%

"tflfTHIRD SYMPOSIUM ONEXPERT SYSTEMS APPLICATIONTO POWER SYSTEMS

Panel Session"Practical applications of Expert Systems

in power systems and their future trend."

Chairperson : J. Toyoda Tohoku University JAPAN

Panelists :B. F. Wollenberg University of Minnesota U. S. A.E. D. F. FRANCJ. M. Mazalerat FRANCE

S. Ito Kansai Electric Power Co. JAPANToshiba Corporation JAPANM. Kunugi JAPAN

APRIL 4, 1991

1V

ESAPS'9I

PRACTICAL IMPLEMENTATIONOF EXPERT SYSTEMS

BRUCE WOLLENBERG

DEPARTMENT OF ELECTERICL ENGINEERINGUNIVERSITYOF MINNESOTA

THIRD SYMPOSIUM ON

EXPERT SYSTEMAPPLICATIONS TOPOWER SYSTEMS

Bruce Wollenberg March 1931

2

ESAPS*9I

DIFFICULTYWITH IMPLEMENTATIONOF EXPERT SYSTEMS1) Second generation computers in EMS systems2) Lack of access to data3) Lack of acceptance by personnel

FUTURE OPEN SYSTEMS AND THE INCORPORATIONOF EXPERT SYSTEMS

1) Ease of incorporation of expert systems2) Open data base3) Better acceptance

3 ESAPS>9I

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WHAT IS AN OPEN SYSTEM?

Standard operating systemsstandard communicationprotocolsopen data base

a) Different manufacturers hardware easily integrated

b) INCREMENTAL UPDATE (change out one CPU at a time)

c) Standard operating system: any application written for thatoperating system can run on the EMS.

d) Standard network interfaces and protocols: anything can beconnected to the system.

c) Any software vendor will be allowed to access data.

f) All applications will use a standard Graphical User Interfacelike XWindows.

ESAPS'9I

RETHINKING CORPORATE COMPUTINGNEEDS:

1) EMS ought to be onlyone of many networked systems in a utility.

2) All the systems ought to be "open" or at leastconnect to open systems

3) Fundamental data describing the equipment ought to be part of somesystem (engineering?, planning?)

4) Each system has a job toperform, but no group is allowedto reenterdata that it can get from another group.

£SAPS'9I 5

DON'T JUST REPLACE THE OLD EMS WITH A NEW EMS

6ESAFS.9I

OTHER SYSTEM

RETHINK THE EMS SYSTEMENTIRELY

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ESAPS'9I

DOING MOREIN YOUR EMS SYSTEM

NETWORK ANALYSIS APPLICATIONS

1) Very fast state estimator (every 10 sec), display state estimate values onone line diagrams.

2) Security analysis (every 30 sec).

3) On-line voltage collapse analysis

4) On-line transient stability analysis, (Time domain + Transient energyfunction)

TRACKING, NOT SAMPLING.

ESAPS'9I

DOING MORE IN YOUR EMS SYSTEM

ARTIFICIALINTELLIGNECE APPLICATIONS:

1) Smart Executive functions: Smartknowledge based expert system executive that coordinates applications

2) Switchingadvisor, instant topology analysis, instant overload and voltagecheck when selecting breakers.

3) Alarm Handling, tell the operator justwhat is needed.

4) Diagnostic Systems, tell the operator where the problem is located.

5) Smart applications software, robust solutions using knowledge basedsystems

AI is easier on open systems since most AI applications were written forthe kind of hardware and operating systems used in open systems.

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ESAPS'9IDOING MOREWITH YOUR EMS:

MORE REAL TIME DATA1) All relay targeting data should be gathered in the same way as breakerstatus data

Allows diagnostic expert systems to attempt to locate faults

2) All equipment measurements such as temperatures and pressures shouldbe telemetered.

Allows diagnostic tests and predictions of transformer failuresNow being done by Westinghouse in US

3) Direct ties to plant information systems.

4) All transmission lines monitored by fiber optic cables to measure temperature every few cm (EPRI project withBattle Research)

Allows location and monitoring of hot spots.

PLAN TO GET THE DATA NOWFUTURE APPLICATIONS WILL USE IT

ESAPS>9I

DATA BASE: WHAT SHOULD IT CONTAIN

1) Not just sufficient information to drive EMS applications

2) COMPLETE PHYSICALDESCRIPTIONS ofevery major piece ofequipment on the system.

DrawingsPhotographsElectrical and Mechanical NamePlate SpecificationsLists of interconnections with other equipmentMaintenance records

3) Derive needed quantities from fundamental relationships:e.g: derive line resistance, inductive reactance and chargingcapacitance from mechanical configuration andelectromagnetic laws.

4) Future application can always make use of the data once it is there.

RETHINK THE WAY YOU STORE DATA AND USEIT

1 1

ESAPS'9I

DISTRIBUTED SYSTEMS:

1) TVy to do each function locally. Ifnot possible pass the information to ahigher level system.

Example: FAULT DIAGNOSIS:

a) Try to diagnosefault in substation using local dataonly on local substation computer system.

b) Ifsuccessful, just pass fault location to nexthigherlevel.If not, pass ail data up to next higher level.

c) Higher level system uses larger amount ofdata todiagnose fault.

2) Use ownership and responsibility boundaries as the guideline to determine who does each function.

12 ESAPS'9I

Existing Problems withNetwork Analysis

Building the OriginalModel:Data is wrongData is constructed in multiple data bases withcross refs

Substation one line on CRTLine and Bus data in fileMeasurement dataGenerator data

Intelligent Data Entry System (IDES):Customer gets workstation to build all dataOne data entry point (substation, line data, measurement data

generator data)Build one line diagram, select bus, line or breaker

get context sensitive window to enter dataRun power How on data whenever neededExpert system to diagnose problemsWhen complete run state estimator and OPFTurn over complete, bug free data to vendor

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13ESAPS'9I

i MAINTENANCE OFEXPERT SYSTEMS IN A CONTROL CENTER1) Operating departments need manypeople to maintain

the data that resides in an EMS.2) The expert systems knowledge base must be easy to maintain3) Mostof the data needed fro the expert system must come

from the EMS data base4) The ability to maintain the knowledge base is a strong factor

in resisting expert systems technology

UTILITYMUST NOT HAVE TO ADD PEOPLE TO MAINTAIN THEEXPERT SYSTEM KNOWLEDGE BASE

Bruce Woilenberg March 1931

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I

APPLICATION OF EXPERT SYSTEMSTO GENERATION SETS

TRENDS AT EDF

Jean-Michel MAZALERATResearch and Development Division - EDF

ADVANTAGES OF EXPERT SYSTEM

Some one hundred expert systems have now been developed byEDF. Of these, a relatively small number, on the order oftwenty, are actually operational; the remainder are in theprototype stage or undergoing feasibility studies.

Despite this fact, there is no doubt that expert systems havedefinitely made a name for themselves^ and have become anindispensable complement to traditional algorithm methods.

The great breakthrough made by expert systems during theEighties was to separate completely the problem-solvingprocess, whether by algorithmic or heuristic methods, from theknowledge base. This concept, enhanced by methodologicaladvances subsequently achieved, has had a two-fold impact:

the solving of combinative problems by heuristic methods,

the representation by explicit model of a field of knowledge.

This approach is not restrictive and has no single field ofapplication. It may be considered to be a new means ofdeclarative programming. It is perfectly compatible with thetools made available today by manufacturers of work-stationsor PCs.

2

II PRINCIPAL APPLICATIONS

At EDF, the principal domains where applications based onexpert systems have been implemented are:

software engineering,

aid in operation of installations,

monitoring and aid in maintenance,

language-processing and human sciences.

KSE and DIVA, which were described in a presentation at ESAP91, are good examples of the benefits of expert systems for aidin operation or in monitoring of installations. (A list of themajor systems now operational or in the prototype stage isincluded in the annex.)

!i

11-1. AID IN OPERATIONS%

Our recent studies have primarily concerned specifications forman-machine interfaces and alarm-processing. I

i_In this domain, two of the advantages to knowledge-basesystems:

; .

. the ability to trace a line of reasoning and therefore to explainthe result,

I

and facilitated modification, by mere updating, of theknowledge base,

are utilized in the KSE system which monitors, in real time, allthe electric power supplies in a 900 Mw PWR nuclear powerplant and helps the operator to deal with any failures whichmay result in the installation.

_.

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II - 2. MONITORING AND AID IN MAINTENANCE

Automated monitoring systems, even using the mostsophisticated techniques for signal-processing, are limited tothe detection of an abnormal situation, and do not offer thepossibility of diagnosing the cause. The use of expert systemsenables one to supplement the global chain of software toolsavailable with modules for interpretation and diagnosis. Thesemodules make available to the operator the knowledge ofexperts, which is contained in the knowledge module on whichthe expert system is based.

One example of software for aid in diagnosis is the DIVA expertsystem, functioning off-line, which will assist maintenanceoperators in nuclear plants in their diagnosis of the principalfailures in large turbine generators.

111. CONCLUSIONS DRAWN FROM THE DEVELOPMENT OFAPPLICATIONS BASED ON EXPERT SYSTEMS

111 - 1. CHOICE OF TOOLS

A considerable proportion of the applications utilize house-developed tools. These are extremely powerful and quitefamiliar to development teams. Two have already beenmarketed (GENESIA I and GENESIA II).

These two products are used by the KSE system, among others.

At the present time, no product has reached the market which issuitable for all types of application.

Emphasis is mainly given to the phase of representation byknowledge-model. The fact that this phase is independent fromthe choice of the tool to be used guarantees the continuingvalidity of the knowledge base and facilitates subsequentmaintenance.

For reasons of cost, when the application is developed bysubcontractors, this independence often results in using theproduct best adapted to the model.

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111 - 2. PERFORMANCE

Most of the applications we have based on expert systems havean operator-aid function and operate off-line. The resourcescurrently available through PCs and medium-power work-stations are sufficient to fill the needs of the users.Interaction with the operator, who generally wishes aninteractive process and representation by graphic display, is aminor constraint in terms of time.

This is not true for real-time systems, for which expertsystems often require too long a period of calculation. Thisclassic problem was resolved in the KSE system, where real-time calculation is performed by several different modules.Each module is a specialized expert system built on a specificseries of logical rules. Each expert system uses the GENESIA Iinference engine, which is based on propositional logic andutilizes forward chaining; for reasons of performance, it iswritten in C language.

IV. CONCLUSIONS - FUTURE TRENDS

While there are few expert systems operational today innuclear plants, EDF's experience with numerous prototypesindicates that this technique will soon lead to significantprogress and benefits in trie domains of aid in diagnosis and inoperations.

Expert systems enable the "recovery" of expert know-how. Suchknowledge implanted in an expert system allows for thepreservation of the accumulated know-how in a given domain.This knowledge may then be put to use by less specializedpersonnel, most particularly in decision-making tools innuclear power plants. The knowledge base may be furtherenriched by extensive user feed-back and improved efficiencyof tools.

The characteristics intrinsic to expert systems make them themost appropriate tools for aid in operations, alarm-processingand aid in diagnosis.

As far as methodology is concerned, a special effort has beenmade with regard to one of the life-cycle phases which appears

iIl

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today to be a determining factor: that of representation byknowledge-model. Such a model often implies an explicitanalysis of the expert's reasoning, sometimes going beyond thisreasoning and taking also into account a field of knowledge onwhich the expert has based his know-how. This type ofapproach has positive repercussions on our future possibilitiesfor maintenance of the system, as well as, in the developmentphase, similarly positive repercussions on the expertsthemselves and on the field of application.

The industrial manufacturing of the first knowledge-basesystems points up the problem of validation. This is aparticularly complex and costly phase. Current studies onsoftware engineering are attempting to define a methodologyfor validation.

Finally, the life-cycle of these systems must be supplementedby new methods for gathering knowledge. In this regard,certain methods are currently being tested in order to constructa methodological link between the "interview" with the expertand the building up of the knowledge-model.

6

ANNEXE

Off-line expert systems for maintenance and non destructive testin■.■

. MIGRE Diagnosis of loose parts and interpretation of mechanical 4shocks in the primary circuit, j|

. DIVA Vibratory diagnosis for large turbo-generator sets, 'f>M i

5 .. RGL Troubleshooting of electronic equipment of the control J)

.

;

rod drive mechanism, fjlUU UMVt.

NICOIICIMIOIM,

||

. EXTRACSION Classification of flaws in U-tube steam |

On line E.S. for operating assistance and trainin

. KSE An E.S. monitoring electric power supplies for Bugey fInuclear power plant,

An E.S. for the training of plant operator in the event ofsteam generator tube ruptures,

. SEPIA-s

.SEPT An expert system approach for the monitoring of extra §high voltage substution control equipment, II

.AMPERE A knoledge-based system for network renconfiguration, |1"i

.SODEXPERT: An E.S. for the diagnosis of chemical contamination of the jsecondary water of a PWR, !

Off line assistance for fiel management and shuffling during outages

A knowledge based system for PWR fuel shuffling

Off line assistance for analysis of the feed-back experiences

. EXPRESS/EXPSF E.S. forreiiability studies.

. EXPLEC An E.S. for humen expertise transfer.

7

PRACTICAL APPLICATIONS OF EXPERTSYSTEMS IN POWER SYSTEMS AND

FUTURE TRENDS

- A USER'S VIEWPOINT -

APRIL 4, 1991

SHUNICHI no(THE KANSAI ELECTRIC POWER C0..1NC.)

2Contents of Presentation

1. Why Developing Expert System (ES)Seeds .Progress of AI TechnologyNeeds :Problems in Utilities

2. Examples of Practical Application

First System Second System1987 1991

500kV s/s 500kV_ sw/s_Control Center etc

*

*

4. Comments From Our Experience

3. Future Visions

.

4

1. Why Developing Expert System(ES)

Seeds :Progress of AI TechnologyNeeds:Problems in UtilitiesUtilities i

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-.

Progress of AI Technology

I

III

iiI!

1i1

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I

Current R&D of AI

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6

»rFI"V

I4

in The Kansai Electric Power Co. .lnc.

" R&D Started from 1984. Classification b oseAbout 50 are underProgress,now Number of R&D

Classification by Fields

Generation(Thermal.Nuclear)

Augiist,l9B9(Total 53)Distribution □ August,l9B7(Total 44)

HZ!□ Transmission Network

Needs of ES Application to Power System

BackgroundOffering Attractive Jobto Young Generation

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9.

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Progress of ES Application

First System Second System1987 1991

500kV s/s 500kV sw/sControl Center etc

ZJ

10

CONTROL AND MONITORING SYSTEM OFSH.IN-IKOMA SUBSTATION

INSTALLED IN 1987

Configuration of Operation Guide System(First System in Shin—lkoma Substation)

Monitoring Board

H.V.Equipments

, y 1 i[Off—Line Inference] \ Inference Results

Languagi: :prologNo.of Rnlcs:ls22

12 FUNCTIONS OF OPERATION GUIDE SYSTEM

12

1.Operation and Troubleshooting Guidance

(l)lntegration and arrangement of fault/trouble information

(2)lnference of fault location and restoration guidancea.Fault :Major problems with circuit breaker operationb.Trouble :Minor problems without circuit breaker operation

2.Self—guided Training for Operators

(l)Review of actual faults/troubles

(2) Simulation of faults/troublesTrainee can set various cases using on-line data

.

Guidance Flow 'i

1i r

Blackout Area j Signals of Fault j Fault Information

i

fIf1

CZD: Operator's Control

13

14

Records of UtilizationFaults/Troubles Training

D^ Due to training or System Maintenance

The System has been much utilized andproved to be very useful

Cju.icia.xioe IVtacie t>y Operators

Cases of Faults /Troubles and Corresponding Guidancescan be Added to the System

"Making guidance is very effective to integrate and arrangeknow-how of experts.

* Input to the system is very difficultand needs to be improved.

15 Expectation to ES Application

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(Results of Survey.January 1990)0 10. 20 30 40 50 60 Max

! 60

-54

! 59

161; 53

!41"Evaluation Method /A : Main Function of

First ES(Shin—lkoma s/s)

:-" " " I : Auxiliary Function" Number of Person :23* of First ES

(*:Managers and Supervisors of Operation and Maintenance) (Shin—lkoma s/s)

RejQLection from . First: ELS Application

Shortcomings

Cases of Faults/Troubles axe limited[Off—line Inference]

Too many letters in guidance

Insufficient Training Function. No on—line Function during Dual CPU System

0Training

0o.0

rt.->

._

Loss of function duringCPU maintenance

Input of additional guidanceis too difficult

j_k User Oriented

' Maintenance System

___^

; j i i i l i I l.

I Information of Fault condition 'YYAAAAAAAAAA7\ 69Emergency Fault Location AAAAAAAAAAAA7\ i67

(Faults) Restorative Operation Guide '/AAAAAAAAAAA7\ \65§ Reports to Related Organizations

;";"

" " : "

; ; ;"

J6O._ —- "____^ im Alart state Countermeasures A:::::::::::::.: v.:::) !55a

■

(Troubles) : """< |

'

N°lL °Peration Proced-e ; check Y/YYYYYYYYY7A\ *Monitoring & Records VYYYYYYYYA \ 54

o Diagnostics ; 59

§ Emergency or Alart State A. .. . !. . ::A: I 1, . ! . : . . \6l<_ Patrol — — , '| Normal State | \53

Repairing or Scheduled Inspection ! 41

; 65

155

! 67

Countermeasures

(\ Adoption of real—timev inference

L/ More graphical guidancerto+->ort

_■

18Configuration of Operation Guide System(Second System in Higashi—Ohmi Switching Station)

Monitoring Board

[Real Time l_Inference]

ESTool: EUREKA- 1No.of Rules : 612No.of Frames : 1781

Handy Terminal

An Example of GuidanceIn Case of Fault [173]

?Q Single Line Diagram andBlackout Area

20

703 & __.#_SHzS (±®&) Blackout Area- &® 2

(Color Changed)

Busbar N0.2

Busbar No.l

m $ m Prf 3fi tL

_$-

T . . _V _. /0 . _

T . Z". TTI An Example of GuidanceInference of Fault Location (Smgle Line Diagram) -^ Case 0^ Fault 1-2/3]

m&nmw&M.£ .tififtlL ( #£_o 100s) <— Name of the EquipmentAAtff. 8 7 13B?7 «— Operated Relays

FaultLocation(Color Changed)

£3

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... g-£Ks/s! Shin-Ikoma Substation

500KV \ U

OKV

100 | | 0 -^j T I,—"—I—"—i 1—"—n ii "ii nii 1 1 1 1 1 1 1 1 ii> i m Dsfo ]^- | SVO j t 1

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;;

«552 562 572$

"

582 1ii 11 n :: n

" " " -- "" ♦ ♦ " *

545 555 565 . 5751 58511 11 11 n 11

100 100 100 ©I e-000Y"Y♦▼ " ♦ ▼"

1h h h548 578 588t« * "620 2j2 650 Zj3

1 ♦—g—* 1 : 1

#

—■—4 1 1I I 100 100 I I

500KV

50OKV

II IIIL 2L IL 2LA A A A100

'

100 100 100

500KV

5 0 OKV $$-3 s_j_K_fi, 7_ 119 18: 50

(30F)_£-#. SS'J i <" TyPe of Fault : Grounding

Afg Phase :Ai 1

I ~° , l^S^S^S.b_li_l ! ! Reman Substation

I _--___!IL 2L II

500KV i

'

J

r^ 1 T ¥ 3«> , . O . 1:" , LP

-,

LPtion- >:: ' ' ' ' 'Ijged) Q572

Q582

Inference of Fault Location (Cross Section) ■ | v S^otF^p^ 21

739 ¥ftffij?fBiffi mm) 500KVM-5 s4>ea 7_ll a 18:51

f£_if.. 4b3s_xKE_ 1 L ( loos ) <— Name ofthe EquipmentA__jffsß -8 7 13B57 <— Operated Relays(3 OF)

_£&3J311 iM <— Type of Fault : Grounding*#tf. Ati .— Failed Phase : A

22An Example of Guidance

[Diagnostics]

Trend of Gas Pressure in GIS

t~)+J

24

I__

C/ 3a.

o

Progress

26Image of Future Tasks in Loa d Dispatching Station

Future System

25

-' Operationi 1

/

\ \

Conventional System . g Planning

;

I / 8 \1

■

' — .v ~X^ ' W Training

Operation N J (^.\ian4tfachine' iiferfaceV)

■

' X/ /////////// /#<

'

- ___j / \ -X

-„, / .Operation)Planning . _r _ _, .

t

' ■ / —System Operation Guide*]

/

—Generation ControlTrailing

/

s

"

—-j / g <Plannning>yAAAAAA//////AZZ). / £ -Scheduled OutagesI— ■"■■■ ]

/ O

| .Generation Control ' &

'

~ Power Generation■vj ■__ _

/

A \L_C ... V N .

Records & Statistics Records & Statistics? ""- ;

"

J n 1 ....Communication Communication

Simulator I CAI(AI Application)

27

28

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f

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Dispatching jStation

;

Function of AICentral D/Sl (1) —* instructionGuidance for

-Restorative Operation■Preventive OperationPlanning .[Power StatioS}/ ILocal D/sl (8)

Guidance forRestorative OperationDiagnostics

Guidance for RestorativeOperations(Autonomous Control)(Unmanned)

I' fy / ftsgV S/1f(37)i IControl Center_63 . A\ fGuidance for Restorative\ & 4* of local Networ

j f77kV S/S, . 7kV S/S^ [A (AUtonomoUscontror';(760)

29Future Developement Image of AI'_

SYSTEM EVALUATION30

I

i

[Expectation of User to; Future ES32Users

Inference Based on Insufficientand Uncertain Knowledge(Fuzzy.Neural Net)

Progressof Function

Faster Inference

Universities [ | Manufacturers

1

2

33

34

Proposal for Future DevelopemenFjRealization of Knowledge Base Packages

For Easy Development and Cost DownUniversities Manufacturers Users

Development of Knowledge Basewith Easy Addition and Deletion

Systematically ArrangedKnowledge j i

Inference aa KnowledgeEngine A ' Base A

Expert System A

Sets ofKnowledge Base Packages(KßP)

Expert System B

Realization ofFlexible System forEasy Modification by Users.

—To Make Practically useful System—

Local or Special Knowledges

When Actual Use StartsOnly Fundamental Knowledgesand Guidances

After Several Years

" Addition of Local or SpecialKnowledges and Guidances byActual Users

" Additoin of Other KnowledgeBase Packages

( Translation ofKBP for Each ES )

32

II

PRACTICAL APPLICATIONS OF EXPERT SYSTEMS IN POWER SYSTEMS AND FUTURE TRENDSA Manufacturer's Viewpoint -

Abstract - In this paper, the approach to practicalapplication of expert systems to power systems andrelatedissues is discussed. In addition, a perspective on the cur-rent state of the art in Japan and future trends from amanufacturers' viewpoint is presented. Topics covered in-clude thecharacteristics ofexpert systems, project manage-ment, expert system hardware and developmentenviron-ments, and the keys to promoting practical application ofexpertsystems.

Keywords - Expert System, Practical Application,PowerSystem, DevelopmentEnvironment

INTRODUCTION

The technological issues concerning practicalapplication of expert systems are being resolved, one afterthe other. The focus is shiftingfrom research toapplication.At present, there are not many expert systems in actualuse. There is a great deal of research and developmentactivity surrounding the application of expert systems topowersystems.

In this paper, the approach to practical application ofexpert systems to power systems and related issues isdiscussed. Inaddition, aperspective on the current state ofthe art in Japan and future trends from a manufacturers'viewpoint is presented. Firstly, thecharacteristics ofexpertsystems are compared to conventional systems. This is alsoused as a backdrop for presenting guidelines for projectmanagement during the expert system developmentandimplementation phases. Trends incomputer hardware anddevelopment environments for such expert systems areintroduced. In closing, the keys to promoting practicalapplicationofexpert systems arepresented.

COMPARISON OF EXPERT SYSTEMS WITH CON-VENTIONAL SYSTEMS IN THE DEVELOPMENT"AND IMPLEMENTATION STAGE

Two fundamental features of an expert system are thatthe expertise of experienced specialists and operatingstandards are stored in a knowledge base, and that aninference engine searches thisknowledge base to extractasolution to a problem. These two features result in a num-ber of characteristics unique to expert systems as indicatedin Fig. 1. Numbers 1 through 6 at the right indicate themerits of expert systems, while items 7 through 10representdemerits andissues.

An expert system is compared with a conventionalsystem that is developed with a procedural language inTable 1. The numbers atthe far left ofthe table correspondto the items in Fig. 1. The merit level is represented by lessthan, greater than and equalstatements. When there is agreat

difference,

double greaterthan and less than symbolsare used. For example,ES > >CS indicates thatconcerningthis point, the expert system is far superior to a conven-tional system. Two superior characteristics of an expertsystem are that the system can growby the incrementaladdition and modification ofknowledge (No. 1);and the

ease with which pre-verification by prototyping can beperformed.

The corresponding life cycle models of expert systemsand conventional systems are indicated in Fig. 2. The lifecycle of a conventional system is best represented by thewaterfall model. A problem definition and system specifica-tion stage is succeeded by a design stage, a programmingstage, a testing stage and finally the operation and main-tenancestage. As a rule, in the waterfall model the start ofa new stage is preceded by the full completion of thepreviousstage.

The lifecycle ofan expertsystem is best representedby aspiral model. A problem definition and knowledge acquisi-tion stage is succeeded by a manufacturing stage and averification/evaluation stage. A prototyping cycle or cyclesare extremely important in the development of the expertsystem.

Two of the demerits of expert systems, as indicated inTable 1, are increased processing time (No. 7) and largermemory size (No. 8). These demerits, however, are beingresolved by state-of-the-art technology. Difficulties inknowledge acquisition(No. 9) and knowledge verification(No. 10) are importantissues that concern the developmentand maintenance of expert systems. These two issues arenot, however, specific to expert systems. They are issuescommon to both expert systems and conventionalprocedural-type systems that concern system specificationand overall system testing.

M.KunugiToshiba Corporation

(Tokyo, Japan)

33

_

A. Expert system (spiral model)

Problem definitionand knowledge acquisition

Fig. 2 Life cycle model ofexpert system and conventionalsystem.

Table 1. Comparison of expert system with conventionalsystem

PROJECT MANAGEMENT IN EXPERT SYSTEMDEVELOPMENT AND IMPLEMENTATION

In theprevioussection, the life cycle ofan expert systemwas presented. Based on this, the development andmaintenance workflow is presented as Fig. 3. This figureindicates the workflow in stages 1 through 6 and thefeedback process ofprototyping, operation and maintenancethat is represented in the spiral model. In addition thisfigure indicates the support tools that are available ateachstage and thecurrent status ofapplication.

The division of work between the manufacturer and theuser, in this case a power utility, is indicated in Fi£. 4 foreach work stage indicated in Fig. 3. The actual division isdependenton the expert system application. In this figurethe two cases of a stand-alone expert system and anintegrated EMS or SCADA system and expert system areindicated. These two cases are fairly representative forJapan. In the case of an expert system integratedwith anEMS or SCADA system, there is often a need for jointdevelopmentwork between the user and manufacturer.This dictates that the two parties have a close workingrelationshipthat is characterizedby mutual cooperation.

The respective integrated EMS and expert systemdevelopmentorganizationsthat are ideal at present for theuser and manufacturer are indicatedin Fig. 5. Manyofthedevelopmental requirements for an expert system areunique. It appears that a separate group from the groupthat normally develops a conventional-typeEMS is advis-

Workflow Support Tool Status

Knowledge acquisition Researchsupport tool

Expertsystem develop-ment support tool Practical

Expert system develop-ment support tool Practical

Knowledge verifica- Researchtion support tool

Expert system mainte- Researchnancesupport tool

Fjg. 3 Workflowof expertsystem development.

Stand-alone IntegratedWork Stage Expert System Expert System

til Problem definition User User

(2J Knowledge definition User User/Mfr.

[31 Inference mechanism User Mfr.review /design

(4J Knowledge-base User Userdesign/ manufacturing

[51 Testing and validation User User/Mfr.[6J Maintenance User User/Mfr.

Support tools Mfr. Mfr.User : power utilitiesMfr. : manufacturers

Fig. 4 Work division between user and manufacturer in expertsystem development (Japan)

No. Expert System Characteristics MeritLevel Solution

The system can growbyperiodicaddition andmodification ofknow-ledge

ES>>CS

Simple supDort ofcom-plex sets of conditions ES>CS

ME Easyto perform pre-

verification ofprototypeR ES>>CSIT Possible tocope with

normally difficult pre-determination ofproces-sing procedures

ES>CS

Possible tocope with

a

i _l;

tionsof conditions

DEMERT

Specialist's expertiseandoperating standardscanbe elucidated and stored

ES>CS6

Processing time islengthened ES<CS High-speed processor

inference method

/ Large size of main mem-ory ES<CS VLSI memorychips

ISSvE

Difficult to obtainknow-ledge ES = CS Knowledge acquisi-

tion support tool

Difficultto completelyverify knowledge ES = CS Knowledgeverifica-

tion support tool10

t

Table 2 Project management ofexpertsystem development.

34

Knowledgeacquisition

[Ul] Conventional System Expert SystemMetrics

i

EMS projectleader

Projectcontrol

Manufacturer

Expertsystemgroup

Expert systemtesting/vali-dation [M2]

Projectleader

ConventionalEMS group' SCADAProject

controlSecuritymonitoring/controlExpert system developmentgroup

Fig. 5 Organization ofexpertsystem development(integratedEMS &expert system).

able. The knowledge acquisition group of the user com-prises superior experts which are limited in number. Incontrast to theknowledge acquisition group, thetesting andvalidation group should comprise a larger number ofexperts toelicit themaximum number ofcomments. On theother hand, it is desirable that the manufacturer's designand manufacturing group and testing andvalidation groupcomprise differentmembers.

ConventionalEMS group

Testing andvalidation

[U2]

Expert systemdesign/mfr.

[Mil

Level of completionindevelopmentstage corr.to the spiral modelandevaluationof experts

Level ofcompletion indevelopmentstagecorresponding to thewaterfallmodel

Progressmanagement

Function ofvol. ofknowledge and infer-ence method

Function of no. ofpro-gram steps andlevel ofdifficulty

Workloadmanagement

Functionofvol. ofknowledge and infer-ence method

Function of no. ofpro-gram stepsandlevel ofdifficulty

Costmanagement

Notes: 1) The most importantmilestonefor the prototype is thepositiveevaluationof experts.

2) In mostcasesthe constructionoftwoor three prototypesystems is required togain the approvalofexperts.

In Table 2, project management of expert systemdevelopment is compared with that for a conventionalsystem. In expert system development,obtaining theapprovalof experts is the most importantmilestone in theprototypingprocess. In high-level developmentwork 2 or 3prototyping cycles are required, while one is usually suffi-cient for lower-level developmentwork. It goeswithoutsay-ing that prototyping can sometimes be eliminated in thecase of development of standardized systems. Workloadmanagement and cost management are based on the vol-umeofknowledgeand thelevel ofdifficultyofthe inferencemethod, but is difficult since a methodology has yet to beestablished.

Issues in expert system project management can besummarized as:

Fig. 6 Hardwareconfiguration.

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Establishment of a project managementmethodology -The reuse ofknowledge and establishment of evaluationcriteria are consideredimportant.

Table 3 Comparison of computer hardware - integrated EMS andexpertsystem.

Systemconfigur-

ationExpertsys-tem com-puter func.

Per-for-

mance

Hard-warecost

Soft-warecost

ComputerhardwareNo.Education and Fostering - Education of system

engineers that perform system integration for expertsystems and fosteringofknowledge engineers. Super-mini-

computerAll-in-onecomputer

EMS.SCADAexpert sys-tem func-tions

1A ■ ■The basic keys toresolving these issues are:

Developmentofdomain shellsAccumulation ofexperiencedataEstablishment of in-house trainingprograms

Engineer-ing work-station

Loosely-coupled

Expert sys-temfunc-tions " "2

computers

Back-endprocessor

Tightly-coupled

Expert sys-tem func-tions

"-3EXPERT SYSTEM HARDWARE AND SOFTWARE - ■ "computersPRESENT AND FUTUREB : Excellent O : Good A : PoorThere are three major types of computer hardware

applied to inference in expert systems for power systems.These are:

1) Mainframe computers and minicomputers i Experts &! knowledgej engineersSuper-minicomputers are applied to expert system

inferencefor EMS.2) Workstations

Workstations are applied to expertsystems for powersystem planning. These workstations aresometimesconnected tomainframe computers or minicomputersthroughLANs (LocalArea Networks).

3) Attached dedicated processor - This type ofprocessor can be attached to mainframe computers,minicomputers or workstations. This is where theinference engine is executed. As an example, thehardware configuration of a control center of theKyushu ElectricPower Co. power system is shown inFig. 6. A dedicated processor (AIP: Artificial Intelli-gence Processor) is applied to the super-minicom-puter implementing the SCADA system as a back-endprocessor for executionofthe expertsystem.

Table 3 is a comparison of implementing an integratedEMS and expert system on these three types of computerhardware. At present, expert system performance is thekey factor and engineering workstations or back-endprocessors are thus often applied to this task. There iscurrently great demand for increasing the inference speedand it is expected that this trend will continue for sometime.

At present, expert system development support tools(shells) are used to build expertsystems. A general-purposeshell is typically used as the developmentsupport tool, butoften proves too general. This causes a large gap betweenthe thinking processes of experts (expert model) and theknowledge representation (knowledge model) of the expertsystem. This is one ofthe underlying causes ofdifficulty inknowledge acquisition and knowledge maintenance. It ishere that a domain shell which focuses on a narrowrange ofapplicationholds great appeal.

An overview of a domain shell for power system faultdiagnosis is givenas Fig. 7. The domain shell is configuredfrom a built-in standard section and an optional section.The system is configured by fillingin the optionalpart. Therelative merits of a domain shell are comparedto a general-purpose shell in Table 4. In order to allowan experttobuildhis own knowledge base using a domain shell, it is neces-sary to limit the size ofthe domain to a specific task such aspower system fault analysis or power system faultrestoration. Decreasing the size of the domain, however,increases the shell cost.

Fig. 7 Domainshell forpower system fault diagnosis.

CONCLUSIONSAn overview of the practical application of expert

systems topower systemshas been presentedmainlyfrom amanufacturer's point of view. The key points can besummarized as:

1) A high-performance processor is required for infer-enceprocessing

2) A domain shellfor narrowdomains and specific taskswhich supports knowledge acquisition, verification,maintenance and user interface configuration

3) Standardization andreuse ofknowledge are requiredtoreduce costs and increasereliability

Table 4 Expert system developmentenvironment.

Develop-ment

supportfunctions

Know-ledge

acquisi-tion

support

Know-ledge

verifica-tion

support

Know-ledge

mainte-nanceDevelopmentsupport tool User

interfaceExternalInterface Inference

perfor-mance

Develop-mentcostNo. Shellcost

support

1 General-purpose shell ■ ' A A A A A " " o2 Domain shell © " " " ■ ■ A

Notes: 1) Excellent, but too many functions.2) Satisfactorytousein domain.3) Easy touse.

4) Criteria for expert system evaluation must be esta-blished

5) Cooperation and coordination between users andmanufacturers is required to promote the applicationofpowersystems

■ : Excellent # : Good A : Poor

Manufacturers will primarily be concerned with the firsttwopoints, and userswith the 4th point. Key points 3 and 5mustbe jointlyundertakenby users andmanufacturers.

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