Utility_Lidar

Distribution Level LiDAR

In Uncategorized by David Ilgenfritz

We often get asked how UAVs can best be utilized in the Utility industry. It is a multi-faceted answer, but I believe there are specific use cases that aren’t being considered that may change the way Utilities operate.


For the sake of this article, we will not be touching on inspection imagery, thermal, or corona data. These applications are fairly straight-forward and in most cases are already being implemented by many Utilities. Further, with the deep learning capabilities of programs like Google’s AutoML Vision, visual inspections are becoming something that can be integrated at a very basic level by linemen crews and engineers on job walks. If you are looking for help integrating these UAV services into your current business, contact us here for help!

The focus of this article will be on utilizing UAV LiDAR for mapping distribution poles for the sake of analysis, pole loading calcs, GIS, and risk mitigation efforts. We believe this is a unique use case that has not been employed previously and could provide substantial benefits to many Utilities.

Background

To add context to our conversation, we will start with a broad overview of Utilities in general. According to an estimate in 2010, there are approximately 200,000 miles of high-voltage transmission lines in the United States.  That is a lot of transmission structures!  However, the same estimate claims that there are approximately 5.5 million miles of local distribution lines.  That is a staggering amount.   Not only are there millions of miles, but in many cases these lines were constructed in the 60’s and 70’s and are decades old. These wood poles, though stout, are reaching the end of their lifecycle.

With this many miles of line, it should come as no surprise that the current condition, location, and health of many of these lines is simply not known.  We see fires generated from falling conductors in California, wind events taking out power in rural areas, and outdated GIS and information data systems with inaccurate and incomplete data.

Further, in many joint use cases (when a telephone or internet company “shares space” with a utility pole) telecom companies have antiquated methods of gathering survey data and producing pole loading calcs. Typically, a crew is sent out with a hot stick to gather heights of conductors and clearances. Though passable, the supporting data is not near as robust as it could be for Utilities to ensure their poles are not overloaded.

All of this to say, data is king when it comes to determining asset health, and UAVs provide a tantalizing new method of data capture. UAV LiDAR can be classified and delivered in a .BAK that will accomodate for any engineers workflow. Planimetrics, Orthophotos, and Oblique images can also be integrated to give an accurate picture of field conditions.

In Practice

With the above context in mind, at Flight Evolved, we have worked with multiple Utilities to develop a methodology for applying UAV LiDAR to map out distribution lines for analysis in PLS CADD.  In many cases, this is a completely new workflow for engineers to prepare their pole loading calcs, line sags, etc. for distribution lines in PLS CADD (this is a common workflow for transmission.)  Many are used to ground survey, hot stick, or other sparse data sources being inputted into OCalc, SPIDA, or other software platforms.

The reasons we haven’t seen widespread adoption at the distribution level are sound. The data just hasn’t existed previously in an economically viable package, to make the endeavor worthwhile. We have heard the following from our clients.

  • Connector.

    PLS CADD

    PLS CADD is most efficient when quality LiDAR data is available. We just don’t have quality LiDAR of our Distribution network.

  • Connector.

    Insufficient LiDAR Coverage

    Manned LiDAR operations (helicopter/plane based) have historically had a very difficult time gathering dense data of small distribution lines. In particular, guy-wire anchor points, comm lines, and fiber lines are all very hard to pick up with conventional LiDAR.

  • Connector.

    Efficiency

    It is horribly inefficient and costly to mobilize helicopters or manned LiDAR for smaller distribution projects.


With those hurdles in mind, we set about solving each of the problems that Utilities were facing.  With a UAV we could be very cost efficient compared to ANY manned aviation based provider. Further, due to our lower acquisition heights, the higher density of our scanner, and the slower speeds we acquire data, our data was higher quality than any data previously received from a manned service provider.

Results

All of this sounds really great…but the results speak much louder than words.  In 2017, Flight Evolved conducted over 700+ UAV missions for our Utility clients. Many of these missions were completed in conjunction with various distribution related projects. The below data set is a trimmed version of a distribution line our team captured. This is not the full feature coded data set, however, our end product was delivered in full PLS CADD .BAK file.

Accuracy Report

Data Viewer


DENSITY: 80ppm
GLOBAL ACCURACY: 3cm
FLIGHT TIME: 12 Minutes

Conclusion

A combination of new LiDAR sensor technologies and the correct UAV to carry them has allowed us to partner with many of our clients to develop new use cases for UAV LiDAR in Utilities.

Naturally, producing usable data and accurate point clouds is not something that happens overnight. In evaluating your needs for accurate LiDAR you must ensure that you are asking people the right questions. At Flight Evolved we have diligently combed through every part of our workflow to ensure our Quality Control and Quality Assurance measures are consistently implemented, and the proper accuracies and densities are achieved. Further, the proper scanner is absolutely necessary to achieve the accuracy required for accurate PLS CADD analysis. For more information on the equipment we employ, check out our Technology page.

Have you employed UAV LiDAR before?  We would love to hear your lessons learned and best use cases!  Drop us a line below.