A LiDAR and GIS-Centric Data Fusion and Risk-Based Prioritization Approach
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Now that the NERC alert mandates are being addressed, the transmission and distribution sectors of the power industry have a wealth of new information that can be leveraged to enhance their business operations. Most power companies are using LiDAR, imagery and GPS data to collect detailed information about their infrastructure. This information can be leveraged to develop a GIS-centric Asset Management database. So, what can an agency do to leverage this information, especially when it can come from multiple vendors, sensors and be of different vintages?
First, it is important to find the common denominator between all of the data the agency is working with. Utility data typically uses a Structure ID or Span ID that can be used to tie all of this information together from a database perspective. The location of the Structure or Span can also be used to tie information together geographically from a mapping perspective as well as temporally for those agencies collecting information annually or as part of a particular inspection time series.
Next, the agency can visualize all of this information spatially, utilizing a GIS so that spatial patterns can be observed. Typical spreadsheet-based deliverables are missing the spatial relationships that can be used to develop better maintenance and operation plans by observing how assets interact with one another. This spatial perspective adds another valuable dimension to help agencies prioritize where to spend their limited resources.
Finally, a Risk-Based prioritization model can then be developed to help the agency decide where to spend their limited funding resources. The assets that pose the highest risk score based on the Probabilities of Failure and the Consequences of those Failures can be prioritized, thus limiting the risk to the agency based on these types of failures.
LiDAR Data Collection, Utility Asset Extraction, and Inspection Data Aggregation
LiDAR data can be captured from fixedwing aircraft or helicopter platforms, depending on the required resolution of the data. Most agencies are interested in capturing information about features that are located within the right-ofway of a powerline or its associated structures. These features are classified in the point cloud and then modeled using encroachment measurement criteria to identify potential hazards to the powerline infrastructure.
The LiDAR point cloud can be used to model the existing as-built structures, tops of towers, conductors, as well as the bare-earth ground model of the area. This information is then loaded into PLS-CADD software and modeled at a maximum load (sag) and maximum blowout conditions. Any LiDAR features that intersect with these "safe zone envelopes" are flagged as encroachments and will be highlighted in the PLS-CADD reports. These reports are exhaustive in terms of the amount of good information contained within them, but can be overwhelming to an agency when trying to figure out "where" to start focusing their time and resources on corrective actions.
Once all of this analysis has been performed, these encroachment features can be geospatially located and mapped for further analysis. For example, vegetation encroachments can be identified as either "grow-in" or "fall-in" potentials and these points are classified as such.
Vegetation Encroachment Management
GIS mapping provides the user the spatial context necessary to make informed Operations and Maintenance decisions. As an example, the location of vegetation encroachments is known and with a little manipulation, the volume and area of the vegetation can be determined very easily. This gives an agency the ability to control the costs associated with their vegetation management program. Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.
The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process. In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information. A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed. Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is.
Risk-Based Asset Prioritization of Work Activities
Once your agency has identified where the encroachment issues are, how do you design a plan of action that gives your agency the biggest bang for your buck? In other words, there may be a section of powerline that contains many different encroachment types– Vegetation, Building, Ground Clearance, etc. Another section of line may only have vegetation encroachments. The agency is most likely handling the corrective actions for these issues out of multiple departments and for good reason. Each type of encroachment brings its own set of design standards or engineering challenges to the table and all of these needs to be considered when designing a corrective action program for the facility.
One criterion that can be applied to this information is the concept of risk. Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific asset classes and asset types. The more critical the asset–the higher the priority it gets when determining an agency’s primary work focus. In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize their maintenance over less critical assets. By prioritizing using risk, an agency can take measures to minimize the risk that exists in its asset portfolio by fixing these pieces and parts first.
Risk models can be very complicated or very simple. It is dictated based on the information you wish to maintain moving forward. You can use multiple automated inputs to help ease the data management strain moving forward. For example, an agency is using their LiDAR information to calculate the risk to a facility based on the number of LiDAR points that have been identified as encroachments as well as their height above ground; the "higher" the point, the more risky it is to the facility. In other words, the higher the vegetation feature, the more risk it poses to the facility. Since LiDAR data is composed of 3D points, the densities of these points can be applied to the facility’s risk score and then used to help prioritize the facilities that need the most work immediately.
Developing a Project Matrix and Estimating Costs Using Budget Forecasting
Once the facilities have been prioritized using the risk concepts described above, the agency can then start planning for the actual work activities that will need to happen as part of their annual capital improvement planning activities. This can be achieved by using the risk scores to determine which facility needs to be worked on and how much it will cost to improve that facility.
First, the facility components can be modeled from the LiDAR point cloud. As a simple example, we can imagine a distribution facility composed of a wood pole, conductors, cross-arm, guy wires and associated hardware. Each one of these facility components has a cost component associated with it based on the materials used and the characteristics of how it was constructed. The cost of materials can then be applied to each component and an overall facility cost can then be determined for the asset.
Once the facility templates are constructed, the agency can then start developing projects to improve or replace these facilities based on the results of the inspection information. This activity will allow the agency to determine the cost of a project in relation to their annual maintenance and operations budgets and then determine what they can improve for that fiscal years’ time frame.
All of this information can then be used to determine future years’ capital improvement plans based on funding availability and projected costs over time. This helps the agency to plan for future fiscal expenditures using a repeatable and defensible model that can be applied to different asset classes and asset types. In other words, multiple, disparate data sources can be fused to support the risk-based prioritization of work activities.
Jason Amadori originally hails from Rochester, NY and began his career as a biologist for the Reedy Creek Improvement District in Orlando, FL. His work with asset management, water quality sampling and NPDES permitting led him to employ the use of GIS and GPS for the creation of maps and databases.
A 2.015Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE