Between the Poles

Toward automating scan-to-BIM for brownfield substations

With about 55,000 transmission substations in the U.S. and the average age of the transformers in these substations over 40 years old, many substations are going to be refurbished, retrofitted, extended or upgraded. Utilities are opting to create digital twins of their existing substations as part of this process to enable maintenance and upgrading activities to be performed more efficiently. For older substations records may consist in outdated 2D paper drawings or documents may be missing altogether.  New digital scanning technology together with machine learning is making it possible to semi-automate the process of creating intelligent BIM models from point clouds derived from laser scanning or photography.

Companies are recognizing the benefits of an integrated geospatial and building information modeling (BIM) approach to design and construction. I blogged about the winner of the Year in Infrastructure 2019 award who applied BIM processes and reality modeling on a brownfield substation expansion project. The integrated approach enabled the organization to cut design time, minimize site visits, reduce land requirement and material waste, and, most importantly, eliminate change orders during construction.

Now reality capture and deep learning have been incorporated in a substation design product to semi-automate the scan-to-BIM process for brownfield substations.

primtech is a CAD-based design tool for high voltage substations.  An important recent enhancement Optical Symbol Recognition (OSR) allows point clouds from laser scanning or photography to be imported and then to apply machine learning to semi-automate the process of creating an intelligent BIM model. 

The software includes an extensive library, which contains more than four thousand intelligent 2D and 3D electrical devices from different manufacturers, steel structures, foundations, and wires and tubes.  The library has been growing for over 15 years and includes new equipment and elements as well as for legacy assets.  Each symbol (device, steel or foundation, etc.) has a specific geometric signature in the database in addition to meta data, file links, connection points and defined object behaviors. Together these comprise smart objects that enables objects in point clouds to be recognized and converted into an intelligent substation information model. The resulting intelligent model provides the basis for the calculations required in the substation engineering process.

The OSR process consists of two phases; first conductors are created and then equipment is added. A wire can be created by simply selecting three points, first anchor point, maximum sag point and end anchor point. For tubes, two or more points can be selected. Wires and tubes are available in the library for different conductor types according to international standards.  After conductors has been created and placed, the OSR tool provides a list of compatible library symbols for each symbol type. It is up to the user to choose which symbol should be selected. The user can select the perfect matching symbol or another one in the list. A similar workflow applies to all the other symbols like support structures and foundation heads. The system then combines the symbols to create assemblies (electrical device, support structure, foundation head) and places them in the model. Once an assembly has been created, the OSR tool will recognize identical assemblies in the point cloud and propose it for those positions in the substation. The wires are automatically attached to the respective electrical connection points on the equipment upon the insertion of the assemblies.  After the OSR tool has examined all objects in the point cloud and they have been mapped to an assembly in the model, a logical and intelligent primtech BIM model has been created. The model can be used as a full-fledged template for extension or retrofit - to create parts lists and plans as well as to carry out calculations including phase checking, clearance calculation, lightning protection calculation, sag and short-circuit effects calculations.

The new OSR technology is not fully automatic but drastically improves efficiency and reliability of converting point clouds into intelligent 3D models.This is an important advance that could dramatically improve the process of scanning brownfield substations and generating  intelligent models by making the process more efficient and less error-prone.

M. Kokorus, F. Pizarro, W. Eyrich and S. Heuser, "From Optical Symbol Recognition (OSR) of Point Clouds to the Substation Information Model," 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, USA, pp. 1-4., 16-19 April, 2018

Read more about substation design

Chattanooga quantifies major benefits from smart grid investment

Chattanooga's power company Electric Power Board (EPB) is one of the most advanced with respect to adopting smart grid technology in North America. At HXGNLive in Las Vegas, Ken Jones reported quantified benefits that EPB has found from its investment in smart grid technology. These include 65% reduction in customer outages and 52% reduction in outage minutes and savings of $23 million in restoring outages after a recent severe storm.

About five years ago I first heard about the remarkable transformation of the power grid in the Chattanooga, Tennessee, a city with a population of about 180,000. With the help of the Department of Energy and other partners the local power company Electric Power Board (EPB) saw the smart grid as not only benefiting the utility and improving the quality of life of its cusomers, but also as a driver of economic development. Early on EPB decided to deploy one of the country’s highest capacity fiber-optic networks to improve power quality, reliability, customer service and energy efficiency. Chattanooga is one of the few municipalities anywhere with 10 Gbit/sec internet speeds for both urban and rural customers. Currently over 50% of EPB customers subscribe to the fiber service.  EPB has estimated that the incremental economic and social benefits of the high capacity backbone for Chattanooga lie between $865.3 million to $1.3 billion. Furthermore, somewhere between 2,832 and 5,228 jobs have been created linked to EPB's infrastructure investment.

In addition to the fiber backbone, EPB has installed 1400 automatic switches, an AMI management system, a distribution management system (DMS), and support for demand management (DM) and distributed energy (DRE).

At HxGNLive in Las Vegas Ken Jones of EPB presented some of the very impressive quantified benefits that EPB has realized as a result of these investments in fiber and smart grid technology.

Reliability

Reduction in customer outages	65%
Reduction in outage minutes	52%

Annual operating savings

Meter reading	$2.0 million
Field services	$0.7 million
Demand charges	$3.0 million

Environmental benefits (CO2 emissions reduction)

Reduced truck mileage	400 tons/year
Demand management kWh reduction	1000 tons/year
Power factor improvement	2000 tons/year

In addition EPB has developed a solar farm of 4,400 panels generating about 2 million kWh/year. Anyone can buy into this starting at just $5/month. Ken gave examples of local firms that completely offset their power usage with solar power.

One of the interesting steps that EPB has introduced to help customers monitor their power usage is a mobile app MyEPB. The app provides real-time energy usage and usage comparison by day, week, and month, real-time outage reporting for the EPB service territory, support for customers to report outages, and billing alerts, for example, notify me when my monthly bill reaches $50. To date there have been 30,000 downloads of MyEPB.

Progress of outage restoration after a recent storm showing areas automatically restored (violet) and manually restored (green)

An area where the investment in smart grid technology has realized a major savings is resilience. Ken presented a map showing the areas in the EPB service territory where power was automatically restored by the automated distribution switches after a recent severe summer storm. The outage and restoration statistics were equally impressive and showed that automation resulted in an estimated savings of $23 million for this storm.

	W/o automation	W/ automation	Difference	Improvement(%)
Customers with an outage	72,622	32,043	40,579	55.88%
Cost of all outages	$69.3 million	$46.1 million	$23.2 million	33.48 %
Outage minutes	16,986,240	12,059,524	4,926,716	29.00 %

One of the important steps that the EPB has taken with its partners is to implement a microgrid testbed at the Chattanooga airport comprising a solar farm, batteries and supporting infrastructure.  This is one of the few airports anywhere that is able function completely off the grid.

These quantified benefits provide tangible evidence of the benefits of smart grid technology deployment.  I would recommend that any power utility operating in a small to medium-sized city seriously consider the Chattanooga experience.

Automating transmission line inspections with BVLOS drones and machine learning

Transmission line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilence.  They are generally performed by manned helicopters or by a ground crew.  Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a powerline (fall ins) or trees that were encroaching on powerlines (grow ins).  These inspections are mandated by NERC in North America and are not optional.  Beyond visual line of sight (BVLOS) drones (UAVs) and software analytics using machine learning has made it possible to approach completely automating this expensive process.  Automating transmission line inspections for vegetation management using BVLOS drones not only saves money but also could improve the resiliency and reliability of the transmission grid.

Mapping transmission lines with BVLOS UAVs

At ILMF 2019 in a very interesting talk Paolo Guzzini of Delair described how BVLOS drones has been deployed to map transmission lines in remote areas of Iceland.  Delair conducted two surveys on behalf of EFLA Consulting Engineers, a general engineering and consulting company. EFLA needed to map existing power lines and survey an area where Landsnet, the main transmission system operator in Iceland, planned to build a new power line between two cities. Iceland's airspace regulations allows BVLOS UAVs to be used in areas that are not densely populated. Delair deployed fixed-wing drones to gather two types of data: photography and LiDAR.  The drones were programmed to fly a predetermined flight plan so the pilot had to only monitor the drone's progress and be available for emergencies.  Delair’s drones were able to capture comprehensive data along the planned route and deliver the processed results to the client the next day — all at a fraction of the cost of hiring a helicopter or ground crew.  UAVs with a range of 110-120 minutes flew for up to 10 km per flight. In one case the UAV flew a total of 70 km with a 21.4 megapixel photo camera to capture a corridor of 200 m width to an accuracy of 0.5 m vertical and 0.2 m horizontal accuracy. The actual vertical accuracy achieved was 0.15 m.  In a second mission a UAV flew at 100 m a corridor 10 km in length with a LiDAR camera to capture a corridor 200 m wide with the objective of vertical accuracy of 0.5 m and horizontal accuracy of 0.2 m. The actual accuracy achieved was 4 cm horizontally and 2 cm vertically. The advantage of using LiDAR is that it can achieve better accuracy in location especially in the vertical dimension. In addition the LiDAR approach was able to detect detect conductors and poles.  In both cases the range of the flights was limited by weather conditions, radio range (15 km), and weight of the camera payloads.

Reality capture for vegetation management of transmission lines with BVLOS UAVs

Periodic transmission line inspection is mandated for North American transmission lines by NERC.  Inspections are currently carried out by manned helicopter or ground crews which are expensive and for that reason not done more often than is required by NERC.  Full automation, which is possible with BVLOS UAVs and automated feature extraction of lines, pylons, vegetation and ground, would dramatically reduce the cost of inspection and enable more frequent flyovers.  The major benefits would be lower costs and a reduction in the number and length of outages resulting from vegetation transmission line interactions.

In the U.S. on July 15, 2016, President Obama signed the FAA Extension, Safety, and Security Act of 2016. The 2016 Act requires the FAA to establish a process to allow certain UAV operations related to utilities, pipelines, and oil and gas production to be conducted beyond the visual line of sight (BVLOS) of the operator and either in the daytime or nighttime.  As a result FAA rules permit beyond visual line of sight (BVLOS) UAVs through Part 107 waivers.  I blogged over a year ago about Xcel Energy which obtained one of the first three waiver to fly UAVs for beyond visual line of sight operations.   Paolo related that 32 of these waivers have been granted and that more than 20 companies are operating beyond BVLOS drones but still with some restrictions.  Paolo related that the FAA very recently began allowing BVLOS operation using radar to track the location of the UAV.  Paolo expects to see many long-distance UAV operations in the near future, making drones fully competitive against helicopters.

Over 20 countries have regulations that permit BVLOS UAV flights. In some areas of the world, Delair has even been able to control flights via a 3G data connection, allowing a drone to inspect up to 30 miles in a single flight.

Post-processing

Paolo told me that Delair's post processing software uses a combination of machine learning and other technologies to classify LiDAR point clouds with 85 to 90 % reliability in order to identify potential vegetation encroachments.  I have blogged about Enview and other companies that also offer software solutions for this problem.

Conclusion

Rapidly evolving regulation to permit BVLOS operations, Delair's successful deployment of BVLOS drones with radio or 3G communications and automated post-processing, often cloud-based, to identify potential vegetation encroachment indicate that we are on the cusp of being able to completely automate this type of transmission line inspection.  Using drones and automated feature extraction software not only dramatically reduces the cost of these inspections but makes possible more frequent inspections including on-demand - for example immediately after storms - which would impact positively the reliability and resilience of the transmission grid.

AR enables better grid damage assessments and improves quality of utility GIS

Maintaining accurate, up-to-date GIS data has been a challenge for utility and communications firms for decades.  Inefficient CAD/GIS workflows result in utility GIS quality degrading with time.  Now with business drivers such as implementing a smart grid, determining service availability and other applications making GIS quality a top priority at an increasing number of utilities and communications firms, new technical solutions are being applied to implement new business processes that raise the quality of utility GIS data over time.  These include CAD/GIS integration; integrated engineering design, GIS and enterprise services; extensive geospatial open source libraries  for viewing, editing and analyzing geospatial and other data; and reality capture often together with AI for inspections, distribution and transmission vegetation management, asset inventories and creating network data models. Another example of a technical advance that can contribute to improving GIS quality over time is a recent proof of concept by Duke Energy, Verizon and EPRI in which augmented reality (AR) was applied to improve the reliability of damage assessments after major events.  While successful in making damage assessments more reliable, Duke found an unexpected result that AR also improved the locational accuracy and description of assets in the GIS.

Broken business processes degrades GIS data quality

 Over the years the lack of CAD/GIS interoperability has cost communications and utility companies hundreds of millions of dollars trying to cleanup their GIS data and streamline their CAD+GIS workflows.  For many utilities and communications firms traditional GIS/CAD business processes, which have been the standard approach for maintaining utility GIS for the past few decades, has simply not been able to maintain cost-effective, real-time, high quality asset location and associated data.  Instead GIS data quality tends to degrade with time.

Challenge: improving the reliability of damage assessments

At Distributech 2019 Aleksandar Vukojevic of Duke Energy together with Paul Giers Duke Energy, Micah Tinklepaugh EPRI, Norm McCollough EPRI, and John Simmins (now Associate Provost for Research and Economic Development at Alfred University) reported on a proof of concept at Duke Energy the primary objective of which was to apply AR to improving the reliability and trust-worthiness of damage assessments after major disasters, but found an unexpected benefit in contributing to improving GIS data quality.

The objectives of the trial were to investigate the application of AR to reduce errors in identifying and characterizing missing and damaged equipment and as a result improve trust in the assessments by the work crews assigned to carry out the repairs required to restore power. It was envisaged that AR could improve damage assessments by visualizing before-storm images over post-storm damage thereby simplifying identifying missing or damaged assets. Currently field technicians visit the area of the outage and use paper and pencil to assess and record damage. Some of the challenges associated with this process is that field technicians, especially less younger, less experienced ones or office staff seconded to the field, may not be knowledgeable about the secondary power grid and in assessing damage to it. The result is missed and incorrect identification of missing or damages equipment. In addition paper processes are slow and error prone making ordering the required replacement equipment a major source of delay in restorating power.  The result is a lack of trust in damage assessments by field crews.

Proof of concept: Applying AR to improve reliability of damage assessments

The trial involved a safety helmet mounted RealWear HMT-1 Android computer with an eye-level display and microphone. It incorporated a GPS so that location was recorded automatically. The HMT-1 was connected via WiFi to router in a truck that connected via a Verizon wide area network to a cloud server.  The field engineer views the "before picture" based on the location data and is guided to align the high resolution, stabilized HMT-1 camera to match the same view to capture a corresponding "after picture". Voice commands and voice responses to damage assessment questions presented in the application are utilized for a fully hands-free user interface. As the engineer performs the work, data is immediately sent to the back office for evaluation and action. The images and data are used to create a list of equipment and inventory required to restore missing and damaged equipment.  The trial did not involve an actual outage but simulated a major outage.  17 different sites were involved in the exercise. 

Results of the proof of concept

Early results indicated that nearly 100% accuracy rate was achieved in damage assessments using AR.  This represents a 50 % improvement over the existing, paper-based process. This resulted in much greater trust by work crews in the damage assessments.  In addition financial benefits were estimated to be significant. The first 12 hours response spent manually recording and transferring assessment reports was sped up dramatically. It was estimated that for an outage affecting 250,000 customers over a four day period, the time to assess and restore power could be reduced by 12 hours with a cost savings of over $ 8 million. One of the big advantages is that training only took 10 minutes even for a worker with no damage assessment experience.  Assessments were delivered to the back office in near real-time, much faster than for paper reports and eliminating the "middle man" who transcribed paper reports into a computer based system reduced the number of errors.

Side benefit of using AR: Improved GIS data quality

While Duke assessed the proof-of-concept to be successful for damage assessment, they also reported an unexpected but important result of the proof of concept.  The process using AR for identifying assets helped improve the quality of utility GIS data compared to the existing paper-based process that could actually result in degrading the quality of the GIS.

In conclusion Duke was extremely happy with the proof of concept. Achieving 100% accuracy even for inexperienced assessors impressed the Duke Energy team and created much greater trust by the work crews in the assessment reports. But AR had an important side benefit - Duke found that using AR to accurately locate and identify assets had the potential to contribute to improving the reliability of asset data stored in the GIS.  This is another example of the application of technology which change business processes and result in improving the quality of GIS data over time.

Power utility identifies 60-70% productivity improvement by integrating engineering design, GIS and enterprise systems

In the last few years strong business drivers for improving power distribution design productivity including an aging and shrinking workforce, an accelerating transition to the smart grid and a rapidly changing utility business model have emerged which are motivating utilities to transform their century old business practices.  With new technologies utilities are finding that they can improve productivity by up to  60-70 % by integrating graphical design, engineering calculations such as flicker, voltage drop, guying, enforcement of engineering standards, and integration with enterprise systems to automate bill of materials generation and job estimating.

Distribution design in the power industry

Designing electric power distribution systems involves CAD drafting, engineering calculations such as sag, flicker, voltage drop, guying, wind loading, pulling tension and clearance and checking them against engineering standards to avoid under-engineering, using compatible units to compute jobs estimates, and comparing costs of different equipment to limit over-engineering.  In many utilities distribution design remains a laborious error-prone process with redundant data entry, back of the envelope engineering calculations and manual lookups.

Furthermore once a design is completed it undergoes a typically paper-based workflow (especially for major jobs) that is inefficient and error-prone.  The CAD design is printed for construction contractors to use in the field.  After construction is completed the paper "as-built" comes back from the field to records possibly with red-lines that indicate what was actually constructed.  The key metrics for this workflow are the as-built backlog and the accuracy of asset location in the utility GIS.  These metrics are important for utilities because they directly affect key statistics such as average outage duration (SAIDI) reported by utilities to regulators.  As examples: I remember a small utility in upstate New York where an engineer told me he spent two hours in the snow and darkness looking for a failed device and ultimately had to come back in the morning light to finally locate it.  A large telecom I worked with could not locate some assets to within two city blocks which not only added to the time locating equipment that required maintenance but reduced the reliability of availability of services calculations.  Fashioning AEC and geospatial data into an efficient data flow from planning through design and construction to operations and maintenance represents a challenge that remains a problem for utilities.  The result of this broken workflow is an out-of-date, unreliable GIS that inhibits field workers from optimal productivity and complicates reporting of operational performance.

Business drivers for improving productivity

Utilities are facing business drivers that are motivating them to improve the productivity of their business processes and improving data quality including locational accuracy of assets to provide a near real-time digital twin of their assets.  These drivers include an aging work force, the replacement of older workers by younger less experienced workers, a reliable near real-time GIS as the foundation for developing a smart grid, and a rapidly evolving utility business model that changes utility priorities.

With the new business models emerging in New York and other jurisdictions, utilities' focus is shifting from selling energy to maintaining a reliable and resilient grid which makes reducing the frequency and duration of outages more critical.

Benefits of integrating engineering design, GIS and enterprise systems

In the last decade or so technology advances have provided a solution for low distribution design productivity and out-of-date and unreliable utility GIS.  I have described the system developed by Southern California Edison (SCE) to integrate distribution and transmission graphical design (AutoCAD), utility GIS (Smallworld) and back office systems including ERP (SAP), customer service system, crew scheduling (Clicksoft), field operations (eMobile) and finance (SAP and PowerPlant).  One of the workflows SCE focussed on was compatible units (CUs). CUs represent the cost of equipment together with the cost of labour to install the equipment. When combined with a bill of materials (BOM), CUs allow the designer to generate job estimates. The compatible unit workflow involves many systems including design, crew scheduling, materials ordering, and fixed asset accounting. As an example, at SCE designers were required to manually list the bill of materials on the design drawing and reenter it, again manually, in the materials ordering system.  SCE's integrated system was successful in improving productivity by bringing consistency and efficiency to distribution and transmission design, eliminating manual lookup, enforcing engineering standards to reduce the risk of under-engineering and avoiding the costs of over-engineering, and reducing or eliminating redundant data entry. The integrated system enables experienced workers to work faster with fewer errors and younger workers to become productive sooner.  It sped up the design to GIS workflow and resulted in improved quality of the data in the utility GIS.

However, while the system implemented at SCE demonstrated the success of an integrated approach, it was a bespoke integration of design, enterprise GIS, ERP and other products and is beyond the reach of small and medium size utilities. 

Benefits of a productized integrated solution

At Distributech 2019 I linked up with Dennis Beck of Spatial Business Systems (SBS), which took over responsibility for future development, support and licensing of AutoCAD Utility Design (AUD) software in 2016. He explained that SBS has transformed the architecture of the SCE integrated solution into off-the-shelf products that enable it to be cost-effectively implemented at any size utility.  One of SBS's customers has determined in detail the productivity benefits of an integrated architecture with SBS components for their engineering design workflow.

Nashville Electric Service (NES) is a publicly owned, nonprofit company that serves more than 355,000 customers in a large service area in Tennessee.  As part of a major upgrade to their business processes NES benchmarked an integrated automated design, utility GIS and enterprise systems solution for two engineering design jobs using SBS components.  NES performed the two jobs four different ways and assiduously recorded the time required for each step of the design process.  This allowed them to estimate the quantitative benefits of different aspects of the integrated solution.  The major components of the NES solution architecture were

• Automated design -  AUD is an AutoCAD-based desktop application that provides a model-based approach to design and bill of material generation along with a comprehensive set of engineering tools, including flicker, voltage drop, guying, and pulling tension and clearance checking.  It supports both overhead and underground distribution design.

• GIS integration - Integrating graphical engineering design with the utility GIS helps reduce as-built backlogs which are a major contributor to out-of-date utility GIS.

• Enterprise integration - Integrating engineering design and utility GIS with the utility's work order system, material management and financial systems makes it possible to automatically enforce engineering standards, generate construction work orders, layouts, bills of materials, design scenarios and estimated costs.

NES calculated the benefits of each level of improvement as applied to overhead and underground residential distribution design.

	Overhead Time (mins)	Overhead Time saved (%)	Underground Time (mins)	Underground Time saved (%)
AutoCAD	173		81
Automated design (AUD)	145	16 %	63	22 %
AUD with GIS integration	132	8 %	50	14 %
AUD with GIS and enterprise integration	69	36 %	23	34 %
Total savings	104	60 %	58	70 %

The analysis revealed important benefits from model- and rule-based approaches to engineering design. It enabled behind-the-scene calculation of physical things like wire sag, flicker and voltage drop. An important contributor to productivity improvement was to completely avoid the need to lookup or manually find engineering and drafting standards. The largest benefits from a productivity perspective were found by integration with enterprise systems especially compatible units which enabled automated bill of materials generation and job estimation.

NES further broke the overall workflow into subtasks including drafting, annotation, job estimating and enforcing engineering standards and analyzed the productivity improvements for each of these subtasks.

Overhead residential design	AutoCAD	Automated design (AUD)	AUD with GIS integration	AUD with GIS and enterprise integration
Drafting	60 mins	95 mins	82 mins	61 mins
Annotation	36	36	36	8
Job estimating	24	14	14	0
Engineering standards	53	0	0	0

 

Underground residential design	AutoCAD	Automated design (AUD)	AUD with GIS integration	AUD with GIS and enterprise integration
Drafting	25 mins	31 mins	18 mins	18 mins
Annotation	22	22	22	5
Job estimating	24	10	10	0
Engineering standards	10	0	0	0

The analysis revealed that while an integrated approach to design did not significantly change the time required for actual graphical drafting, it dramatically reduced the time required for annotation, applying engineering standards, and job estimating (including BOM generation).  These improvements in productivity were derived primarily from eliminating redundant data entry and manual lookup.  Another significant point is that these timings only represent the time savings to engineering design – they do not include savings on re-entry of data into the GIS from as-builts.

Other benefits that NES did not quantify include avoiding the risks associated with under-engineering, reducing the costs of over-engineering, increased quality of the utility GIS by recording engineering designs directly in the GIS rather than re-entering it from paper as-builts, and enabling less experienced workers to come up to speed faster without increasing the risk of errors or significantly reduced productivity.

Improving productivity of distribution design by up to 60-70% is a huge benefit.  SBS is finding that other customers are reporting similar experiences, although they haven't documented their productivity gains as meticulously as NES has done. For utilities that are facing a significant portion of their workforce retiring, this improvement in productivity can free up experienced engineers and others to accelerate the transition to the smart grid and to adapt to a rapidly changing utility business model.

Automating vegetation management for electric power transmission lines using deep learning with LIDAR

With FAA rules permitting beyond visual line of sight (BVLOS) UAVs available through Part 107 waivers the cost of capturing the data required for vegetation management for power lines, whether transmission or distribution, could drop dramatically.  However, manual post processing of the imagery to identify areas of vegetation encroachment, grow-ins and fall-ins, remains an error prone, tedious and time consuming process.  I have come across several firms (for example EPRI, SiteSee, Trimble RealWorks) over the years that have been working towards automating the process for identifying vegetation encroachments including grow-ins and fall-ins, but I have not encountered a utility that is using this technology operationally.  At Distributech 2019 in New Orleans I had a chance to chat with a startup Enview, based in San Francisco,  who described to me how their software has been implemented  over 7,000 miles of transmission lines last year to automate the identification of vegetation encroachment from LiDAR point clouds.  Furthermore, they expect the total distance covered to double this year.

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation.  Vegetation encroachment initiated the massive North American northeastern outage of 2003.  It is so critical for utility operations that utilities even have a vice president responsible for vegetation management.  It can be costly for utilities to identify areas of high risk vegetation encroachment which typically are carried out by manned helicopters.  Scheduling and routing crews to prune the trees that require attention is often sub-optimal adding to the cost of vegetation management.  Optimizing these processes can produce significant benefits in the form of fewer outages and reduced costs. I have blogged about Energisa in Brazil who used satellite stereo imagery to prioritize and optimize tree pruning and ComEd's pilot project in Chicago that uses LiDAR imagery to not only prioritize and optimize but also to change the pricing structure for procuring distribution tree trimming.

Enview applies machine learning, computer vision, and geospatial big data analytics to highly reliable feature extraction from LiDAR point clouds to identify the exact location, height and extent of trees and then to determine proximity to power lines and other utility equipment.  Utility GIS data can be integrated and results visualized using colour-coding to identify the level of risk for each tree.  This enables the utility to prioritize their tree trimming and optimize contractor routing.  In environmentally sensitive areas the visualization can be used to inform discussions with the various stakeholders responsible for different land uses.

At Distributech I had a chance to chat with Krassimir Piperkov, COO at Enview, who described how this technology has been deployed on more than 7,000 miles of infrastructure last year, and is on track to be deployed on over twice that much this year. He gave a very recent example of annual vegetation management analytics performed on 230kV transmission lines in California. These lines run through suburban and environmentally-sensitive, heavily forested areas.

3D visualization of a 230kV span (blue lines) with potential grow-in (yellow) and fall-in (red) vegetation that falls within client-determined clearance boundaries.

LIDAR data was acquired by a third party through a helicopter-mounted Teledyne Optech LiDAR sensor package. Four band imagery was co-collected during the LIDAR survey and registered into an orthomosaic of RGB and NIR bands. The data collection generated almost 2GB per linear mile. The Enview data analytics engine processed this data to (i) first classify the massive amounts of LiDAR data and (ii) then automatically calculate potential grow-in and fall-in vegetation points with high speed and accuracy. Insights and data were delivered to the operator not only via GIS-compatible file format for incorporation into existing workflows but also via an intuitive web portal accessible across silos. As a result of the superior accuracy and speed, the client was able to confidently prioritize vegetation prescription work to the areas that need it most and do so much sooner than otherwise possible with traditional methods.

The results being achieved with Enview's technology suggest that we are on the cusp of the widespread adoption and application of deep learning technology to automate specific tasks. The combination of UAVs for data collection and machine learning for feature extraction dramatically reduces the cost of inspections for utilities. This would permit them to be done more frequently than with manned aircraft and would lead to improvement in the reliability of above ground electric power and communications networks.

LiDAR changes the cost structure for utility distribution vegetation management

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation.  Vegetation encroachment initiated the massive North American northeastern outage of 2003.  It is so critical for utility operations that utilities may have a vice president responsible for vegetation management.  It can be costly for utilities to identify areas of high risk vegetation encroachment.  Scheduling and routing crews to prune the trees that require attention is often sub-optimal adding to the cost of vegetation management.  Optimizing these processes can produce significant benefits in the form of fewer outages and reduced costs. I blogged previously about Energisa in Brazil who used satellite stereo imagery to prioritize and optimize tree pruning. At Distributech 2019 in New Orleans, Sainab Ninalowo and Dan Marron from ComEd described a pilot project in Chicago to use LiDAR imagery to not only prioritize and optimize but also to change the pricing structure for distribution tree trimming.

Vegetation SAIDI

Currently ComEd trims 25% of their service area every year so that their entire service area is covered every four years. In addition they perform reactive trimming - when they have a vegetation-related problem reported they dispatch a crew to resolve the problem. Statistics show that the number of vegetation-related outages (vegetation SAIDI) has been declining year over year. The contractors who do this work are currently paid by the circuit mile independent of tree density, so that a contractor working in a rural or suburban area with many trees is paid the same per mile as a contractor in an urban area with few trees.

Fall-ins: yellow doughnuts Grow-ins: orange within 0.5 ft, amber within 0.5-1.5 ft, light yellow within 1.5 to 5 ft

ComEd has had experience using LiDAR for vegetation management for its transmission lines and decided on conducting a pilot project to apply the technology to its distribution network. It chose circuits in four areas in its service territory with a range of tree densities ranging from rural to urban. ComEd put the capture of LiDAR data and post-processing of the point clouds out to competitive bid and selected two vendors. For the LiDAR imagery acquisition fixed wing aircraft were used. Post-processing involved combining the distribution network from their GIS with the vegetation identified in the point cloud imagery and modeling cylinders around the cables to reflect the tolerances for vegetation encroachment (grow-ins and fall-ins).

Using the volume of vegetation encroachment in the cylinders it was possible to calculate the volume of tree trimming required. This made it possible to procure tree trimming services with a different pricing model, based on per cubic foot instead of per circuit mile.  This change in procurement modus enables contractors to more accurately estimate labor and equipment costs and ComEd to prioritize their tree trimming based on the needs revealed by the data.

In addition to LiDAR cameras the aircraft were equipped with IR and hyperspectral sensors. The IR imagery made it possible to successfully identify weakened and unhealthy trees. The hyperspectral data was used to try to identify different tree species, but the results were not able to this reliably enough to recommend using this technology operationally.

The pilot has been deemed to be a success, although there are not yet direct cost comparison figures publicly available. In addition to the perceived benefits for vegetation management, ComEd has identified other significant benefits which can be derived from the LiDAR data. Based on the results of the pilot it is being recommended that LiDAR be phased in gradually for the entire network with the goal of conducting a LiDAR scan of the entire distribution network annually.

This is another example of how geospatial technology is fundamentally changing the utility industry, in this case how vegetation management services are procured.

Sharing real-time utility outage information

During natural and man-made emergencies, a major problem that emergency managers have to face is that, as Scott Sternfeld emphasized at the December Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, there is no shared public regional, state or national outage map in the U.S.  Emergency management personnel have to go to each utility one by one. Utilities may not have an online outage map, which means calling them.  The Outage Data Initiative is designed to change that by enabling utilities to share outage information in real time.  At Distributech 2019 Zac Canders of DataCapable and Mike Webber of PSEG, the largest power utility in New Jersey announced that PSEG would be using this approach to share outage information gleaned from social media with Comcast, a large communications company.

DataCapable has been mining social media and generating map visualizations using a GIS for some time.  By combing social media channels for specific terms that would signal an outage, DataCapable can notify utility staff including the location of the tweets.  The data can be immediately forwarded to a utility and presented on a mapping dashboard, showing the time and location of the each tweet found.  Experience has shown that with this method utilities can detect outages faster that by relying on traditional methods.  In 2018 DataCapable detected over 181,000 conversations relating to outages on Twitter alone.  Data Capable has provided this service to utility customers such as Seattle City Light and also offers a service that shows outages worldwide. 

Now there is an initiative underway to make it possible to share outage information.  Called the Outage Data Initiative and initiated by the White House several years ago, it is designed to use an XML-based exchange standards to enable outage information gleaned from various sources including social media to be shared.  It is a voluntary open standard for sharing power outage and restoration information. I puts public data, such as that gleaned from social media, in structured easy-to-use and machine readable format.

it is being used by Seattle City Light and four other utilities in the Puget Sound area.  At Distributech 2019 Zac Canders of DataCapable and Mike Webber of PSEG, the largest power utility in New Jersey announced that PSEG would be using this approach to share outage information with Comcast, a large communications company.

The big benefit is that is provides a cost-efficient foundation for utilities, emergency responders, and other first responders to maintain a real-time common operating picture of utility outages and restoration during a disaster.

DataCapable has been mining social media and generating map visualizations using a GIS for some time.  By combing social media channels for specific terms that would signal an outage, DataCapable can notify utility staff including the location of the tweets.  The data can be immediately forwarded to a utility and presented on a mapping dashboard, showing the time and location of the each tweet found.  Experience has shown that with this method utilities can detect outages faster that by relying on traditional methods.  In 2018 DataCapable detected over 181,000 conversations relating to outages on Twitter alone.  Data Capable has provided this service to utility customers such as Seattle City Light and also offers a service that shows outages worldwide. 

Now there is an initiative underway to make it possible to share outage information.  Called the Outage Data Initiative and initiated by the White House several years ago, it is designed to use an XML-based exchange standards to enable outage information gleaned from various sources including social media to be shared.  It is a voluntary open standard for sharing power outage and restoration information. I puts public data, such as that gleaned from social media, in structured easy-to-use and machine readable format.

it is being used by Seattle City Light and four other utilities in the Puget Sound area.  At Distributech 2019 Zac Canders of DataCapable and Mike Webber of PSEG, the largest power utility in New Jersey announced that PSEG would be using this approach to share outage information with Comcast, a large communications company.

The big benefit is that is provides a cost-efficient foundation for utilities, emergency responders, and other first responders to maintain a real-time common operating picture of utility outages and restoration during a disaster.

Machine learning cost-efficiently creates high quality utility GIS data for smart grid applications

Many utilities' GIS contains only a small part of what is required for managing a smart grid.  Often even the data in the GIS is too unreliable to use for smart grid applications.  For example, utility GISs may not include which customers are connected to which transformer, or if they do include this information it is incomplete or incorrect.  Many utility GISs cannot identify the phase(s) each customer is using and they may not include network topology - what is connected to what.  Other missing data may includes street lights, distributed energy generation such as rooftop PV panels, the type and condition of poles, joint use of poles, and encroachment.  To correct these problems typically requires a foot survey which is expensive.  For example, a pole inventory costs at least $20 per pole.  At the Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, John Simmins of EPRI described the research he is leading at EPRI to use modern technology including machine learning to reduce this cost to pennies per pole using available imagery such as Google Streetview.

I began working in the utility and telecom sector in 1993, specifically in developing software for records management (called network documentation outside of North America), and at that time the magnitude of the AEC+geospatial cultural mismatch was just beginning to be appreciated by utilities and telecoms. Planners tend to use GIS tools, engineers and designers CAD tools, construction folks in the field paper CAD drawings, and asset managers FM, GIS or integrated tools. Fashioning AEC and geospatial data into an efficient data flow from planning through design and construction to operations and maintenance represented a challenge that remains a problem for utilities.  For many utilities the backlog of as-builts and updates waiting to be entered into the GIS stretches into months, with the result that GIS data is permanently out of date inhibiting management and field staff from relying on this data operationally.  The Between The Poles blog is over ten years old and one of the persistent themes from its very beginning in 2006 was the challenge of integrating CAD and GIS data in a common workflow (some examples; 2006, 2007, 2008 ).

There are signs that modern technology is beginning to offer a solution to the problem of developing and maintaining an accurate digital model of electric power grids.

I wrote recently about a utility in western Pennsylvania that intends to implement an Advanced Distribution Systems ( ), but realized that to this requires a network data model. Duquesne Light is typical of many North American small to medium electric power utilities.  It does not have an outage management system or an electrical data model which means it is difficult to connect customers to transformers and to determine upstream devices when customers report power problems.  Currently Duquesne Light's circuit maps are AutoCAD drawings.  Duquesne Light is moving to modernize its grid and plans to deploy an Advanced Distribution Management Systems (ADMS) including outage management (OMS), DMS and DSCADA.  But the ADMS will require developing a complete distribution electrical data model and to that in the traditional way can be expensive.

To quickly, safely and cost efficiently develop a network data model, Duquesne Light looked at several alternatives including reality capture technology.  A pickup truck was equipped with with side mounted photo and IR cameras and LiDAR laser scanners. The reality capture survey could be completed rapidly and safely, with no boots on the pavement  Actual development of the data model was completed in the office without the necessity of going to the field for quality checks because the imagery provided all the data that was required to create and verify the data model.  Duquesne Light found several significant advantages of the reality capture approach.  The first was asset accuracy - both location and asset attributes.  In addition the captured imagery could also be used for other purposes.  For example, it could be used for a joint-use audit.

But this is still basically a manual process requiring a human being to scan images.  At the Energy Summit John Simmins described his research using neural network technology to automatically identify poles and attached equipment from available imagery such as Google Streetview.  Normally applying neural networks requires large quantities of training data.  To reduce this requirement, John has evaluated "biologically inspired", forward pass, neural networks.  In this approach the algorithm first looks (foveates) for vertical things that have the characteristics of a pole, and then for the characteristics of equipment attached to the pole, for example, cross members, street lights, transformers and large cables.  From this analysis is possible to determine pole location, height and width, cross member position on the pole and projected length, street light support strut position and length, position of transformer and large cable connection locations.  The ultimate goal of the research is in addition to automatically detecting each element of equipment to identify the type, size and manufacturer of each piece.

This is not a simple problem and there is no silver bullet.  Using a standard neural network approach requires large volumes of training data.  For poles it requires images of poles with different types of damage under different lighting conditions and in different types of weather.  An Australian company SiteSee claims to be able to do this for communications towers and transmission pylons - identifying the type and manufacturer of each antenna or insulator on a tower or pylon and comparing it to the manufacturer's specification to detect signs of corrosion and other types of damage.  When what you are looking for has a distinctive geometry that differentiates it from the surroundings, a biologically inspired approach may provide a way of identifying equipment without large volumes of training data.  Another area where several different approaches have been applied to automate feature extraction is electric power transmission lines for vegetation management.

Creating a digital twin of an electric grid is essential for developing and maintaining a smart grid.  Utilities have been wrestling with this challenge for decades and in many cases their GIS data is still not much closer to what is required.  If it would be possible to use the technology that John Simmins is investigating or other technology to reduce the cost of improving the quality of their GIS data, it would accelerate the adoption of smart grid technology at many utilities.  In his presentation at the Energy Summit Jared Green described his research in using smart meter and AMI network data to map secondaries from the transformer to the home, identify sources of unmetered load, and use impedance data from smart devices to correct utility GIS data.  Given the history of the challenge to develop and maintain accurate GIS data at many utilities, these approaches represent a beacon of hope which may enable utilities to cost effectively develop a digital twin of their networks that is an essential prerequisite for a  smart grid.

Pilot for sharing location of underground utilities underway in UK

Every construction project involves devoting significant efforts to locating underground utilities prior to and during construction. But this information is rarely shared and the location of underground infrastructure is recaptured over and over again.  At the Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, Carsten Rönsdorf of the Ordnance Survey (OS) described UK initiatives supported by the OS to address the challenge of sharing information about the location underground infrastructure.   In particular, he described a pilot that has gone live in Sunderland (Newcastle on Tyne) in North East England that includes almost full water, gas, electricity and a great deal of telco data.

North East Underground Infrastructure Hub (NEUIH) is the overall initiative name for OS's and Northumbrian Water's underground work in the North East of England. The first phase of this project was a ‘Common Infrastructure Map’ covering a few small sample areas around Newcastle.  This combined map is the first step towards creating the NEUIH.  The current phase of the OS initiative is called ‘Sunderland sand box’ and implements a dataset that contains almost full water, gas, electricity and a great deal of telco data for the city of Sunderland.

In July of last year the OS and Northumbrian Water undertook to create a map of underground infrastructure for Newcastle on Tyne at the Northumbrian Water Group Innovation Festival.  The objective was to compile in collaboration with utilities, local authorities and partners a combined infrastructure map for Newcastle and its surrounding area including water, wastewater, gas, electricity, telecoms and other underground services. The project benefited from a spirit of cooperation - all local utilities and telecoms in Newcastle were happy to share their data and had vector data available to contribute.  The result was a database where each utility was steward of its own data which was shared through a hub. Interoperability was demonstrated with three different GIS systems consuming the combined underground data. The data could be ingested into GIS systems with internet access, regardless of format.    As part of this experiment in creating a shared map of underground utilities, contractors were brought in to actually dig a hole so that participants could experience first had what is involved in excavating to determine the location of buried utility assets.  The OS created a web interface to enable excavation teams in the field to access the data relevant to them via a mobile device.  By the end of the week it was possible to demonstrate a working system that allowed excavators to query the map of underground utilities and use this information during excavation and construction.

At the innovation festival a data sharing agreement was developed that all parties agreed to – Northumbrian Water, Northern Gas Networks, Northern Power Grid, Openreach, Durham County Council, Newcastle and Sunderland City Councils, Cranfield Soil and Agrifood Institute, British Geological Survey and Ordnance Survey. Access to different data layers was made possible through an API and desktop and mobile device front ends were designed that provided quick access to integrated underground and above ground data to planners and field engineers .

The NEUIH is expected to reduce the need for potholing - it has been estimated that the total cost of a pothole is on the order of $30,000.  It will reduce customer disruption by minimizing street works duration with utilities working together more efficiently.  Northumbrian Water Group has estimated savings of approximately £1m from a fully integrated system. These savings will come from a combination of reduced back office effort to produce safe working maps and plans, reduced costs associated with utility strikes and avoidance of abandoned jobs due to lack of information.

Next steps include a harmonized data model (such as the MUDDI model under development by the OGC).  The system will also support a feedback loop where the excavator in the field can update location information to correct errors for underground utilities that are encountered during excavation and construction.  Such a mechanism would be expected to lead to improved quality of location information over time.  The system will also make it possible to upload supplementary evidence such as open trench photos.

An important issue for any shared dataset of underground utilities is liability.  In the UK, as in North America, the liability for any damage to and resulting from underground infrastructure is generally the contractor's who is required to employ safe excavation practices.  In contrast Carsten reported that in Belgium where the KLIP system for providing location information about underground infrastructure has been in place for a number of years, legislation has been passed that shifts the liability to the utility owners.  In France liability is shared between the contractor and utility owners.

This is a remarkable achievement, thanks in large measure to the high level of cooperation among the local utilities and telecoms.  It will be interesting to see the quantified benefits realized by the shared underground infrastructure database.  Another important metric to monitor is how well the database is maintained.  One of the most important metrics is the backlogs of as-builts and updates.  The longer the queue of backlogs, the less people will rely on the information in the database.