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Panel Taking action on data-driven insights for Vegetation Management
- 1. Guiding Tree Management Decisions – A data Agnostic Approach utilizing Remote Sensing 2022 Trees & Utilities Adam Helminiak – ATC Patrick Eisenhauer – E Source 9/21/2022
- 2. 2 © 2022 E Source | www.esource.com Speakers Adam Helminiak Consultant Maintenance Program Manager American Transmission Co. ahelminiak@atcllc.com Patrick Eisenhauer Engagement Manager E Source Data Science patrick_eisenhauer@esource.com
- 3. 3 © 2022 E Source | www.esource.com “With all that could be done, how much should we do?” Leveraging data for Tree Management
- 4. 4 © 2022 E Source | www.esource.com • Began operations in 2001 as the nation’s first multi-state, transmission only utility • Headquartered in Pewaukee, Wis. • Operate over 10,000 miles of lines and 580+ substations • Over 500 employees American Transmission Co.
- 5. 5 © 2022 E Source | www.esource.com Manage Risk Manage Cost Develop People ATC's Business Imperatives
- 6. 6 © 2022 E Source | www.esource.com Evolution of ATC’s VM Program (Adam)
- 7. 7 © 2022 E Source | www.esource.com Challenges and Desired Trajectory •Turning data into information to help drive decisions • Prioritize work • Fiscal responsibility • Demonstrate completion of right work at right time
- 8. 8 © 2022 E Source | www.esource.com ATC Data-driven Initiatives •Microsoft Power BI •LiDAR •ArcGIS Online • VM System • Survey123 • QuickCapture •Imagery based risk assessment and prioritization
- 9. 9 © 2022 E Source | www.esource.com ATC Initiative Objective Provide a decision support framework to optimize the cost-benefit of conducting VM work across the system • Assess tree risk and line criticality risk
- 10. 10 © 2022 E Source | www.esource.com Methods and Results
- 11. 11 © 2022 E Source | www.esource.com Tree Presence & Height Tree Risk Predictions Criticality Scoring Risk Prioritization Tree Analytics – A Multi-Model Process
- 12. 12 © 2022 E Source | www.esource.com Generate points at finite resolutions (e.g., 1m spacing) across the territory. Characterize each point (e.g., tree presence, LiDAR derived height, imagery, slope, terrain etc. Employ statistical methods to predict the LiDAR derived tree presence & heights. Analytical Design: Imagery-Based Models
- 13. 13 © 2022 E Source | www.esource.com Data Coverage Public LiDAR ATC LiDAR Imagery Data Data Coverage miles of conductor million points Public LiDAR ATC LiDAR Imagery Data Developing Training Dataset
- 14. 14 © 2022 E Source | www.esource.com Line/Conductor Data LiDAR (ATC & Public) Digital Elevation Model National Land Cover (Landsat and Sentinel) Aerial Imagery Landsat 8: Satellite Imagery Sentinel 2: Satellite Imagery 115+ Variables 10+ variables 10+ variables 10 variables 2 variables 4 variables 30+ variables 50+ variables Beyond a Single Data Source
- 15. © 2022 E Source | www.esource.com 15 Imagery tree predictions are highly correlated with LiDAR estimates. R2 = 0.74 (estimating tree height) AUC = 0.95 (estimating presence of trees) Tree presence and height models were predicted system wide. Imagery-Based Tree Model Results
- 16. 16 © 2022 E Source | www.esource.com Tree Presence & Height Tree Risk Predictions Criticality Scoring Risk Prioritization Tree Analytics – A Multi-Model Process
- 17. © 2022 E Source | www.esource.com 17 Tree-Outage Risk Analytics Model Estimation Data Capture Predictio n *Estimates the Probability and Counts of encountering a risk tree (i.e., tree posing risk to power reliability)
- 18. © 2022 E Source | www.esource.com 18 Overstrike Estimate Tree Presence & Height ROW Edge Effect Tree Stand Characteristics Terrain Probability Risk- Removal Tree (Point Resolution) Condition–Based Tree Risk
- 19. 19 © 2022 E Source | www.esource.com Circuit Resolution Tree Risk Tree presence, height and risk models predicted across 10,025 miles of ATC’s transmission system Point predictions were aggregated to provide outputs at circuit and span resolutions. Summary of Tree Model Outputs
- 20. © 2022 E Source | www.esource.com 20 Scaling-up Tree Model Predictions POINTS UP CLOSE POINTS Able to deliver tree analytical outputs at multiple resolutions: Tree presence, height, risk.
- 21. © 2022 E Source | www.esource.com 21 Scaling-up Tree Model Predictions POINTS CANOPIES
- 22. © 2022 E Source | www.esource.com 22 Scaling-up Tree Model Predictions POINTS CANOPIES SPANS
- 23. © 2022 E Source | www.esource.com 23 Scaling-up Tree Model Predictions POINTS CANOPIES SPANS SEGMENTS
- 24. 24 © 2022 E Source | www.esource.com The LiDAR and Imagery derived estimates of Conductor Overstrike and Tree Risk are strongly correlated at the Span and Circuit resolutions (>0.86) Resolution Sample Size LiDAR : Imagery Overstrike Correlation LiDAR : Imagery Tree Risk Correlation Span 847 0.87 0.86 Circuit Segments 21 0.92 0.94 LiDAR vs Imagery Model Performance
- 25. 25 © 2022 E Source | www.esource.com Strong Correlations at Span Resolution Imagery-Based Models have higher predictions relative to LiDAR (Conservative Risk Ranking)
- 26. 26 © 2022 E Source | www.esource.com Tree Presence & Height Tree Risk Predictions Criticality Scoring Risk Prioritization Tree Analytics – A Multi-Model Process
- 27. 27 © 2022 E Source | www.esource.com Criticality What is the impact if an outage were to occur? Grid Stability Voltage Black Start Ops Stability EHV Tie Line Generation Impact Generation Critical Path Customer Impact Radial Feed Critical Load or Area Load Risk Critical Customer Load at Risk ATC Critical Ranking
- 28. 28 © 2022 E Source | www.esource.com Tree Analytics – A Multi-Model Process Tree Presence & Height Tree Risk Predictions Criticality Scoring Risk Prioritization
- 29. 29 © 2022 E Source | www.esource.com Vegetation Prioritization Tree Outage Risk (Probability) Presence & Height Ecological Conditions Criticality (Impact) Generation Impact Customer Impact BES Impact Putting the puzzle together
- 30. 30 © 2022 E Source | www.esource.com Priority = Combination of Tree-Outage Risk and Criticality. Actioning against outage risk. Low Priority Medium Priority High Priority Criticality Tree-Outage Risk 0 1 1 0 1 3 2 Target Tree-Outage Risk Target Highest Criticality Target Both 1 2 3 Looking beyond Tree-Outage Risk
- 31. 31 © 2022 E Source | www.esource.com Conclusions and Next Steps
- 32. 32 © 2022 E Source | www.esource.com Insights and Conclusions Imagery-based and LiDAR-based estimates of tree conditions were highly correlated. Imagery provides an advantage due to reduced cost and speed to acquisition / delivery. Used a nested / scaled approach to provide decision support at each level of the organization. Think beyond: • A single data source e.g., LiDAR • Trees alone e.g., Risk and criticality.
- 33. 33 © 2022 E Source | www.esource.com Challenges Data: availability, integrity and source Extended timeline to refine regionally specific vegetation models
- 34. 34 © 2022 E Source | www.esource.com Successes E Source was adaptable based on ATC’s data availability Satellite imagery has limitations, but costs less than LiDAR Met aggressive timeline with limited internal resources
- 35. 35 © 2022 E Source | www.esource.com Applications Validation of annual work plan •AGO map with risk at circuit, segment, span Adapting to changing generation VM ground patrol awareness
- 36. 36 © 2022 E Source | www.esource.com Next steps Enhance analysis with LiDAR inputs as data becomes available •Ground truthing Update models to account for work performed and criticality adjustments
- 37. Questions
Editor's Notes
- Key Points 1 - With all that could be done, how much should we do? Key Points 2 – To identify risk trees you need also to be able to identify good tree. Don’t become paralyzed with information, analysis paralysis, at the end of the day its about removing risky trees and avoiding outages. We are empowering utilities to become ““Guardians of their Galaxy” Key Point 3 – Its about creating an approach that is a flexible solution to allow ATC to evolve their program over time. Key Point 4 – We are looking to optimize, become more efficient, and recognize cost benefits.
- Privately owned by the approximately 25 utilities, municipalities and coops with divested assets.
- ATC CULTURE: ONE TEAM – connected by purpose, curiosity and positive energy ATC business imperatives align with our program direction ATC Business Imperatives Manage Risk – compliance, forced outages and storm response, safety, public relations Manage Budget – continue to find efficient and effective ways to manage Dev People – challenge and provide opportunities to succeed Explore technology, challenge the status quo, use data to inform decision making
- About 10 years ago ATC hired vegetation specific personnel to support the VM program That was really the start of our VM program journey which has evolved tremendously in the last 10 years We started like many programs – high reactive/unplanned work; clearing the easy to recognize lines in poor condition We now have 5 VM Specialists and a Manager of VM who oversee 6 contractors and about 150 contract employees We’ve cleared about 85-90% of our lines – We do choose to manage a more aggressive clearing strategy using IVM tools to create a compatible ROW We’ve made significant progress with clearing our ROW’s to specification but completing the right work at the right time is more challenging and requires more than head knowledge to prioritize more holistically at the system level Diversity in tree types, growth rates, easements, topography vary across the system and Our next step in program maturation is technology to enhance the program
- Most of us have the largest line item in the maintenance budget and when you have close over grown trees everywhere it’s an easy justification but we’ve found as the VM program matures technology has helped Prioritize work Fiscal responsibility Demonstrate we’re completing the right work at the right time – data driven decisions VM has a lot of data - but we really had limited ways to use this information Very manual manipulation of the data Technology can help us turn data into information
- How is ATC becoming data driven Today we’re focusing on our E Source initiative but keep in mind it’s not just a single technology solution and many times these initiatives feed each other Other examples of technology to enhance the program PowerBI – good news is we collected data for about 15 years – scheduling, costs, budget breakdowns (planned vs. unplanned) but we needed a tool to turn this data into something useful LiDAR initiative started last year and expanded to our FAC applicable lines serving uses of vegetation clearances as well as cycle optimization Modeling and predicting vegetation growth AGO Vegetation Management System for work planning and accessible in the field down to the crew level Survey123, Quick Capture This initiative with E Source We are a management company and we need to leverage our personnel along with industry professionals to help find what works best for us Leverage those working with other utilities – E Source happened to be the experts we leveraged with this specific initiative The technology needs to be the right fit and how you get there needs to be the right fit
- Key Point - Mapping tree presence system wide is critical to providing a decision framework and approach. Explain. Key Point - Analytical Design Step 1 – Cast points, at 1m spacing, across the right-of-way extent system wide. Key Point - Analytical Design Step 2 – Attribute points with relevant variables essentially creating a training dataset. Key Point – Analytical Design Step 3 – Use variables to predict tree presence and height at each point across the system. End Point – This is a hierarchical modeling approach, central to spatiotemporal modeling with two components (1) Tree Presence (2) Tree Height. Often multiple models are deployed across service territory, with different variables and multiple sources / types of imagery allowing for the best predictions in specific locations. Some sources of imagery might be better for height and some for presence. e.g. cloud cover and cornfields. Generate points at finite resolutions (e.g., 1m spacing) across the territory extent. Each point can be characterized by: Tree Presence Yes/No LiDAR derived height Satellite imagery Aerial Imagery Slope, elevation, terrain Infrastructure conditions … Employ statistical methods to predict the LiDAR derived tree presence and heights
- Why are we doing this. We don’t always have LiDAR everywhere. And LiDAR is expensive. Developed an imagery-based model using public and ATC captured LiDAR data to develop it system wide. LiDAR is expensive to procure, do you really need it everywhere? Key Points – Ecologically Specific Key Points – Using best available data Key Point – Fairly northern, had to be careful of imagery sources used during spring capture. Snow can impact model performance.
- Key Point - Utilizing best available data across the system. Candidate variables. We narrow these down via analytical works to get at the key model drivers. Best
- Not all Lidar is the same. If I was standing in front of a tree example. SO, how well are the models performing. Not perfect. Reminder that LiDAR is not perfect either. Goodness of fit metric for continuous variables. Goodness of fit metric for yes/no is AUC. Perform cross validations – Train Test Key Point - 35Million Points
- We have used generalized model risk. Don’t have enough outages to train on like we do on distribution. Data Capture - So, we setup a sampling design. Prediction – ACU < 0.80 This is how our outage risk analytics work. Crews - Identify Red Canopy Captured Why Sampling can be performed and would improve model performance.
- Risk Tree Predictions were estimated using point process models (32 million points) across ATC’s entire Transmission System (10,025 miles) Geo-spatial datafiles at multiple resolutions (point, span, circuit) are delivered to support ATC’s vegetation management and mitigation planning (as a risk prioritization tool)
- As you can see, there are tons of points generated. In this example, we display points with predicted heights (blue points are short, red points are tall) We need to take this immense amount of data and aggregate it to deliver actionable tree analytics
- One potential spatial aggregation method is tree canopy polygons containing the heights of detected trees In this example, white polygons represent short trees and red polygons represent tall trees
- Another potential spatial aggregation is at the span level, or each section of conductor from pole to pole. Here is an example spatial aggregation of the total number of trees per span Blue lines contain the least number of trees, red lines contain the most number of trees
- We can scale this up further and spatially aggregate tree presence and height at the segment level as well, or even broader spatial resolutions In this example, Blue segment sections have the least number of trees, and red segments contain the most trees
- Because we Key Point - Imagery provides a great advantage due cost and speed to acquisition / delivery Key Point - Thinking beyond tree presence and heights. IE Risk and criticality. Key Point – Nested approach provides operational power and each level of the organization.
- Expert Opinion based scoring system Score was fit 0-1
- How do we tie this Tree Risk – What is the probability of a tree caused outage. Criticality – Given that we are going to have an outage, what is the impact
- Opener Slide Number 2 - Tell the story to set the stage. 1 - High tree outage risk warrants an investigation. 2 - High Line Criticality warrants an investigation. 3 – Bad Apples This can be done at multiple resolutions including, polygons, spans, circuits,
- Key Point - Imagery provides a great advantage due cost and speed to acquisition / delivery Key Point - Thinking beyond tree presence and heights. IE Risk and criticality. Key Point – Nested approach provides operational power and each level of the organization. Imagery-based and LiDAR-based estimates of tree conditions were highly correlated. Imagery provides an advantage due to reduced cost and speed to acquisition / delivery. Think beyond: A single data source. e.g., LiDAR Trees alone e.g., Risk and criticality. Used a nested / scaled approach to provide decision support at each level of the organization.
- Initially a challenge but this initiative provided opportunities to improve Forced ATC to look at appropriate data sources – line load, sensitive customers, etc Data sharing permissions and security In some cases there were multiple data sources which provided an opportunity corporately to identify appropriate data sources We were missing conductor location and had to make assumptions Ecologically specific tree models were developed using additional data such as time of year – snow, agriculture It took more time but more data was used to overcome this
- Cost LiDAR can be 30x cost – this won’t be used as a compliance tool! E Source adaptable – it wasn’t that we had to have X, Y and Z to make it work – they adapted to what we had available or were able to use what we had to use where it fit best Aligning the data and expectations with the deliverables You don’t necessarily need LiDAR data to predict tree risk We all have a lot going on within our routine work and yes it took work, but having the right partner can help minimize the work
- Prioritization used to validate our annual work plan Right work at the right time AGO map is a visual integrated with our AGO map tools for field use Our industry is changing rapidly with generation sources We’re seeing a significant amount of solar and wind Line criticality adjusts as system conditions changes – such as new generation added This helps tie VM objectives with system operation objectives We anticipate this data be used during VM patrols and work planning We can’t 360 degree inspect every tree across 10K miles but we can use the tool to help narrow those areas of higher risk
- We still have some work to do Supplement LiDAR with the models as it becomes available Ground truthing will help refine the accuracy of the models which was not in scope with this phase of the project Update models as data changes – work, outages, line risk Closing: There isn’t one single solution, and it needs to fit your program needs Don’t be afraid to start this journey because you don’t feel you have the right information, data or resources Many of these solutions area flexible, adaptable Start your technology journey by leveraging the experience in the industry – but start that journey