The new technology also provides field access to the data through mobile geospatial application (GIS) solutions. These applications enable the TVM team to better manage system integrity and reliability over a multiyear horizon and enhance the utility’s integrated vegetation management (IVM) strategy, while also balancing the needs of Duke Energy and property owners.
Reasons For Change
Business Transformation
While the strategic focus provided directional guidance, the organizational alignment changes needed to support the new strategy included the creation of a central TVM Strategy and Support (TVM SAS) organization. TVM SAS, which combined personnel from the TVM regions and the utility’s vegetation governance organization, was created to develop an enterprise approach for TVM programs and implement innovative and cost-effective technology solutions. Prior to the creation of this group, TVM processes, procedures, and operational practices varied across the regions and technological innovation was a low priority.
Technology Initiatives
Cutting Down Risk
- Sheer volume of data and the ability to manage it
- Solutions not being designed to fully meet use-case needs
- Limited capability to produce concise actionable deliverables for execution.
- Provide tree-canopy polygons
- Analyze threats under all rated electrical operating conditions
- Document reactive work threats
- Predict vegetation threats over a six-year to eight-year period
- Provide capability to manage and transfer large datasets
- Develop predictive reliability-risk analytics
- Provide scenario-planning capabilities for annual work planning
- Create optimized annual work plans
- Create an application that supports an end-to-end approach for the corridor (planned), floor and reactive management programs
- Support work unit and should-cost predictions
- Support multiyear work planning
- Provide actionable deliverables for execution (that is, predicted work units per stem at the tree-canopy polygon level)
- Support field mobile access
- Support assignment of work for execution
- Provide completed-work reporting capability
- Meet business-case objectives.
Establishing the RSP
Once a remote sensing data source was determined and the method for managing threats was established, requirements related to RSP data capture and processing were defined. The capture requirements included average LiDAR points per square meter, survey absolute locational accuracies, relative accuracies for the LiDAR point cloud, ground control points, LiDAR point cloud feature coding, minimum corridor processing delivery widths, coordinate systems and many other factors related to imagery. The processing requirements were established to include the creation of tree-canopy polygons, tree-canopy tops representing the highest point within the canopy polygon and vegetation threat polygons. The deliverable requirements supported the ability to associate vegetation threats at the tree-canopy polygon level.The final step of the RSP was to establish requirements for vegetation condition and threat analysis. With an expectation that all rated electrical operating conditions of the conductor be considered (which meets FAC-003 requirements), vegetation threat modeling was built around “max sag,” “as-flown” and “design blowout” conductor positions. Threats from vegetation, using one or more of the conductor positions, were to be predicted for the next six-year to eight-year period and categorized as “grow-in,” “blowing together” and/or “fall-in” threats.