Remote Sensing Analytics Solutions for Utilities...2019/10/01 · LiDAR data collection and processing to provide highly accurate location information for network infrastructure,

Copyright © 2019 HCL Technologies Limited | www.hcltech.comTechnology for the Next Decade, Today.

Remote Sensing Analytics

Solutions for Utilities

Leveraging Remotely Sensed Data To Automate

Asset Management Operations

HCL & L3Harris - Geospatial Solutions

October 1, 2019

2 Copyright © 2019 HCL Technologies Limited | www.hcltech.comTechnology for the Next Decade, Today.

Puneet Modi and Dan Gruidel

▪ Introduction to innovation – Focus on Remote Sensing for Utilities

▪ Challenges in Utilities

▪ Case Studies – Vegetation Management, Asset Inspection, EPRI & EPIC


Innovation in Remote Sensing for Utilities

Remote Sensing for Utility Operations


Remote Sensing Continues to Improve


Remote Sensing (Drones, UAV, UAS) in Utility Market Forecast

“While we were investigating ways to use drones to improve

inspection, interest in AI among utilities increased, and the two

technologies have converged into a great opportunity. If

drones capture a large volume of high-quality images of

transmission and distribution equipment, AI can analyze them

for signs of breakage, deterioration, and other problems.”

http://eprijournal.com/drones-and-ai-converge-for-power-

delivery-inspections/

Innovation is Ready

▪ PWC estimates that commercial applications have a total

addressable market of $127 billion globally. Drones are

expected to become part of daily operations across

industries as varied as insurance, agriculture, and

journalism.

▪ Consulting firm BCG estimates the industrial drone fleet in

Europe and the US will be $50 billion by 2050 and more

than 1 million units, with most of the value linked to drone

services and data collection.

But, Timing of It Varies

http://eprijournal.com/drones-and-ai-converge-for-power-delivery-inspections/

http://press.pwc.com/News-releases/global-market-for-commercial-applications-of-drone-technology-valued-at-over--127-bn/s/ac04349e-c40d-4767-9f92-a4d219860cd2

https://www.bcg.com/publications/2017/engineered-products-infrastructure-machinery-components-drones-go-work.aspx


Challenges and Drivers in Utilities

▪ Utilities increasingly capture and consume remotely sensed data, but a deeper knowledge of imagery and data management is required

▪ Unmanned Aircraft Systems (UAS) are still maturing and regulatory uncertainty remains, but UAV policies, regulations, and technology standards are gradually taking shape

▪ Remote sensing technology combined with geospatial analytics is becoming a safe, accurate, and low-cost approach that can provide critical insights faster and more affordably then traditional inspections – if set up correctly!

Challenges

Solution


What are Utility Peers saying ….

Customer Benefit Commentary

Leading East Coast

Spends several hundred thousand dollars a year to send people out in the field to do mapping

and measuring of its electrical system. A UAV equipped with LiDAR can collect the same data

and more at a small fraction of the cost and time.

Leading Southern CA

Utility

Key benefits to UAS deployments expected include:

▪ Locate the cause of power outages

▪ Inspect power and gas lines

▪ Access infrastructure in remote areas that are difficult to access by ground crews or

helicopters

▪ Improve situational awareness during emergencies through monitoring of fires

▪ Achieve cost savings, noise reductions and environmental protection by avoiding the use of

helicopters and other heavy machinery.

Leading Northern CA

Utility

Utility envisions drones playing a key role in storm and disaster response. Obstacles such as

downed trees or icy roads make it difficult for crews to assess damage, which in turn hinders

response and restoration time. Using drones to capture high-resolution imagery in real time will

help speed up damage assessments and the deployment of the right resources to restore

power.


Harris / HCL Products & Services • Geospatial Data Management • Geospatial Analytics • Change Management

• Project / Program Management • Integration Services • Managed Services

Business Driven Outcomes• Vegetation Management • Fire/Storm Assessment and Restoration

• Remote Asset Inspection • Active Grid Management

Enterprise Centralized Image & Video Analytics Platform – An end state

Edge

gateway

Protocol

conversion /

Device mgmt.

Utility

Operations &

IT+OT Data

PlatformEvent Hub

Work Mgmt.Image capture

Sensor

4G/5G data

communication

Alerts

Data acquisition

IOT Platform Data Analytics platform Visualization Integration

IOT gateway

Asset Mgmt.

GIS

Work

Execution

Asset Condition

Monitoring

Network Update

Data & Analytics Insights

Customer

captured images

Harris Amplify

Data Streaming

Data Storage

AI / ML

Stream Analytics

Rules Engine


Case Study EPRI – UAS Analytics and Automation Study

Areas of focus

▪ Damage to porcelain insulators

▪ Normal, Flashed, Contaminated, Chipped

▪ Detection of bird’s nests

Two-staged approach

▪ Identify if the image contains the object(s)

▪ If an insulator is detected, identify condition(s)

Results

▪ 550 images processed in ~35 minutes across all potential

anomalies

▪ Despite very small training sets, achieved 77% recall,

72% precision

▪ Highlighted importance of data management integrated

with machine learning training workflow to manage

survey data collection


Vegetation Management / Risk Mitigation

▪ Vegetation is a leading cause of power outages that impact system reliability as well as public and utility staff safety

▪ Vegetation management typically makes up the largest portion of annual T&D maintenance costs

▪ Utilities often use a fixed-time-based approach – regular schedule with routine inspection and cutting in pre-defined areas

▪ Remote sensing data collection (LiDAR, imagery, hyperspectral) to obtain accurate relationship of vegetation to network infrastructure

▪ Advanced analytics to automate current vegetation encroachment detection and anticipate future impacts

▪ Flow results to downstream operational systems through integration points

▪ Move away from fixed-priced management strategy to a risk-informed, condition-based treatment cycle

▪ Improve utility measures across safety, reliability, and cost

▪ Impact customer satisfaction, reduce risk, meet regulatory requirements, and lower environmental impacts

Challenges

Solution

Benefits

Automatic detection of vegetation

encroachment to conductor and to pole with

specific area of encroachment identified (3m)


Asset Inspection Automation

▪ Many inspections still performed only visually with helicopter or inspectors on the ground which are costly

▪ Field employees and pilots are put at risk during the inspection process

▪ Inspections occur infrequently based on regulation and results can vary greatly by inspector

▪ UAV/Drone based remote sensing data collection (LiDAR, imagery, video, IR, thermal, UV/corona) to automate planned/unplanned inspections

▪ Provide scalable data management tools for all types of input data

▪ Deliver accurate and timely analytics that assess infrastructure


▪ Improve safety by reducing trips to the field, climbing

▪ Reduce costs by automating inspection operations with analytics (faster, more accurate, focused work management orders)

▪ Increase reliability by detecting anomalies more accurately and frequently, anticipate outages

Challenges

Solution

Benefits

Automatic defect identification of insulator

with machine learning analytics applied to

UAS imagery data


SAR Land Displacement Monitoring

▪ Continuous structural health monitoring of critical assets (power plants, transformers, dams, etc.) and linear infrastructures (transmission lines, pipelines)

▪ Prevent potential risks of structure failure as result of land displacements

▪ Interferometric processing of remote sensing Synthetic Aperture Radar (SAR) data for analytical and operational land displacement and infrastructure monitoring

▪ Ad-hoc disaster land shift analysis and long-term multi-temporal displacement monitoring

▪ Obtain improved situational awareness of land displacement activities to keep assets structurally secure

▪ Reduce costs by remote data capture, achieve high density of information, precision and accuracy, an wide area coverage

▪ Increase reliability with continuous monitoring and alert areas where land is moving

Challenges

Solution

Benefits

Displacements Elevation

Multi-temporal analysis of Displacements


HCL Overview

Our Growth Our Mode 1-2-3 Strategy

Revenue Split


L3Harris – Committed to Excellence

L3Harris Technologies is an agile

global aerospace and defense

technology innovator, delivering

end-to-end solutions that meet

customers’ mission-critical

needs.

ENGINEERS

20K50KEMPLOYEES~ ~400

LOCATIONS~CUSTOMERS IN

COUNTRIES

130~

Ground and airborne communications and network systems for U.S./International militaries, and commercial customers

$3.8B

Communication

Systems

Mission solutions for space and airborne domain with defense, intelligence and commercial applications

Space & Airborne

Systems

$4.0B

Leading technology integrator to U.S. and international militaries for Intelligence, Surveillance and Reconnaissance, airborne and maritime platforms

Integrated Mission

Systems

$4.9B

Commercial and military aviation solutions, systems, networks and pilot training

Aviation

Systems

$3.8B

$8.9 BILLION | 143,000+ IDEAPRENEURS | 44 COUNTRIES

www.hc l tech .com


Case Study - Remote Sensing Analytics & Data Management Study

Key development themes

▪ Data Management

• Create a centralized, shared repository for imagery,

LiDAR, and associated data

• Allow users to attach, edit/replace, and remove

documents/reports

• Support batch processing to associate files to one or

more GPS locations

▪ Actionable Results through Analytics

• Identify presence/absence of asset components

• Determine vegetation encroachment with LiDAR

• Identify pole tilt angle, set alerts

• Determine if pole is accessible from nearest road

▪ GIS Integration

• Export results to update existing GIS system

• Access GIS layers to answer flight planning questions


Asset Detection / Inventory As-built Update

▪ Maintaining an accurate asset database, as actual construction often differs from the original design

▪ Over time repairs are made that may not be recorded, creating a discrepancy in the asset database

▪ Joint use pole attachments are often changed without documentation

▪ Remotely sensed data collection (LiDAR, imagery) to identify current asset configurations (machine learning analytics)

▪ Change analytics to compare to previous states of the system to identify where change has occurred


▪ Reduce costs by having a better asset register, making routine inspections, outage management, and break/fix work more efficient by knowing exactly what components are in place

▪ Increase reliability by detecting anomalies more accurately and frequently, anticipate outages

Challenges

Solution

Benefits

Automatic component identification of

transmission infrastructure with LiDAR,

imagery, and machine learning analytics


Asset Location Improvement (Conflation)

▪ Many network record database were registered to older, inaccurate base maps

▪ Consolidations and acquisitions create challenges in merging databases

▪ New network build records may not be updated in a timely fashion

▪ LiDAR data collection and processing to provide highly accurate location information for network infrastructure, substations, and real estate property

▪ Conflation methods merging two or more overlapping vector datasets to update existing network records and insert newly identified assets at scale

▪ Flow results to operational systems – Operations, Engineering, GIS, Finance, Real Estate Management

▪ Reduce costs by having a better asset register, making inspections, outage management, and break/fix work more efficient by knowing exactly what components are in place

▪ Increase reliability by understanding the current state of the network infrastructure

Challenges

Solution

Benefits

Red linework indicates new improved asset

locations after conflation


Disaster Management (Change Detection)

▪ Prepare for emergencies and restore power as quickly as possible after a disaster

▪ Damage assessment can take several days because of impassable roadways and inefficient methods of collecting information

▪ Need to understand where infrastructure is damaged and prioritize where to send repair crews and equipment

▪ Leverage remotely sensed data at various scales (LiDAR, imagery, video, SAR) to detect areas/assets affected

▪ Multi-level change detection to diagnose affected areas and focus remediation efforts

▪ Precise anomaly detection to identify damage and prioritize work

▪ Accurate flood mapping and modeling

▪ Shorten restoration time by detecting areas of change and anomalies quickly and more accurately

▪ Improve safety by collecting data through remote sensing rather than rely on field staff

▪ Save cost by better management of field crews, particularly help from adjacent utilities

Challenges

Solution

Benefits

High-level detection of change areas from

space-borne radar to queue up more detailed

UAS missions to detect specific damaged

assets

$8.9 BILLION | 143,000+ IDEAPRENEURS | 44 COUNTRIES

www.hc l tech .com

Documents

Remote Sensing Analytics Solutions for Utilities...2019/10/01 · LiDAR data collection and processing to provide highly accurate location information for network infrastructure,