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and updates waiting to be entered into the GIS stretches into months, with the result that GIS data is permanently out of date inhibiting management and field staff from relying on this data operationally.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.