panel accountable outage program at western area power ... · dji matrice 100 dji matrice 210 baam...

PANEL

Accountable Outage Program at Western Area Power

Administration’s Rocky Mountain Region

Orlando Reyes

Technical Writer, Power System Operations Western Area Power Administration

Accountable Outages at NPPD

Chris Overman Director, Safety and Human Performance

Nebraska Public Power District

Denver, ColoradoMarch 14th, 2018

Orlando Reyes

RMEL Transmission Planning and Operations Conference

Transmission Switching Operations

2RMEL, March, 14th, 2018

Power Marketing Administrations

• One of four PMAs under the Department of Energy

www.wapa.gov

3RMEL, March 14th, 2018

How WAPA is organized

• Market and deliver clean, renewable, reliable, cost-based hydroelectric power and related services

www.wapa.gov

Existing Accountable Outage Program

• Accountable = where we have control (preventative)

• Based on outage frequencies

• Monthly review within regional team members


Future Accountable Outage Program

• Human Performance Focus

• Lessons learned methodology

• Communication venues

• Promote culture of learning and sharing


Managing Accountable Outages …and Other Human Fallibility Issues

Chris OvermanDirector of Safety and Human Performance

Nebraska Public Power District

[email protected]

NPPD Transmission System

• 345kV, 230kV, 115kV• ~5200 miles of line/185 Substations • ~150 Technicians and Support Staff

– Lines/Subs/Construction/Metering

• Transmission Control Center – Doniphan • Experienced with Human-Caused Outages

Human Performance Improvement

• Four Pillars1. A Strategic Foundation2. Reduce Errors3. Manage Defenses4. Culture and Leadership

• Prevention, Detection, Correction (PDC)– Focus 80% on prevention and detection– Get the correction piece right the first time

Areas of Emphasis • Make it Visible

– Balanced Scorecard Indicator• Reinforce the Learning Organization

– Do Good Things with the Information – Searchable Lessons-learned Repository

• Formal Corrective Action Program– Event Classification Matrix – Monthly Steering Committee Meetings

• Training and Education – Individuals, Leaders, SME’s, Evaluators

• Technology – For Tracking Action Items, Trends, Observations

• Leadership – Inspire, Inform, Influence, Persuade

Drew, or Gunn?

Hurricane Harvey – Lessons Learned

Vincent Herrera Manager, South Texas Engineering

Texas New Mexico Power

Texas-New Mexico Power Company Hurricane Harvey Lessons Learned

March 14, 2018

Prepared by:Vincent Herrera, Manager - South Texas Engineering

Prepared for:RMEL

2

TNMP Service Areas

3

TNMP Affected Areas:Bay Area

Alvin Friendswood League City

Brazos Area Angleton Brazoria Sweeney West Columbia

Mainland Dickinson La Marque Texas City

TNMP Affected Service Areas

Comparison – Harvey to Ike

Hurricane Ike• Direct Hit• Serious Damage then

moved on• Wind Damage affecting

Facilities & trees throughout area

• Two weeks to restore all power

Hurricane Harvey• Glancing Blow• Multi-round Bad Storm

• Some wind and lightning, but mostly a flood event

• Restored service more quickly

4

Restoration Summary

5

Transmission and Industrial Summary No transmission outages No loss of service to industrial customers

Distribution Summary Distribution outages only Less than 20,000 at peak 1,720 Outages worked 77,968 customers restored

Equipment Damaged/Replaced Distribution Poles - 50 Pad-mount Transformers - 84 Overhead Transformers - 86 Meters – Approximately 700

Restoration Support Effort TNMP Field and Craft Employees – 171 TNMP Support Employees – 10 Contractors/ Foresters – 288 Additional support on standby – 61

6

Sweeney

7

Brazoria

8

Sweeney

9

INACCESSIBILITY

10

INACCESSIBILITY

TNMP Texas City Office

11

12

Logistics

TNMP - What We Did Well

EOP Plan AssessmentsLogisticsOutside ResourcesRestoration StatusCommunications

13

TNMP – Opportunities for Improvement

Assign Coordinators for Remote RegionsSecurity RestroomsFuel TrucksReview Plans for MealsPrepare Response for when Customer cannot

accept service

14

TNMP - Conclusions

EOP Plan WorksStaging Areas were not RequiredCommunication has improves since IkeReview allocation of Outside ResourcesReview methods of AssessmentRestoration and Status

15

QUESTIONS

THANK YOU

16

Unmanned Aircraft Transmission Line Maintenance Technology

Dusty Birge Owner

UAV Recon

Unmanned Aircraft Transmission Line

Maintenance Technology Dusty Birge

UAV ReconFt. Collins, CO - Solutions Providerwww.uav-recon.com

Specializing in Electrical Infrastructure & Aerial Thermography

RMEL’s Transmission Planning and Operations ConferenceMarch 14th-15th 2018

Agenda

sUAS Trending Services

Aerial Thermography

Case Studies

sUAS Overview (Optional)

Q&A

sUAS Trending Maintenance Services

sUAS – Drone – UAS – Unmanned Aircraft – Bird - Ship

Trending Service’s for “Maintenance Optimization”

Maintenance Inspections

Up Pole

Components & Hardware

Aerial Thermography

Structure Locating for GIS

Structure Inventorying

Rapid Damage Assessment

Condition & Maintenance Documentation

RoW Management

Vegetation Mapping

2D & 3D Maps

LIDAR

Topo Contour Maps

Risk Assessment Models

3 Types of External sUAS Business Models

Equipment Sales & Service

Drones, Software, Training

Distributor – No Flight Op’s

Repairs & Service Work

Service Provider

Equipment Owner

Flight Operations

‘Generalist’

Solution Provider

Service Provider +

Technical Analysis

Data Management

‘Specialist’

In-House Business Model

Utility is responsible for entire sUAS program, operations, and final product.

Sample Checklist Budget Impact BudgetMultiple Aircraft *FixedCharging System & Equipment *FixedField Tech - Laptop Computer & Mobile Tablet Fixed/AnnualOffice Tech - Desktop Computer & Server *FixedPre-Flight Software *Fixed/AnnualPost-Flight Software *Fixed/AnnualInsurance - Aircraft & General Liability AnnualSoftware & Hardware Training FixedTransportation AnnualPersonnel - Flight Ops & Data Management AnnualPersonnel - Management AnnualPer Deim, Travel, Project Costs Annual

* Flight Operations Dependent

Coverage RateCost / ROIScope of Ops

Visual Inspection Data Types

Photos or Video

(1) structure of ~ 25 photos (175MB)

(1) structure of 3:00 Minute Video (1GB)

Data based on previous slide 75 Miles; 24,270 Images; 1,005 Structures; 160GB of Image Data

Photos are easier to file, search, markup, share, and analyze

Videos offer significantly higher amounts of data recording

Extracting stills from video isn’t efficient

Data sizes will vary due to market available sensor options

Stitched & Processed Data

3D Point Cloud

2D Orthomosaic

LIDAR

https://vimeo.com/236642814

http://134.249.136.27/demo/uav.01/#14/40.8595/-99.5880

Understanding Capabilities& Limitations

Tool- a device or implement, used to carry out a particular function; or to be equipped to carry out

a particular function or process. Not all tools are alike!

Aerial Thermography

Thermography Accuracy Factors

45 4540 4330 4115 36

0 33-15 29-30 26-40 24

Smaller Span = Finer Detail

Why is 'Span' important?

Span = 85 o Span = 21 o

Spot Size Ratio =

Span & Spot Size Ratio

Ground Vs Air - View Comparison (Video)

https://www.youtube.com/watch?v=CmdqesPWgQY&feature=youtu.be

How is Aerial Thermography Different

Substation & Line Inspections Smaller Span

SSR Angle – More Accurate

Physically Safer

Visual + Thermal

Patrol Faster +(10) mph vs ground based

*based on default camera settings

Missed during nighttime ground thermographer

(3) Case Studies

Rapid Response – Outage & Damage Patrol

69kV Loop Circuit – Ground Patrolled 2x

(1) Pilot Flew ~90 structures, 400 Photos / 2GB Data

Issue Identified via sUAS, confirmed by Bucket Truck

Utility Cost to Patrol - $1,680 (3) day

sUAS Cost to Patrol - $1,500 (1) day

Plan a sUAS Response Integration

By planning ahead of time, sUAS operators can have pre-programed travel routes; grid understanding,

and faster in-field coverage.

Case Study #1

Maintenance Optimization – w/Circuit Comparison

(30) Poles @

9.6%

(14) Poles @

2.4%

(45) Poles @

38.1%

By using sUAS & maintenance optimization strategies, utilities can budget condition-based repairs and prioritize maintenance on distinct identified

structures, vs percentage of grid, resulting in more ‘critical repairs’ completed per year, which meet traditional maintenance minimums.

• Figures based on actual circuits flown in 2018• Visual Inspection Only – Live 69kV SubT• Each section 15 days or less• Higher Procurement Accuracy

a Estimated contractor costs for climbing maintenance of $500/poleb Estimated in-house costs for climbing maintenance of $300/poleSavings Estimate factors sUAS inspection costs of $75/poleExcludes Material Costs for Repairs

$119ka

$ 63kb$239ka

$126kb$31ka

$17kb

$206k - $389k Savings Estimate on (916 total) Structures @ 91.1%

Structures Considered Critical (89 total) @ 8.9%

Case Study #2

Substation Thermography

Ground Based Method

~ $100/sub; 30 minutes per site, Nighttime only

(1) thermal advisory

~(1.5) total man hrs including report & analysis

Aerial Thermography Method

~$800/sub; Nighttime + Daytime (2 hrs total)

(9) thermal advisories; (2) visual advisories

~(5) total man hrs including report & analysis

Case Study #3

sUAS Overview

FAA – Laws & RegulationsPart 107 – Commercial Operation Guidelines

Flights up to 400’ AGL

sUAS weight limited to less than 55 lbs

Daylight Operations Only

Pilot in Command (PIC) must maintain ‘Line of Sight’

No operations over people or moving vehicles

Can not operate from moving vehicle

Class G only, Other airspace requires ATC approval

Pilot can operate only 1 sUAS at a time

FAA does not regulate Privacy or Data Acquisition

Federal Regulation – Not State Regulated

*Most guidelines above have Waivers & Exceptions which permit legal operations outside of these parameters.

sUAS Platform Categories *Electrical & Industrial Applications

AceCore NEO

DJI Matrice 100DJI Matrice 210

BAAM TECH Futura Aeryon SkyRanger

Multi-Rotor$5k - $40k

Asset Inspection> 30 min flight time

Multiple Payload OptionsManual & Autonomous Flight

Fixed Wing$12k - $20k

Mapping & Photogrammetry Large Coverage Area< 45 min flight time

Fully Autonomous Flight

Advanced Operation$50k +

Inspection & MappingSecure Networks

Extended flight timesLIDAR & Complex Payloads

Intel Falcon 8Pulse Vapor

SenseFly Ebee

Q&A

Key Takeaways

Dusty BirgeUAV ReconFort Collins, COwww.uav-recon.com970-451-1896

• Pick the right tool for the job

• Multiple sUAS is preferred

• Find ‘Specialists’

• Start program with fastest ROI to gain buy in

• Leverage Volume

• Pick 2: Price – Speed – Quality

• _____________________________

• _____________________________

• _____________________________

• _____________________________

Case Study for Long Range Beyond Visual Line of Sight (BVLOS) UAS

Project

Jason Kack VP, Product Delivery

DataSight, Inc.

James Oliver VP, Technology DataSight, Inc.

© 2014 HDR Architecture, Inc., all rights reserved.© 2014 HDR Architecture, Inc., all rights reserved.© 2014 HDR Architecture, Inc., all rights reserved.© 2014 HDR, Inc., all rights reserved.© 2014 HDR, Inc., all rights reserved.© 2014 HDR, Inc., all rights reserved.© 2016 HDR, Inc., all rights reserved.

March 15, 2018 | RMEL Transmission and Planning Conference

Case Study for Long-Range Beyond Visual Line of Sight Project

© 2016 HDR, Inc., all rights reserved.

Helicopters In Transmission Inspection• Helicopters will always have a place in

transmission construction and repair• Transmission inspection services will be

predominantly replaced with Unmanned Aerial Systems (UAS)

• Safer• Higher resolution• Repeatable• Autonomous • Ever increasing variety of sensor technologies

• UAS use limited by inspection distance and cost

Beyond Visual Line of Sight Inspections

Current FAA Part 107 require an elusive waiver to fly beyond the visual line of sight (BVLOS) of the pilot

FAA is starting to support more test cases proving the safety of flying safely BVLOS

There are many approaches to safely flying BVLOS (radar, ADS-B, remote ATC). We have been testing the Visual Flight Rule (VFR) approach

In comparison to overall costs, the economics of helicopters to UAS starts to tip in the favor of UAS at 10 mile inspection flights

BVLOS adoption will be process of sensor technology, flight technology and regulatory process

The Four “C”s of BVLOS

Craft

Communication

Control

Collision

Professional Grade UAVs – Payload & Endurance

Fill’r upPlug me in

Communication

• Cellular• Bonded Cellular• Microwave• Satellite • Combination• Live Video Feed• Current technologies working at 5 miles

Control• Line of Sight (LOS)

• Pilot-in-command (PIC)• Visual Observer (VO)• PIC is always in visual sight of craft

• BVLOS• Visual Flight Rules (VFR)• Sense and Avoid Technology• Real time video transmission• Ability to control craft• Quit on command• Collision avoidance

Collision Avoidance

Traditional Data Management

BVLOS Mission Planning – Universal Data Collection

New Paradigm for Power Company Data Management

Professional GIS Knowledge workers Executive access Public engagement Work anywhere Enterprise integration Contractors

Supporting the Entire Organization

Process to Successful UAS Program Development

Needs• Identify data users• Optimize data collection efforts to meet the needs of most users

Quality• Precision, Accuracy, Reliability, Repeatability and Data Security• Data Quality Objectives (DQOs)

Sensors• Select sensors to best meet DQOs• Understand optimal flying conditions for sensor (altitude, spacing, speed, etc.)

Craft• Initial screening of UAS craft to carry sensors for expected mission length• Final selection based on environmental conditions (weather, portability)

Product• Field check procedures for validating data collection• Processing of data for specific for users (.las, DEM, .dwg)

Workflow• Document workflow from flight operations to data storage• Encourage user feedback for continuous workflow improvement

UAS Sensor Technology

LiDAR• Terrain mapping• Volume quantification• Ground surface mapping• Change detection• Feature detection• Permitting• Vegetation Encroachment

Multispectral / Hyperspectral• VNIR and SWIR• Very high resolution (0.1

m)• Vegetation health

Corona• Coronal discharge• Daylight Use• Video of discharge and

emitter

Magnetic• Subsurface Anomaly• Exploration• Pipeline location• Well head clearance

Thermal• Water temperature• Heat loss• Mechanical stress• Pipeline leaks

Imagery• Vegetation assessment• Cultural resources• Photogrammetry• Infrastructure Condition

Inspection

LiDAR versus Photogrammetry

• LiDAR is more accurate• Penetrates vegetation• Classification of features• Planimetrics• Not impacted by shadows• Construction details• PLS-CADD Ready

UAV Image Example from Phase I 100 Mega

Pixel Camera at 20 meters

Image Zoom with

1.7 mm GSD

LiDAR Data Collection• Transmission lines• Distribution lines• Substations

Choice of craft is based primarily on payload and endurance

Use over critical infrastructure should also evaluate onboard redundancy in electronics and flight motors

Imagery and LiDAR need stability Autonomous versus pilot control Mission simulation Emergency landing considerations

UAS Craft Selection Considerations

BVLOS Flight Mission• 10 mile survey in central Nevada

• Performed with Nevada Institute for Autonomous Systems (NAIS)

• Flight operations performed by AviSight a DataSight partner

• LiDAR and imagery collect

• Pulse Aerospace Vapor 55 with RIEGL VUX-1 UAS LiDAR

Mission planning

• UAS Altitude• UAS velocity• Flight pattern• Data density• GPS RTK Base Station• Ground Panels for calibration• Ground shots as quality

control• Flight simulation

Data Collection Mission Planning for LiDAR• Density – Points per square meter

(ppsm)

• Field of View

• Number of returns

• LiDAR pattern

• LiDAR intensity

• LiDAR wavelength

• Colorization

2 Return LiDAR 7 Return LiDAR

Colorized and non‐Colorized Point Cloud

LiDAR Scan PatternFlight planning is different between side to side scanning versus down facing circular pattern LiDAR

Direct and Positional Communications During Visual LOS UAS Flight Operations

BVLOS for 10‐Mile Transmission Line Data Collect10 Miles

LiDAR Processing

• Inertial Measurement Unit Correction• Static Base Station

• RTK versus PPK• Core Station

• Calibration• Quality Control• Point cleanup• Classification• Planimetrics• Digital terrain model (DTM)• Digital elevation model (DEM)

IMU

GNSS Antenna

LiDAR Point Cloud Ready for Evaluation

Data Evaluation – Data Density Drives New Methods

The automatic analysis of images and videos by computers for event detection, object recognition, tracking, etc.

Achieved thru writing computer programs that apply filters to remove noise and look for patterns and anomalies.

• Reading license plates as you pass thru toll gates• Reading money inserted into vending machines• Inspecting apples to determine if there are bruises• Warning drivers when they are drifting out of their lane• Facial recognition/biometric security checks

…and many more

AI or Computer Vision is used in many places

Simple Example –Woodpecker Holes

IncludeStep 1. Train the model

Exclude

AI Program analyzes image: highlighting holes that are over 1” wide

(yellow) highlighting holes that are over 2” wide (red)

Step 2: Run AI Pattern Recognition Program

Step 3: Review Results

Inputs: Pole 7-76-114 today and from an image 12 months ago

Step 1. Train the model – (model is already trained)Step 2. AI Program analyzes image

A) highlighting holes that are 10% larger B) highlighting new holes

Step 3: Review results

Insight into Action – only ”active” poles need treatment

Woodpecker Inspection – Part 2

Step 3: Review Results

Insight into Action – only ”active” poles need treatment

Inspection Workflow

UAS offers a technology to safely collect higher quality data BVLOS technology is advancing and supporting regulatory adoption Understanding data needs and quality is part of UAS mission planning Data management has to be part of the planning process Larger data sets will likely require some degree of automation Long-term goal is better use of data to optimize uptime and increase worker safety

Conclusions

Questions

Non-contact EMF-based Transmission Line Monitoring

Jonathan Marmillo Business Development Manager

Genscape

www.genscape.com | © 2017 Genscape Incorporated. All rights reserved.

Non-Contact Transmission Line Monitoring and Dynamic Line Rating Jonathan Marmillo, Business DevelopmentM: 484-368-4630E: [email protected]

LineVisionTM


Introduction to Genscape

Leading provider of Energy Market Data and Systems- Serving leading generators, utilities, industrials, integrated energy companies

and regulators with fundamental data and forecasts to bring transparency to energy markets and manage risk

- Founded in 2000

- Over 500 employees; US-based with offices in CA, CO, KY, MA, NJ, NY, TX

- European offices in: Amsterdam, Zurich, London, & Hamburg

- Over 5,000 transmission line monitors deployed worldwide

- Nearly 100 patents in areas of monitoring, analytics & business process

Provider of LineVision Transmission Line Monitoring Systems Genscape LineVision Monitor


The Genscape LineVision System

Features & Capabilities:

• Non-contact, ground-based transmission line monitoring system

• No need for outages, utility crews, or heavy equipment

• Secure web interface to view and download data

• Secure EMS data feed option

• Complete turnkey solution, including installation and warranty

Real-Time Data Fields:

• Dynamic Line Ratings (DLR)

• Short Term Emergency Limits

• Forecasted Ratings

• MW, MVAR, Power Factor

• Loading/Current

• Conductor Sag/Clearance

• Conductor Temperature

• Icing & Galloping DetectionGenscape LineVision Monitor

at installation


LineVision™: Non-Contact Clearance and Temperature Monitoring

Principle of Operation:• Measure AC magnetic (B) field amplitude, phase and vector orientation in several locations• Determine conductor clearance/sag by adjusting circuit-geometry EMF model to agree with sensor data• Determine conductor temperature based on sag/temperature analysis• Over time: Adjust sag/temperature coefficients based on as-built observations


Measurement of Electromagnetic FieldTransmission Line EMF Patterns Relate to

Conductor Sag/Temperature LineVision EMF Monitor Schematic

Wireless data transfer

Electrometer (E-field) measures voltage

Inductive coil magnetometers measure:Horizontal B-fieldVertical B-field

Analog-to-Digital Converter samples 60 Hz waveform from each sensor at ~10 kHz

Hot Conductors

CoolConductors

Ground Level

LineVisionEMF Monitors


LineVision EMF Monitoring: Monitored Loading/Current vs. SCADA


Line Monitoring from the Real-Time Operations perspective

DLRs are very favorable relative to static ratings, and 5-min STE ratings are even higher.

Circuit A CurrentCircuit B Current

Circuit A


Average ampacity this hour of day

5th percentile of ampacity

95th percentile of ampacity

Actual current loading (hourly avg. and 5th, 95th percentiles)

Static rating

DLRs typically peak in mid-afternoon.

DLRs are lowest in early AM hours.

DLR hourly trends reflect average wind speed distributions:

-Calm in the early AM

-Windy in the afternoon

Hourly Wind Speed(Average with 5th/95th percentiles shown)

Time-of-Day Ratings Profiles


The Overall Asset Management Strategy

Asset Reliability Asset Health Asset Optimization

Clearance Monitoring

Icing & Galloping Detection

Capacity Utilization


Asset Management - Clearance Monitoring

115kV, Southwest USA

Loading Pattern:Early Morning Peak

Monitor & Trend Conductor Sag

True-Up PLS-CADD Models

Anomaly Detection & Variance Algorithms

LiDAR Verification


Asset Management – Icing Detection

Ice Detection algorithm is based on a comparison of observed vs. expected:

• Conductor temperature • Conductor clearance

…under actual loading and weather conditions.

-10

0

10

20

30

2/3 2/4 2/5 2/6 2/7 2/8

Tem

pera

ture

(deg

C)

Ambient TemperatureIEEE738 Conductor TemperatureGenscape Monitored Conductor Temperature

0

1

2

3

2/3 2/4 2/5 2/6 2/7 2/8

Icin

g R

isk

Sco

re Icing Probability ScoreAlert Threshold

10.5

11.0

11.5

12.0

12.5

2/3 2/4 2/5 2/6 2/7 2/8

Line

Cle

aran

ce (m

)

Expected ClearanceObserved Clearance

Conductor temperature discrepancy

Conductor clearance discrepancy

Icing alert sent to operator

Improve Situational Awareness

Prevent Asset Damage


Asset Management – Galloping Detection

Galloping Detection algorithm is based

on the detection of a low-frequency

harmonic signature

Galloping alert sent to operator

Improve Situational Awareness

Prevent Asset Damage


Asset Management – Capacity Utilization

CIGRE Grid of the Future 2016 Paper: Model historical Dynamic Line Ratings for a 161kV Line and correlate to times of market congestion.

Primary Mode of Constraint: Wind Driven Congestion

A

B

Engineering Assumptions:


97% of the time, extra capacity was available

3% of the time, reliability may have been at risk




Binding Constraint: Breached

Shadow Price: < -$700

November 7th 2017 - morning hours

Real-Time LMP Pricing Contour Map

Studied Line – Flow Gate

November 7th 2017 –Later that day… 3:05pm MT

Peak Convective Cooling Hours

Asset Capacity Not Optimized


[email protected]

US: +1 502 583 3435

EU: +31 20 524 4089


Jonathan MarmilloE: [email protected]: +1 484-368-4630

[email protected]

US: +1 502 583 3435

EU: +31 20 524 4089

panel accountable outage program at western area power ... · dji matrice 100 dji matrice 210 baam...

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