11

Integrated Decision Algorithms for Auto-steeredElectric Transmission System Asset Management

JimJim McCalley, Yuan Li: McCalley, Yuan Li: ElectricalElectrical EngineeringEngineering –– PowerPower SystemsSystems

Vasant Honavar, J. Pathak: Comp. Vasant Honavar, J. Pathak: Comp. ScienceScience –– Data Data IntegrationIntegration, Software , Software designdesign

Bill Meeker, Y. Hong: Statistics Bill Meeker, Y. Hong: Statistics –– Reliability, DecisionReliability, Decision

Daji Qiao, Computer Engineering Daji Qiao, Computer Engineering –– Sensor NetworksSensor Networks

Ron Roberts: Aerospace Engineering Ron Roberts: Aerospace Engineering –– Nondestructive EvaluationNondestructive Evaluation

Sarah Ryan, Ye Mujing:Sarah Ryan, Ye Mujing: Industrial Engineering Industrial Engineering –– Stochastic OptimizationStochastic Optimization

Iowa State UniversityIowa State UniversityNSF NSF AwardAward CNS0540293CNS0540293

www.cs.iastate.edu/~jpathak/dddas/index.htmlSession W42d (10-12 am) on

Dynamic Data-Driven Application Systems ICCS – 2007, Beijing, May 29, 2007

22

OutlineOutline• Project overview

Motivation and basis for project

A 5-layer design: Software architecture

Layer 1: Long-term power system simulation

Layer 2: Sensing and communications

Layer 3: Data integration

Layer 4: Data transformation

Layer 5: Simulation & decision

• Integrated decision problems: Benders, example

33

Motivation: Ages of two US companyMotivation: Ages of two US company’’s s transformerstransformers

PJM: 55% are within 10 years of design life

For this company, 60% are within 15 years of design life

44

Solution: MonitorSolution: Monitor

Serveron Corporation Launched to Monitor the Performance of Critical Electric Power Transmission and Distribution EquipmentSan Diego, Calif. — DistribuTECH 2001 ÷February 5, 2001 — Today at the DistribuTECH2001 show in San Diego, Serveron Corporation announced its launch as the electric power industry's first dedicated provider of turn-key equipment and services to monitor the health of electric generation, transmission, and distribution substation equipment.

Power Monitor Solutions Welcome to Sierra Pacific…Specializing in affordable next generation power monitoring,predictive maintenance and infrared camera solutions.

ABB wins consulting order to increase power equipment reliability in U.S. 2003-09-15 - Asset management services will help track critical equipment Zurich, Switzerland, September 15, 2003 - ABB, the leading power and automation technology group, today announced it has won an order to help International Transmission Company in the United States manage its power transmission assets.

55

Basis for project: a dynamic data driven Basis for project: a dynamic data driven application systemapplication system

••Monitoring technologies proliferating, provide Monitoring technologies proliferating, provide continuous data streaming of equipment state continuous data streaming of equipment state

••Presently used for detection and diagnosisPresently used for detection and diagnosis

••We want to use for failure predictionWe want to use for failure prediction

••Failure prediction is essential for decision: operations, Failure prediction is essential for decision: operations, maintenance, investment, sensor deploymentmaintenance, investment, sensor deployment

Develop HW/SW prototype for autoDevelop HW/SW prototype for auto--steering steering informationinformation--decision cycles inherent to managing decision cycles inherent to managing operations, maintenance, & planning of highoperations, maintenance, & planning of high--voltage voltage electric power transmission systems.electric power transmission systems.

66

Algorithms for system-

level decision

Failure indices

Monitors?

Monitor

Xfmrs, CBs, Lines

Substation communication

box

STAGE 1

STAGE 2(Scenario 1)

x*

y1*

+c(x*)

P1d(y1*)

Min

STAGE 2(Scenario 2)

y2*P2d(y2*)

Min

x*

Expand?

Maintain?

Operate?

Dynamic Data-Driven Asset Management

Data Integration -Transformation

System

Wide area network

Condition Data

Power Grid (can be simulation)can be simulation)

77

Software Design: ServiceSoftware Design: Service--Oriented ArchitectureOriented ArchitecturePower system asset management: Power system asset management: geographically/physically distributed assets geographically/physically distributed assets and processes, heterogeneity in data and processes, heterogeneity in data semantics, interoperability issuessemantics, interoperability issues

need a software design to accommodate need a software design to accommodate integrationintegration

PSAM: Power System Asset ManagementPSAM: Power System Asset Management

ServiceService--oriented architectureoriented architecture–– Web Service designed to support interoperable Web Service designed to support interoperable

machinemachine--22--machine interaction over a networkmachine interaction over a network–– Facilitates handling heterogeneous data semanticsFacilitates handling heterogeneous data semantics–– Has commercial support, based on standards: Has commercial support, based on standards:

SOAP, WSDL, WSRFSOAP, WSDL, WSRF

J. Pathak,Y. Li, V. Honavar, J. McCalley, “A Service-Oriented Architecture for Electric Pwr Transmission System Asset Mngmnt,” 2nd

Intrntnl Wrkshp on EngrService-Oriented Apps: Design & Composition, Dec. 4, 2007, Chicago, Ill.

88

PSAM PSAM FrameworkFramework

Domain Specific OntologiesExecutes Executes

job request job request (workflow)(workflow)

Handles job Handles job requests requests

from usersfrom users

Stores results of computation Stores results of computation after job is executed; enables after job is executed; enables users to retrieve resultsusers to retrieve results

Establishes dynamic data links Establishes dynamic data links between infobetween info--processing & processing & data providing servicesdata providing services

Maintains index of Maintains index of infoinfo--processing processing services registered services registered w/brokerw/broker

EqpmntEqpmnt data: data: nameplate and nameplate and oprtngoprtng, , cndtncndtn, , maintmaint histories. histories. Querying Querying functions.functions.

Data analysis logic: Communicates Data analysis logic: Communicates w/ dataw/ data--providing services in providing services in federated fashion. federated fashion.

99

1010

Layer 1: LongLayer 1: Long--term power system simulationterm power system simulationEMS, Energy management system: in energy control centerEMS, Energy management system: in energy control center

OTS, Operator training simulator: EMS interfaced with OTS, Operator training simulator: EMS interfaced with power system simulator, plus instructor control.power system simulator, plus instructor control.

An $800k project, cost shared between An $800k project, cost shared between –– AREVA ($537k)AREVA ($537k)–– ISU College of ISU College of EngrEngr ($65k)($65k)–– ISU ISU ECpEECpE ($41k)($41k)–– ISU Elect ISU Elect PwrPwr RsrchRsrch CntrCntr ($25k)($25k)–– MidAmerican ($132k)MidAmerican ($132k)

Developed Developed ““Process to ProtectProcess to Protect”” to obtain EMS modelto obtain EMS model

1111

Layer 2: Sensing and Communications•• XfmrXfmr, expensive, winding paper failure ends life., expensive, winding paper failure ends life.•• DGA: Detect gases generated as paper deteriorates. DGA: Detect gases generated as paper deteriorates. •• Purchased DGA monitor at cost ($30k), Purchased DGA monitor at cost ($30k), KelmanKelman donating timedonating time•• MECMEC’’ss sub, sub, DesMoinesDesMoines, monitor 500 MVA, 345/161 kV , monitor 500 MVA, 345/161 kV XfmrXfmr•• Cannon donating cellCannon donating cell--modem, server, communicate realmodem, server, communicate real--time to time to Minneapolis webMinneapolis web--sever, accessed via internetsever, accessed via internet•• Original install date, 10/06, Original install date, 10/06, XfmrXfmr failed day before! Legal issue, failed day before! Legal issue, required contractual agreement. New install date: 7/07, sister urequired contractual agreement. New install date: 7/07, sister unitnit

Photo-Acoustic Spectroscopy for DGA

• Pulsed EM pressure variation, detected using microphones

• Each gas has unique absorption spectrum

• Select f, measure resultant signal, detect concentration of any gas

1212

Layer 2: Sensing and Communications

1313

Layer 3: Data Handling and Integration

ProblemProblem: Decision algorithms need : Decision algorithms need most recent data. Warehoused most recent data. Warehoused data always data always ““oldold”” with respect to with respect to source data.source data.

SolutionSolution: Make data queries : Make data queries directly to source (equipment)directly to source (equipment)

ProblemProblem: Attribute naming : Attribute naming convention varies in source data. convention varies in source data. User must remember names in User must remember names in databases & relation between databases & relation between them.them.

SolutionSolution: Build name mapping : Build name mapping from userfrom user--side to source.side to source.

Implemented these two solutions Implemented these two solutions using a using a federated, queryfederated, query--centric centric data integration systemdata integration system..

Substation 1

Substation 2

Substation 3

Substation 4

Substation n

…

CB

XFMR

CB

XFMR Agent

CB XFMR

CB

XFMR CB

XFMR

Monitored dataMaintenance

histories (corporate database)

Equipment Data

(corporate database)

1414

Data Sources for Layers 3, 4

RealReal--time data available, but only for one transformer, what about ottime data available, but only for one transformer, what about others?hers?

Obtained a $113k contract with MEC to use DGA lab tests to perfoObtained a $113k contract with MEC to use DGA lab tests to perform rm health assessment/ life prediction of entire health assessment/ life prediction of entire xfmrxfmr fleetfleet

WWill use both realill use both real--time & historical data in DDDAStime & historical data in DDDAS

Also use other data sources, weather, other substation equipmentAlso use other data sources, weather, other substation equipment

λ23 λ12 λ34

λjk

deterioration

function g(c(t))

Statistical Processing

Most recent observation

c(T+1)

Maintenance history and test afterwards c(m)

Inspection historical data c(t) t=1,…,T

Failure probability and time to failure

at any time t MinorNew Major Failed

1515

Layer 5: Simulation & Decision: Coupled, Nested: Coupled, NestedOperations T=1-168 hrs

Maintenance T=1-5 yrs

Planning T=5-10 yrs

From→ To ↓ Unit

commit (UC)

Optimal power flow (OPF)

Security Assessmnt(SA)

Shortterm maint

Longterm maint

Investment planning

Unit commit (UC)

Total load

Optimal power flow (OPF)

Units committed

Bus loads, topology

Ope

ratio

ns

Security Assessment (SA)

Units committed

Operating condition

Weather, failure datainst. cndtn data

Shortterm maint

Units committed

Operating condition

Operating (risk) history

Maint effcts, failure data, cdt history, resources

Mai

nten

ance

Longterm maint

Units committed

Operating condition

Operating (risk) history

ST maint schedule, ST eqp deter rate

Cost of capital, failure data cdt history

Plan

ning

Investment planning

Units committed

Operating condition

Operating (risk) history

ST maint schedule, ST eqp. deter rate

LT maint schedule, LT eqp. deter rate

Cost of capital, failure data, cdt history

• Sequential coupling; solution to latter-stage problem depends on former-stage solutions

• Mixed integer

• High dimension

• A good application of Benders decomp

• Problems have not been solved together before; doing so will improve solutions.

1616

Layer 5: Simulation & Decision: Benders: Benders

hFyEx

bAyts

ydxcMin TT

≥+

≥

+

..

:

Problem P

LB=z*

UB=(h-Fy*)Tλ*+dTy*

*FyhEx..

xc min T

−≥ts

Sub-Primal:zmin

Master:

bAy

ydz.. T

≥

≥ts

λTFy*)(h max −Sub-Dual:

cE.. T ≤λts

1. UB=∞

2. Solve Master: y*, LB=z*

3. While UB-LB>ε do:

a. Solve Sub-Dual

b. UB=min{UB,(h-Fy*)Tλ*+dTy*

c. Solve Master with added constraint z≥(h-Fy*)Tλ*+dTy*

d. LB=z*

4. End while

1717

Layer 5: Simulation & Decision: Operational DM: Operational DM

1. Unit commitment: min {Start-up+Shut-down costs} stdemand≤total gen cap≤demand+reserve2. Optimal power flow: min {Cost of generation} stdemand=total gen, line flows≤line capacities3. Security assessment: min {Contingency risk} stdemand=total gen, line flows≤line capacities

Applied to operations:

Applied to maintenance & planning:1. Long-term maintenance: min{InspCst+MntCst+RplcmtCst}2. Investment planning: min {InvstmntCst+PrdCst}

M. Yan, S. Ryan, and J. McCalley, “Transmission Expansion Planning with Transformer Replacement,” Proceedings of the 2007 Industrial Engineering Research Conference, May 19-23, Nashville, TN.

Y. Li, J. McCalley, S. Ryan, “Risk-Based Unit Commitment,” to appear in Proc. of the 2007 IEEE PES General Meeting, June, 2007, Tampa Fl.

1818

Illustration: test systemIllustration: test system

Fig. 4. 6-bus test system Fig. 5. Effect of contingency

0 5 10 15 20 251500

2000

2500

3000

3500

Hours

$

Average10*Average

UC solution is the same in the two cases illustrated in Fig 5.

But UC solution changes if contingency probabilities are 0, an extreme situation which in fact corresponds to the way UC is solved in practice where UC and SA are solved separately.

Better solutions result when different problems are solved together.

1919

ConclusionsConclusionsAreas of work:Areas of work:–– MonitoringMonitoring–– Component life/failure predictionComponent life/failure prediction–– SimulationSimulation

Nested, coupled decision central to power systems, but not Nested, coupled decision central to power systems, but not formally solved in this way due to scaleformally solved in this way due to scale

Capturing influence of one decision problem within another Capturing influence of one decision problem within another improves solution quality of both; decomposition techniques improves solution quality of both; decomposition techniques make it tractablemake it tractable

NSF support being leveraged to bring $400k+$500k more.NSF support being leveraged to bring $400k+$500k more.

–– Software architectureSoftware architecture–– OptimizationOptimization–– Data integrationData integration

2020

Current EffortsCurrent Efforts

• Solve decision problem of installing next monitor

• Couple operational w/ maintenance, planning decision algs

• Install simulator & monitor; implement software design

• Transform monitoring data to failure rates & life prediction

Single bullet: Dynamic-data driven failure indices and coupled decision algorithms improve quality of operational, maintenance, and investment solutions for capital-intensive electric power asset management.