensing device can be considered to be associated with each other. It is to be noted that a plurality of sensing devices may be associated with one wind turbine.

In this exemplary embodiment, there are sensing devices 312, 314 and 316 that are comprised in a first wind farm, such as wind farm 100 discussed in the context of figure 1. Correspondingly, there are sensing devices 322, 324 and 326 that are comprised in a second wind farm, such as wind farm 150 discussed in the context of figure 1. Alternatively, all sensing devices could be comprised in the same wind farm or in more than two wind farms. Each of these sensing devices is associated with its respective wind turbine. The sensing devices could be any type of sensing device, such as any of the types discussed in the context of figure 2. Yet, in this exemplary embodiment, for the sake of explanation, the sensing devices 312 and 316 are radars, while the sensing device 314 is a lidar.

The sensing devices 322 and 324 are also radars, while the sensing device 326 is a surveillance camera.

The radars 312, 316, 322 and 324 may be located on such wind turbines that are on the edge of their respective wind farms, for example in the outermost row of wind turbines in case the wind turbines form a line, or in case the wind turbines form a cluster, on the edge of the cluster. It is to be noted that the wind turbines hosting the radars do not have to be next to each other, but the sensing range of the radars could be taken into account when placing the radars such that uniform coverage is enabled. For example, the radar 312 and 316 may be mounted on wind turbines that are on the outer edge of the wind turbine < cluster forming their respective wind farm, and there are one or more wind

N turbines between them. The radars 312 and 316 may be placed to face away from 5 the wind farm and thus provide visibility to the area away from the wind farm. As = the wind turbines form a cluster, to provide better coverage of the area outside — 30 the wind farm, the radars 312 and 316 may be configured to be rotating radars

E thus adapting to the curvature of the outer edge of the cluster and covering as — large an area as possible. In this exemplary embodiment, the lidar 314 is then x placed on a wind tower of a wind turbine located in between the wind turbines

N associated with the radars 312 and 316. The lidar may be activated for example if

N 35 either, or both, of the radars 312 and 316 provide sensor data based on which itis determined that additional information is beneficial, and the lidar 314 is capable of providing beneficial sensor data that can be used as the additional information.

The lidar 314 may then provide the sensor data that may be for example more detailed and thus help in detecting for example an object, and what the object is, or otherwise provide further information regarding the object. Optionally, the sensor data obtained from one or both of the radars 312 and 316 could be combined with the sensor data obtained from the lidar 314 and thus the overall sensor data becomes more versatile and informative.

In this exemplary embodiment, the radars 322 and 324 are part of the second wind farm. It is to be noted that is some other exemplary embodiments, — there may be sensor data obtained from sensing devices having their respective associated wind turbines in one wind farm, or in more than two wind farms. In this exemplary embodiment, the wind turbines in the second wind farm are placed in two lines such that there is a front line and a back line, and the lines are defined by geography. Yet, the line could be defined using any other criteria as well and the line does not have to be a straight line. Also, there could be just one line or there could be a plurality of lines. In this exemplary embodiment the adjacent wind turbines in one line are 1 km away from each other, but any other distance could be used as well. It is to be noted that in some exemplary embodiments, the wind farm could be a combination of line(s) and cluster(s). In this exemplary embodiment, the wind turbines in the front line and in the back line are placed such that they are interleaved, although any other suitable placing could be used as well. The radar 322 is placed on a wind turbine that is in the front line and the radar 324 is placed on a wind turbine that is the back line. This allows the sensing data from these radars to be combined such that better coverage can be obtained depth wise. The radars 322 and 324 are placed such < that the sensing data they produce overlaps at least partly such that the area

N away from the wind turbines can be properly covered by the sensing data. 5 Optionally, the radars 322 and 324 may be rotating radars. If based on the = sensing data obtained from the radars 324 it is determined that camera image — 30 from the area would be useful, then the camera 326, which is located in the same

E wind tower as the radar 324, may be activated and image data from it may be — obtained. x It is to be noted that in a wind farm there may be more sensing devices

N than are discussed in this exemplary embodiment. To process and analyse the

N 35 sensor data obtained from the sensing devices 312, 314, 316, 322, 324 and 326, data connectivity means may be utilized to transmit the produced sensor data to a central computing entity 350, such as a server. The server could be hosted by a computing device, by a group of computing devices or it could be a cloud-based service. Optionally, to avoid transmitting unnecessary data and to allow pre- processing of the sensor data obtained from the sensing devices, edge computing may be utilized between the sensing devices and the central computing entity 350. Such edge computing may be implemented using one or more computing devices. The sensing devices 312, 314 and 316 in this exemplary embodiment are connected to the edge computing unit 330, which may be understood as a logical unit the implementation of which may vary. The sensing devices 322, 324 and 326 in this exemplary embodiment are connected to the edge computing unit 340, which may also be understood as a logical unit the implementation of which may vary.

The central computing entity 350 may perform any suitable image processing and/or data processing to analyse the sensor data obtained from the — sensing devices. Additionally, the central computing entity 350 may combine at least some of the sensor data obtained from the sensing devices with sensing data obtained elsewhere. For example, there may be sensor data obtained from an imaging satellite, from a weather station providing radar-based sensing data or from aradar located at an airport providing sensing data. By combining these — sensing data images more coverage as well as more versatile data may be obtained which may be beneficial for example when observing nature. For example, the radars at weather stations and/or at airports may be limited in terms of their sensing capabilities due to the wind farm(s). Thus, by having sensing devices incorporated to the wind farm(s) and facing towards a direction — that would otherwise be limited to the radar outside of the wind farm, such < limitation can be alleviated and additionally, more versatile sensing data may be

N obtained. Optionally, at least some of the wind turbines may have blades that may 5 then have material that enhances the combining of the sensing data. For example, = if the blades are made of material that enhances reflection of the radar signals,

TY 30 then the wind turbines are better visible for radars that sense them from outside

E of the wind farm. Optionally, the radars outside the wind farm may be utilized to — detect if the blades of the wind turbines are turning, or not turning as intended. If x the central computing entity 350 then determines that they are not turning or not

N turning as intended, then the central entity may activate a sensing device, such as

N 35 the camera 326 or the lidar 314, in proximity of a wind turbine that may not function as intended, to activate and provide sensing data. Based on that sensing data it may be verified if the wind turbine is not functioning as intended.

It is to be noted that in case a sensing device such as a radar is to be active and provide sensor data, then its associated wind turbine may be among wind turbines that are not rotating. In case the field coverage of the sensing area oftwo adjacent radars with their respective associated wind turbines are overlapping, then sufficient coverage may be obtained although one of the radars would not be activated as its associated wind turbine would have its blades rotating. This may be beneficial in case the wind turbine is for example in a more optimal position with respect to wind and to allow wind turbines to be used in an approximately even manner. The central computing entity 350 may for example determine when a sensing device is to be activated and then provide a command to the sensing device accordingly via the data connectivity means. Such determination may be done for example based on analysing obtained sensor data.

Itis to be noted that the central computing entity, which may be understood as a computer device system comprising one or more computers configured to processes and analyse sensing data obtained from one or more sensing devices, may be located in any suitable location. For example, it may be an independent entity located separately from all the sensing devices, or it may be located for example along with one sensing device, for example together with a radar — configured to monitor weather conditions and/or traffic, which may be vehicle traffic, air traffic or a combination of both.

Figure 4 illustrates a block diagram according to an exemplary embodiment in which obtained sensor data is processed and analysed by a system 420 such as the central computing entity 350 described above, or any — other suitable computing system comprising one or more computing devices. The < blocks are to be understood as logical units and they are used herein for the sake

N of explanation, so the actual implementation may vary. First in block 410 sensor 5 data is obtained from one or more sensing devices. When sensor data is obtained = from a sensing data, it may be real-time sensor data, or almostreal-time sensor — 30 data. Alternatively, or additionally, at least some sensor data may be stored first,

E for example by using edge computing, and then transmitted to a central — computing entity, for example, in pre-processed batches of information. x The sensor data 410 obtained by the system 420 may be so called raw

N data that has not been processed, or it may be pre-processed. Pre-processing may

N 35 also be performed by the sensing device itself. The pre-processing may comprise for example noise filtering and/or amplifying to better bring out signals detected by the sensor(s) of the sensing device. Additionally, or alternatively, image processing and/or object recognition may also be performed before transmitting the sensing data 410 to the system 420. This may be beneficial in some examples if itis pre-determined that the sensing data is to be transmitted merely in case a certain object is recognized, or there is an object that is not recognized. This allows to reduce the amount of data that needs to be transmitted. Such object recognition may be performed for example by the sensing device, by an edge computing unit or by a combination of both.

The system 420, which may be the central computing entity, thus receives the sensor data 410 from the one or more sensing devices. In this exemplary embodiment, there is at least one sensing device that is associated with a wind turbine, although there may be a plurality of such sensing devices from which sensing data is obtained. There may also be sensing device that are not associated with any wind turbines and are located away from any wind turbines. After the sensing data is received, through any suitable data connectivity means, there is a sensor data processing unit 422 that first processes the data. The sensor data may be processed for example to remove noise by filtering, decrypting the transmitted data in case it was protected using encryption, and/or in case the data was transmitted using data packages, — ordering them correctly.

Once the obtained sensor data, which may also be referred to as received sensing data, has be processed, it can then be analysed by the sensor data analysing unit 424. The data analysis may comprise for example object recognition, pattern recognition, recognition of one or more changes in the environment, or determining any other pre-determined, or unexpected, changes. < For example, the object recognition may recognize movements of wild animals or

N birds, movements of an ocean, movements of cloud formations or movements of 5 land-based or airborne vehicles. The analysis may be performed using one or = more software algorithms, in other words, by any suitable computer software. — 30 Additionally, or alternatively, trained machine learning models may also be used

E for the object recognition. For example, neural networks may be used to analyse — the obtained sensor data. x After the analysing is performed, there is then a sensor data combining

N unit 426, which is configured to combine sensor data obtained from a plurality of

N 35 sensing devices and/or combine the sensor data obtained with previously obtained sensing data. Thus, when performing the analysis, the obtained sensor data may be analysed in combination with previously received sensor data such that developments over time can be taken into account. Additionally, sensor data from different sensing devices may be combined with each other to obtain more information and a more holistic coverage of the sensed area. For example, a weather station may observe formation of weather conditions. Yet, when that information is combined with sensor data obtained from sensing devices that are associated with wind turbines of an offshore wind farm, then there is more sensor data available, which enables more detailed and versatile analysis of the weather conditions. Additionally, images obtained from an imaging satellite may also be combined with the obtained sensor data thus providing even further data for the analysis.

When combining sensor data obtained using two or more sensing devices, the obtained sensor data may be for example superimposed to one another, they may be compared with each other, and/or the obtained sensor data may be parsed together such that overlapping parts of the sensor data from different sensing devices are used as markings where to parse the data together.

Alternatively, or additionally, any other suitable data processing may be performed to combine the sensor data obtained from the different sensing devices. The combined sensor data may then also be processed and analysed — further.

Once the data has been processed analysed and combined by the system 420, an output, which may be understood as a sensor data output 430, may be provided. The output 430 may be such that it may be stored and then be accessed using data connectivity means. Optionally, there may be authentication requirements for accessing the data to ensure that only authenticated users are < allowed to access the stored output 430. Alternatively, or additionally, the output

N 430 may be transmitted to one or more pre-determined receiving devices. For 5 example, there may be a criterion that in case certain type of flock of birds is = detected, and it is detected moving towards the wind farm, a notification is — 30 transmitted to one or more pre-determined devices such that corresponding

E action, such as stopping one or more wind turbines, can be performed. — Additionally, or alternatively, in case weather conditions are determined such x that the wind will be more favourable in parts of the wind farm in which wind

N turbines are not currently active with their blades rotating, then those wind

N 35 — turbines may be activated by transmitting an indication to activate them and correspondingly, wind turbines in less favourable conditions that are currently activated with their blades rotating, can be stopped. In case the wind farm extends along tens or hundreds of kilometres, there may be significant differences in terms of local wind conditions. By obtaining sensor data from sensing devices associated with individual wind turbines, the variation in local wind conditions may be detected and provided as an output 430.

Thus, based on the sensor data analysis, an indication, in accordance with pre-determined rules, may be transmitted to a receiving device. The receiving device may be for example a monitoring device located remotely, a device with an application that is configured to receive such indications, and/or one or more of the sensing devices with their respective associated wind turbines.

The sensing devices may thus receive indications such as activating their sensing functionality, for example in case it is determined that the sensor data the sensing device is configured to produce could provide helpful data. Alternatively, the sensing device may receive an indication for deactivating transmission of sensor data it produces, as the sensor data is not currently required. Such a situation may occur for example if the associated wind turbine is to be activated, such that its blades will be rotating, and the blades would cause disturbance to the sensor data produced. Correspondingly, in case the associated wind turbine is to be deactivated, such that its blades will no longer be rotating, the sensing device may receive an indication to provide sensor data again.

Figure 5 illustrates an exemplary embodiment of a computing device 500, which may be or may be comprised in a computing device such as those comprised in a sensing device, in an edge computing system or computing system comprising one or more computing devices, such as a computing entity, for example a server. In this exemplary embodiment, there is at least one processor < 540, at least one memory 530, at least one connectivity unit 510 and at least one

N unit for receiving input and providing output 520. It is to be noted that the units 5 described here are logical units and thus the actual implementation may vary. The = at least one processor 540, at least one memory 530, at least one connectivity unit — 30 510 and at least one unit for receiving input and providing output 520, such as

E visual or audio output, may be connected to each other. — The at least one processor 540 may also be referred to as core, a central 3 processing unit (CPU), microprocessor or graphical processing unit (GPU). A

N processor may be understood as an integrated circuit for performing calculations

N 35 according to instructions provided using computer code. The at least one memory 530 may comprise volatile and/or non-volatile memory. Thus, the at least one memory 530 may be understood to be one block of memory or a combination of different blocks of memory. The memory may be for storing different types of data.

The at least one memory 530 stores also computer program instructions, for example in the form of an application and/or an operating system. The at least one memory 530 provides computer program instructions to the at least one processor 540 for executing and the at least one processor 540 may then be configured to store data into the atleast one memory 530. Some examples of memory are random access memories (RAMs), such as static RAM (SRAM) and dynamic RAM (DRAM), read-only memory (ROM), flash memories, optical discs, and magnetic computer — storage devices, such as hard disk drives. The input and output unit 520 may allow user input, such as pressing a button, touch input and/or voice input, to be received by the device 500 and output such as audio, haptic or visual output to be provided to a user. The connectivity unit 510 allows connection to be formed between the device 500 and another device. The connectivity unit may allow wireless and/or wired connections to be formed between the device 500 and other devices.

Examples of connection types that may be supported by the connectivity unit 810 are cellular communication -based connections, local area networks, Bluetooth- connections, Wi-Fi connections, etc.

The present disclosure has been described above with reference to the exemplary embodiments. However, a person skilled in the art will understand there may be embodiments that vary from the example embodiments discussed above within the scope of the claims. Thus, skilled person will understand that the exemplary embodiments described above may, but are not required to, be combined with each other and/or other exemplary embodiments in various manners. i

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Claims

1. A method for obtaining sensor data, the method comprising: obtaining, using a first sensing device, a first sensor data from one or more sensors of the first sensing device, wherein the first sensing device is associated with a first wind turbine located at a first location; receiving the obtained first sensor data by a central computing entity; receiving, by the central computing entity, a second sensor data obtained by a second sensing device located at a second location, the second location being different than the first location; and combining, by the central computing entity, the first sensor data and the second sensor data.

2. A method according to claim 2, wherein the first sensing device is mounted on the first wind turbine.

3. Amethod according to claim 1 or 2, wherein first sensor data and the second sensor data are overlap at least partly.

4. A method according to any previous claim, wherein receiving the — first sensor data comprises receiving the first sensor data from an edge computing entity, and the edge computing entity is configured to receive the first sensor data from the first sensing device.

5. A method according to any previous claim, wherein the blades of the — first wind turbine are stationary when the first sensing device produces the first x sensor data. -

+ 6. A method according to any previous claim, wherein the method = further comprises: = 30 receiving, by the central computing entity, a third sensor data, wherein E the third sensor data is obtained using a third sensing device, and wherein the 5 third sensing device is associated with a second wind turbine; and 5 combining, by the central computing entity, the third sensor data with N the first and the second sensor data. N 35

7. A method according to claim 6, wherein the first wind turbine and the second wind turbine are located in a first wind farm.

8. A method according to claim 7, wherein the first wind turbine is located in a first line of wind turbines in the first wind farm, and the second wind turbine is located at a second line of wind turbines in the first wind farm.

9. A method according to claim 6, wherein the first wind turbine is located in the first wind farm and the second wind turbine is located at a second wind farm.

10. A method according to any of claims 6 to 9, wherein the third sensing device is mounted on the second wind turbine.

11. A method according to any previous claim, wherein the central computing entity is configured to perform data analysis to the received sensor data comprising at least the first sensor data, and the data analysis comprises performing object recognition.

12. A method according to claim 11, wherein the method further comprises transmitting, to a receiving device, an indication based on a result of the object recognition.

13. A method according to any previous claim, wherein the first sensing device is one of the following: a first radar, a lidar, or a camera; and wherein < the second sensing device is a second radar. S +

14. A system comprising a first wind turbine comprised in a first wind = farm, the first wind turbine being associated with a first sensing device, and a — 30 second sensing device located away from the first wind farm, and a central E computing entity, wherein the system further comprises means for performing a = method according to any of claims 1 to 13. 5 N

15. A computer program product comprising instructions, which, N 35 when executed by a system comprising one or more computing devices, cause the system to perform a method according to any of claims 1 to 13.