Harnessing GIS and Imagery for Power Transmission Inspection. ESRI European Users Conference October 15, 2015

Transcription

1 Harnessing GIS and Imagery for Power Transmission Inspection ESRI European Users Conference October 15, 2015

2 About Us Airborne/Threod Designer, manufacturer & service provider for multi-rotor & fixed wing UAV products Deliver best in class solutions, services, and support Application specific solutions & services to meet customer needs directly InspecTools Asset risk strategies for wind, solar, utility assets UAVs & manned aircraft for aerial data acquisition Actionable and ongoing asset awareness

3 Data Collection Modes UAV MANNED AIRCRAFT GROUND SATELLITE

4 Data Collection Sensors Visual Imagery Stereo Imagery Multispectral / NDVI Live video LiDAR IR / Thermal

5 Pros & Cons UAVs MAN AIRCRAFT SATELLITE FOOT PATROL Cost LOW HIGH LOW LOW / MED Resolution HIGH LOW VERY LOW HIGH Sensor HIGH HIGH LOW LOW Options Speed FAST FAST AVERAGE SLOW Environment Dependent LOW (rain/high winds) HIGH (Cloud cover) HIGH (Cloud Cover) MED (rain, access) Regulatory Required (CAA) Required (CAA) None Required (property access)

6 Power Line: Data Acquisition Manned aircraft PhoDAR (photo-based 3D modeling) Data can locate assets & vegetation risks Data can serve as baseline for future analysis Develop terrain & canopy models Deliver actionable information

7 Data Analysis Near IR Imagery Vegetation Health NDVI Height Model Priority Locations

8 Risk Evaluation

9 Power Line: Deliverables Asset data Location of poles & conductors Tree threat data Trees identified & evaluated Vegetation data Source data Color IR orthoimagery Classified LAS files produced from orthoimagery Elevation model

10 Vegetation Analysis Case Study Client: The Threat: The Need: Current method: Elering (Estonian TSO) Annual overgrowth of brush and trees endangering lines 1) Accurately identify brush ground area and number of trees to cut down 2) To confirm the cutting work Inspectors walk the lines on foot, take photos and make notes

11 Why UAVs? WALK THE LINE Inaccurate / human error Limited as to distance can be covered in a year Ineffective in difficult to reach places No time for verification Lack of accurate data for ordering cutting works UAVs Quick to deploy Single operator deployment Higher accuracy Oblique imaging possible No geographical limitations Increased coverage means healthier network Save money on cutting works Cheaper than helicopter Higher resolution vs manned aircraft Can fly below cloud cover

12 Method 1. Work with client to understand specific needs, goals, etc. 2. Create flight plan and approve with client 3. Obtain required flight permissions 4. Acquire aerial imagery 5. Locate transmission / distribution assets 6. Develop terrain & canopy model 7. Identify high risk trees 8. Deliver actionable information to client to pass to contractors or internal cutting works department

13 Flight Plan & Permissions Waypoint mapping options Permission requested from Civil Aviation Authority and Tallinn Harbor and granted within 1 week Coordination with local flight towers during operation

14 Aerial Platform - EOS Hand Launch Parachute Landing

15 Photogrammetry Process Stereo Image Acquisition Stereo Image Triangulation

16 Terrain and Tree Canopy Model Ortho Image Visual Height Layer Client Specific Filter (height + distance from center) Visual Risk Layer

17 Risk Analytics Previous 1 2 Client historically wanted to look at small grid areas.5mx.5m with each grid classified by risk level Grids were then grouped into 4x4m cells turned into a dot matrix, and applied as a map layer 3 4 5

18 We offered another way Dot-matrix method yielded ~3-4x more trees than were actually present Airborne/InspecTools proposed analysis of tree crowns ~2x more accurate

19 Vegetation Analysis Report ID Risk_Level Type date Method class height zone x_coord y_coord 1dangerous tree 5/29/2015Photogramm {07,15} 7.940{15,20} dangerous tree 5/29/2015Photogramm {07,15} 7.190{05,10} emergency tree 5/29/2015Photogramm {07,15} 7.349{05,10} emergency tree 5/29/2015Photogramm {07,15} 11.47{10,15} emergency tree 5/29/2015Photogramm {15,20} 16.77{05,10} emergency tree 5/29/2015Photogramm {20,25} 20.90{05,10} emergency tree 5/29/2015Photogramm {15,20} 18.31{05,10} Client requested Excel showing: Type of threat Risk class Height Map zone Coordinates of threat

20 Asset Evaluation By UAV

21 Business Models Utilities perform processes internally Contract for data acquisition, analyze internally Contract for full service (data acq, analysis, results)

22 Thank You! Feedback and questions welcome Sean Whalen, CEO Chris Bley, CEO

23 APPENDIX

24 Solar Inspection: Overview Aerial inspection using IR & imaging sensors Analysis, reporting & storage of data Boots on the ground, follow-up inspection Defective module replacement tracking

25 Solar Inspection: Aerial Acquisition Manned & unmanned aircraft Specialized sensor payloads High productivity, accuracy & reliability Low site impact Modules per hour On Ground UAV Manned aircraft

26 Solar Inspection: Ground Acquisition Mobile map platform Structured data collection Off-line functionality Web integration Cleaning data compatible Integrated reporting

27 Solar Inspection: Deliverables Mapped locations of suspect modules Mobile map interface IR video & images

28 Blade Inspection: Mobile Data Blade data available in the field Structured data collection Commissioning, on-ground inspection, repair Procedural guidance Access to technical documents

29 Blade Inspection: Deliverables

30 Blade Inspection: Aerial Imaging High resolution High resolution images images Ideal vantage Ideal point vantage point Increased Increased safety safety Fast & efficient Fast & efficient Automatable Automatable

31 Blade Inspection: Overview Purpose-built UAV: ITX8 Find defects at specific locations on blades Reports generated from database Cradle to grave condition data Identify serial defects & under performance

32 Our Solutions Data acquisition systems using: Manned & unmanned aircraft Mobile apps for field use Cloud-based data storage & analysis Geo-centric data reporting Enterprise system integration

33 Problems We Address Asset condition often poorly known Current inspection options not economical Inspection & maintenance not performed Asset managers need better software Owners forfeit benefit of warranties

34 Why Photogrammetry? Complements periodic, required LiDAR inspection 50-75% less expensive than LiDAR Faster, less expensive analysis Yields vegetation health & species data Suitable for transmission & distribution Combines well with mobile LiDAR

35 The EOS 3.2m wing 1.6m length Weight: 5.5kg Flight time: up to 2hr km/hr Stabilized two-axis camera gimbal Camera options Sony A5100 EO camera with 36x zoom IR Camera 640x470 resolution Other payloads available Geo-location, geo-tracking, video tracking *Longer flight duration and faster flight speed models are available

36 Color IR Imagery & NDVI (optional) Stereo 4-Band Images Includes NIR Typically 6 or 12cm Yields point clouds Yields color images Tree health and species ID used as factor in risk calculation * Was not performed in this case study but optional upon request

37 Client Needs 1. Oblique imaging of towers and substations 1. GSD 2.5cm / pixel 2. Towers captured as full towers (exposing legs and isolators in single photo 3. Geo referenced photos 2. GSD of less than 5cm (actual was 2.5cm / pixel) 3. 90m minimum ROW corridor width for 330kV lines and 60m for 110kV lines 4. Minimum overlap of 60% - 90% of features within 20cm of true geolocation and no area more than 30cm of true geodetic location. 5. Brush and tree classification according to Client custom designed matrix relating distance from line to height of identified object (tree or brush) 6. Outputs 1. Digital ortho-photos (lossless JPEG2000 / TIFF format) 2. Orthomosaic 3. Oblique images 4. ESRI compatible data shape file outputs 5. Colored polygon area to represent brush and colored individual points for trees (color according to riskiness class

38 Summary HELICOPTER LIDAR UAV PHOTOGRAMMETRY FOOT PATROL COST HIGH LOW LOW / MED Vegetation analysis quality GOOD GOOD POOR Data depth/density GOOD FAIR POOR Speed of deployment and data analysis Requires NIR for Veg. Analysis Weather/Terrain Sensitive (i.e. clouds & marsh land) SLOW FAST SLOW YES NO NO YES NO YES

39 Future Value of Data Asset location data can be refined, tested, reused Terrain data can be refined for more accuracy Risk tree data can form a watch database Mobile LiDAR data contains pole loading info Mobile LiDAR & imagery is viable patrol alternative All data delivered in standard ESRI formats

40 Thank you! Contact Information: Chris Bley, InspecTools 2823 Mission St., Santa Cruz, CA 95060, USA (831) Sean Whalen, Airborne Tallinn, Estonia Phone