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How LiDAR Can Help Detect Change at a Community Micro Hydro Power Generation Site

Location

Congleton, UK

Scan time

25 Minutes

Size

100m x 100m Area

Scanned

Micro Hydro System

Industry

Surveying

How Using Micro Hydro Power Generation Produces Clean Energy

Discussions around climate change, and how we can lessen our environmental impact, have become increasingly more relevant in recent years. This has driven some companies and communities to explore different ways of producing renewable technologies, to reduce dependency on fossil fuels. One method for gathering renewable energy is by using Micro Hydro power generation.

Hydroelectric power generation relies on a constant water cycle. Nature is perpetually replenishing this, making it a good clean source of energy. This method of producing electricity using hydroelectric power generation is what the Congleton Hydro Project has set out to achieve.

Dane Valley Community Energy Ltd (DVCE), a non-profit community benefit society, developed and constructed the project. DVCE is a volunteer led organisation, run by a small team of volunteer directors, who have all worked within Engineering and Management Companies. Funded by the community of Congleton, the project aims to generate enough carbon-free electricity, using hydroelectric power generation, to power 60 homes within this local area.

Utilising an Archimedean Screw for Micro Hydro Power Generation

For this project, the team set up a Micro-Hydro System, including an Archimedean Screw. The hydroelectric energy is generated by the nearby Havannah Weir River. The energy is extracted by using the water flow to turn the Archimedean Screw, which is connected to an electrical generator.  Having constructed the whole system in just 12 months, DVCE were able to produce their first load of electricity in a relatively short space of time.

The area surrounding the Archimedean Screw required significant and extensive ground works, together with a substantial walkway, powerhouse, and piped water inlet system. Whilst designing the project, it became apparent that a core worry would be movement of the terrain, due to weather and time. As a result, they decided they needed to frequently monitor this surrounding area.

The Archimedean Screw has a life expectancy of 40 years, and the team hope to make returns on their investments in the next 20 years. In addition to generating clean energy, a core objective is to generate an annual surplus, which will fund the local community. It is therefore essential that any change in land stability does not impact the planned generation. The team decided that a monitoring system would help identify any movement so that timely corrective action could be taken.

The Micro Hydro Electric power generation site

Tracking Changes in the Land and Facilities Management

Via a family member (Dr Jonathan Owen), the team acquired a 3D handheld laser scanner, GeoSLAM’s ZEB Go. The handheld nature of the scanner will mean they can track land movement and vegetation rates over time. In addition, they can map the on-site building to help with facility management and storage.

GeoSLAM Connect’s Stop and Go Alignment can help the team align these scans, as it would give them a more accurate view of the exterior and interior areas together.

GeoSLAM technology is ideal for this type of work, due to the uneven terrain. The mobile device can map an area by simply walking around, whereas systems that require a more complicated setup would struggle to scan the area promptly.

The Benefits of GeoSLAMs Technology

The ZEB Go’s speed of capture enables DVCE to carry out scans of the 100m x 100m area surrounding the Micro-Hydro System in just 25 minutes. As the team are detecting change in the ground movements and vegetation, they can frequently scan the area to track any issues that may arise. A great way to document the area, as frequently as DVCE need.

The ZEB Go’s ease of use means the team would not need to be survey trained for the scan, unlike more complicated to use scanning hardware. Further, the ZEB Go’s capabilities save all the team from having to repeatedly return to the site, as just one individual is needed.

The team were impressed with the ease that the ZEB Go was used to survey the complete site, with no tripods being needed and no complicated set up.

Creating Accurate Georeferenced Point Clouds

Whilst capturing the data, the team laid down control points using a GNSS receiver. This allowed the team to georeference the data using GeoSLAM’s software. Now the surveying pins are in place, the team can simply georeference the data for each scan they conduct.

Implementing control points was important for DVCE as it allows for clear comparisons between multiple scans of the same area. Georeferenced data places the scan in the real world and makes the data even more accurate. This will benefit the team as they continue to scan the Micro-Hydro System’s surrounding areas in the future and detect any gradual change.

The ZEB Go delivered an accurate 3D replica of the area that continues to help DVCE in their project.  The versatility of the ZEB Go and resulting point cloud means the team can look into new ways to interpret the data – protecting this vital equipment for both the environment and local community.

Jonathon was the lucky winner of our ‘Win a ZEB Go Competition’ at GeoBusiness 2021.

View the PDF here

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    Referencing using control points

    What are control points?

    Control points are points within a given area that have known coordinates. They are a key tool in the geospatial industry and can be utilised in a variety of ways, including georeferencing point clouds and aligning aerial images to terrestrial data. By using control points, surveyors are able to accurately map larger areas and position overlapping surveys of an area together. They can also be used in non-geospatial industries, such as construction and mining, to show clear temporal comparisons between multiple surveys of the same area. This method of georeferencing is also referred to as adjust to control.

    Previously, checkerboards and spherical targets have been used as control markers – these items are captured in surveys and can be identified for georeferencing or aligning. The main drawback with these methods is that they rely heavily on human interpretation when processing, meaning that the processed datasets may be susceptible to an increased amount of error.

    When capturing handheld surveys, GeoSLAM systems are able to collect reference points. These can then be matched with known control points to reference scans and increase the level of accuracy.

    What makes GeoSLAM referencing different?

    • More accurate: GeoSLAM scanners are used with known control points and survey grade pins, rather than more traditional moveable targets. This reduces the margin of error within point clouds.
    • Save time: using known survey control points means there is no need to manually position individual targets before every scan. Data capture can then be repeated regularly, faster, easier and with no concerns that reference points are captured in different places each time.
    • Safer: in dangerous or inaccessible areas, targets are not required to be physically positioned on pre-defined control points prior to each scan. This reduces the time exposed to hazards and unsafe areas.

    Industries using control points

    Geospatial

    Geospatial

    Easily reference point clouds and produce reports highlighting accuracy values.

    Utilities

    Mining

    Regularly monitor site operations (e.g. stockpiles) and hazards.

    Security & Defence

    Construction

    Compare changes over time and map progress onto predetermined CAD/BIM models.

    Using control points with GeoSLAM

    Capture

    All GeoSLAM ZEB systems are able to capture reference points using the reference plate accessory. These reference points can simply be measured by remaining stationary for periods during a scan and will be recognised during the processing stage. Points can be captured from a horizontal or vertical position, depending on which ZEB system is used, making it easier than ever to georeference datasets.

    Process

    Using the Stop & Go Georeferencing workflow in GeoSLAM Connect, datasets can be automatically referenced through a rigid or non-rigid transformation.

    Rigid Transformation

    Scans are rotated and adjusted and reference points are matched to the known control points without changing the scale factor. A single transform is applied to every data point in the point cloud.

    Non-Rigid Transformation

    The scale factor of datasets is altered to suit the control points – every data point is moved to a new position; this means the relative positions of these points also changes. This method is better suited for poor SLAM environments.

    A clean georeferenced point cloud is produced using both methods. An accuracy report of the transformation is also generated and includes an RMS error value.

    Point clouds with endless possibilities

    Once georeferenced using control points, point clouds can be optimised further using leading third party software:

    • Comparisons with existing CAD/BIM models
    • Point cloud to point cloud registration showing changes over time within a given area
    • CAD/BIM model creation

    For more information about our third party partnerships, head to our integrations page.

    Case Study

    Mapping hazardous mines under intense time constraints

    Beck Engineering, an Australian mining engineering consultancy specialising in mining and rock mechanics analysis, needs to rapidly map mines under intense time constraints using versatile technology which is adaptable to any environment. GeoSLAM’s handheld mobile mapping solution was chosen as it is compact, portable and delivers a high level of accuracy. With GeoSLAM’s “go-anywhere” 3D technology in hand, Beck Engineering has been able to supply invaluable data regarding the direct effects of mining to better understand the implications of a deforming rock mass. Beck Engineering is now able to accurately measure the shape of an excavation or tunnel over time. As a result, tunnels are safer, better designed and more cost efficient.

    We have continued to use GeoSLAM products as they have proven to be affordable, lightweight and sufficiently robust devices for their application underground. GeoSLAM continues to produce a high-quality device that is at the forefront of practical mobile laser scanning devices.
    – Evan Jones, Senior Rock Mechanics Engineer at Beck

    Read the full case study

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      Iowa Department of Transport uses SLAM to create 3D models of salt stockpiles

      Industry

      Mining

      Scan time

      10 mins per
      scan

      Location

      Iowa,
      USA

      Size

      109
      stockpiles

      Scanned

      Salt
      stockpiles

      Words by Tina Greensfield | Iowa DOT

      At Iowa State DOT (Department of Transport) it is our job to make sure over 24,000 miles of road remains clear and safe to use in winter. We have 109 maintenance areas across the state where stockpiles of salt are kept for distribution. Each facility can each hold up to 1200 tonnes.

      Throughout winter salt is loaded onto trucks and spread on roads to stop the surface from freezing. Pay loads are measured in weight as salt is loaded onto spreading trucks and supplies are depleted. But as the salt is used, there is a clear discrepancy between the volume of salt in the shed and the paper records – it is not reliable to just look inside a half-empty shed and assess how much material remains.

       If volume of salt is too low or we don’t know how much is available, we may find ourselves forced to make snap decisions about redistribution which is both costly to the state and inconvenient to residents and businesses alike.

       We needed another solution and following a few severe winters where salt reserves around the country ran out, the Great Lakes froze and shipments were halted we were determined to invest in a reliable measuring process for managing stockpiles in future, which led us to a GeoSLAM volumes solution.

      “In terms of speed and accuracy, this was a real game changer for us!“

      Using the handheld SLAM device, we can produce a three-dimensional model of the stockpile in just a few minutes. We have never experienced this level of accuracy before and capturing data was as easy as surveying the site with the naked eye.

      The surface of the stockpile is very uneven with lumps on one side and big cliffs on the other where loaders have dug-out salt for spreading, in the past our ‘best guess’ used to involve looking at the stockpile against some markers on the walls of the shed which provided limited accuracy to say the least, so this was a real game changer for us.

      Data is then processed using GeoSLAM Hub and imported to the volumes software. As the granules vary in size, we apply a bulk density value as well as defining a floor and perimeter of each pile calculate the total volume of the stockpile in tonnes.

      From start to end, the entire process took around twenty minutes. We now have depot staff going out and scanning the stockpiles regularly. This new level of insight means we don’t have to worry about compromising road users’ safety across the state of Iowa as we always know what volume of salt we have available to use.

      Download the PDF here

      GeoSLAM Sample Data

      View and download data in our free point cloud viewer

      Here’s some helpful tips for the best viewing experience

      • If your internet connection allows, move the Point Budget slider to the maximum amount available to view all the points in the cloud.
      • Making the point size smaller using the Point Size slider makes the data easier to view and interpret.
      • In the tools section of the viewer, you can measure the distance and angles of features within the pointcloud.
      • Using the materials section of the viewer, you can use the Select Attributes dropdown to view by intensity, elevation and RGB (if point cloud is coloured).

      Villa

      Location: N/A
      ZEB Scanner: ZEB Go
      Scan time:       20 Minutes

      View data

      Would you like to see a specific dataset that’s not on this page? Contact [email protected]