NASA TechnicaI Memorandum[ 110311511 - i

...... _ :_:Autorn-ated EleCtric ......... Power

....... Management and Control for

Space Station Freedom

James L. Dolce, Pamela A. Mellor,

and James A. Kish

Lewis Research Center

Cleveland, Ohio

(NAgA-T_-IO315L) AUT_MATFD ELECTRIC POWER N90-23125

MAN^GFMFNT A_ CONTPOL FOR SPACF STATION

FREED;3M (NASA) B p CSCL 09B

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_ prepared for the ...........

25th Intersociety Energy Conversion Engineering Conference _ ............ -_.... -_-i_ ......

, -- - cosponsored by the AIChE, SAE, ACS, AIAA, ASME, and IEEE

Reno, Nevada, August 12-17, 1990

- _. _ _: _

Automated Electric Power Mapagement and Control for Space Station

Freedom

James L. Dolce, Pamela A. Mellor, and James A. Kish

National Aeronautics and Space Administration

Lewis Research Center

Cleveland, Ohio 44135

O

!

ILl

Abstract

A comprehensive automation design is being developed for Space Station Freedom's electric power system. A joint effort between NASA's Office of Aeronautics and Space Technology and NASA's Office of

Space Station Freedom, it strives to increase station

productivity by applying expert systems and conventional algorithms to automate power system operation. An integrated approach to the power system

command and control problem is defined and used to

direct technology development in: diagnosis, security

monitoring and analysis, battery management, and cooperative problem-solving for resource allocation. The

prototype automated power system is developed using

simulations and test-beds.

1 Introduction

The paramount objective for our power system's operation is: to generate and dispatch electric power to the

loads while mazimizing Space Station Freedom's productivity and without violating any constraints. The

initial station operation will use dispatchers aided by

human-interactive computational facilities to perform

the necessary command and control tasks. These

tasks constitute planning and decision-making activities that strive to eliminate unplanned outagesl To

make quality decisions, the dispatchers must have an

acumen sharpened through years of experience. Space

Station Freedom will adopt an intensive human command and control approach initially, but we perceive

that in the long run there are opportunities to reduce our reliance upon skilled dispatchers and to make

faster and more consistent on-line decisions by capturing this knowledge in expert systems [1].

2 Power System Automation

2.1 Perspective

The Space Station Freedom's electric power system

is a complex subsystem which must be operated in

concert with other station subsystems and payloads.

Using automation to decrease the human involvement

in these operating activities will increase productivity.

Freedom's operation itself can be viewed as two interactive tasks: apportioning the available resources

among the subsystems and payloads and operating

those subsystems and payloads. The apportioning

tasks must insure that station productivity is maximized while the operating tasks insure that station

integrity is not jeopardized. In the case of the electric

power system, the apportioning of electric energy divides among storage and loads. This activity must reserve adequate energy for periods of eclipse, maximize

experiment productivity, and respond to emergency

power losses [2].

Within the electric power system, resource apportioning and system operation can be accomplished using a recognize-act procedure. The recognition part

acquires data and then classifies the quality of the operating state and its performance status using Subsystem functional knowledge and diagnostic ability.

The action part of the procedure apportions the electric resource and dispatches it through the distribu-
tionnetworkusingvaluejudgmentandapportioning

knowledge.Therecognize-act procedureis repeated

whenevernewdatarequiresnewaction.Thisproceduralparadigmrequirestechnologydevelopment and

demonstrationifiveareasofknowledge[1]:

Detection and Classification: This area applies discriminative knowledge to convert quantitative state data into qualitative classifications of

operating behavior.

• Diagnosis: This area applies diagnostic knowledge to determine the cause of failures.

Forecasting: This area applies predictive knowledge to determine degradation patterns that precede equipment failures and to predict the remaining system performance after component failures.

Planning and Scheduling: This area applies

apportioning knowledge to allocate the electric

power resource.

Integration of Knowledge-based and Conventional Automation: This area applies

problem-solving knowledge to coordinate and focus both algorithmic and knowledge-based problem solving activities.

To develop technology for these knowledge areas, we

have embarked on an aggressive prototype design and

demonstration program. The project is jointly funded

by NASA's Office of Aeronautics and Space Technology and NASA's Office of Space Station Freedom.

2.2 Automation Architecture

Because electrical power system management is a mature terrestrial discipline, tried and true operating

philosophies and techniques exist that can be applied

directly to Space Station Freedom. We believe that

this is prudent and have defined Freedom's power system's operation using electric utility practices and procedures as the motif. In the terrestrial power systems,

algorithmic decision aids are used by experienced dispatchers to guide their command and control considerations [1]. We propose to augment this approach by

developing expert systems to direct the analyses and

to integrate the results into oversight decisions. The

gist is to perform a closed-loop command and control

function using several specialized expert systems to

perform diagnosis, security analysis, and overall coordination; and to use conventional algorithms for power

scheduling. We also include a paraphrase of the Operations Management Application software (OMA) I to

investigate interactions within the hierarchical architecture proposed for Freedom's baseline design. To develop and demonstrate our automation design we will

use the Lewis Space Station Freedom Electric Power

Test-bed and real-time simulations.

The command and control cycle, shown in Figure 1,

begins with a sample of data from the electric power

simulation. This data is processed by expert systems

that recognize and classify the operating state of the

power system and then proceed to perform specialized

tasks based upon the results of the classification cycle.

These expert systems perform two basic functions: 1.)

system security monitoring and analysis and 2.) failure cause diagnosis.

The security monitoring and analysis software assesses the current operating state of the power system

and analyzes the overload risk from possible failure

modes. The system is judged secure if there are no

contingencies that result in an emergency situation.

Otherwise, insecure operating conditions are ranked in

decreasing order of consequences and translated into

constraints upon the scheduling and distribution of

power.

The diagnosis software determines the most likely

cause of abnormal operation. Like the security analysis software, it too generates constraints upon the

scheduling and distribution of electric power.

Imperatives and correlative data are provided from

the Operations Management Application software.

This communiqu_ consists of the strategic commands

for power system operation and pertinent global information. Since we have proposed a hierarchical, cooperative, problem-solving environment, this data is part

of a bargaining transaction among the Station's resource consumers, their suppliers, and the operations

executive--not unilateral commands directed to lowlevel power control functions. This emphasizes the

OMA's role as a manager and coordinator and minimizes its role as an autocrat. These OMA requests are

translated into constraints on scheduling and overall

operation.

The Arbiter expert system software coordinates the

Operations Management Application software's requests, security analysis results, and diagnostic conclusions by specifying appropriate system operating

constraints and electrical loads to a scheduling algorithm. The Arbiter software also determines which

schedule and operating plan is to be used given the

current state of the power system's operation. This

current plan is used to operate the electric power syslThe OMA software--part of the computerized Operations

Management System--performs the automated coordination of

Freedom's systems and payloads [3].
SIMULATIONS

ARBITER

PAYLOADS

AND

OTHER

SUBSYSTEMS

OMA

REQUESTS SCHEDULER

CONSTRAINT

INTERFACE

PLANS PLAN POWER SYSTEM

TEST-BED

SECURITY

ANALYSIS

DATA

DATA

Figure 1: Automation Architecture

tem simulation.

The Scheduler software finds power profiles that

maximize productivity and that satisfy the operating

constraints stipulated by the Arbiter expert system.

The resulting power dispatching schedules repose in

memory (plans) awaiting selection by the arbitration

software.

3 Design and Development

3.1 Software for Security Monitoring

and Analysis

Many competent algorithms exist for the monitoring

and analysis of operating security. The monitoring

task consists of taking measured values of power at

a few critical locations and taking the distribution

network's topology to calculate all remaining voltages

and currents. These parameters are then compared

to the tolerances for line over-current, over-voltage,

and under- voltage to produce a classification of either

normal or emergency operation. The security analysis task is similar. Known transmission line, generator,

and load disturbances are superimposed upon the current operating conditions and analyzed to predict operating tolerances for contingencies [1]. The workhorse

tool for the monitoring and analysis is the fast, decoupled load-flow algorithm [4] which is used to solve

for the voltages, currents and operating margins. We

are concerned that the conventional approach to security monitoring and analysis will impose an extreme

computational work load upon Freedom's power control computer--so much so that our power system will

forgo security monitoring and analysis. Our only initiative has been with Case Western Reserve University

in 1988 to apply artificial neural networks to the security monitoring task. The results are encouraging.

A competent security monitor using artificial neural

networks can "recall" the same operating margins as

calculated by fast, decoupled load- flow in about ....L__ 10,000

of the processing time [5].
3.2 Software for Diagnosis

Two expert systems are being developed for the diagnosis function:

The first, APEX, has been developed in KEE -_ for

use with 20kHz. switchgear. It is a rule-based expert

system that uses antecedent driven logic for generating failure hypotheses (fault detection) and consequent

driven logic for deducing the most likely hypothesis

(fault isolation). An explanation facility is used to

justify the failure cause. The APEX software can accommodate both static and temporal data. Temporal

data is used to identify incipient failures, the detection of which is based on linear regression and correlation analysis. The algorithm finds "soft" failures by

detecting graceful degradation in system performance

and augments this with rules which isolate the cause

of the degradation. APEX is thus capable of detecting anomalies such as: insulation breakdown in transformers, contact depletion in mechanical switches, and

thermal conductivity degradation in power semiconductors. The APEX software also provides postdiagnostic recommendations, i.e., actions needed to recover

from a particular type of failure [6, 8].

Several interfaces have been added to the APEX

software: a graphic data display provides a human

interface, a test-bed data acquisition interface and a

power scheduler interface provide access to their respective systems. The APEX system is now able to

replan power distribution after diagnosing the failure

cause on its power system test-bed. Pertinent data

values from the test-bed hardware are asserted into

APEX's knowledge base. For the incipient fault detection, the values are stored in a first-in first-out table

that contains the last200 values for each test-bed measurement. The automated scheduler produces an optimum load profile and activates_the antecedent driven

failure detection to find deviations from the optimum

plan [7].

The second diagnostic expert system, TROUBLE

III, is being developed in ART 3 for use with the photovoltaic generation and nickel-hydrogen battery storage

systems. It is an expert system that uses set-covering

rather than a series of if-then rules to encode the failure knowledge [9]. In this software, a data base linking

all known system failures to their known symptoms is

built and searched to generate the failure cause hypotheses for observed symptoms. Antecedent driven

rules control hypothesis generation and determine the

most likely cause. Nonmonatonic inference is implemented using reasoned assumptions [10], and rule cotaflicts are identified and resolved using Petri net tran2 (_Intel]JCorp, Inc,

3 (_)In.ference Corp.

sitions [11, 12]. The failure knowledge, however, is

stored as data and is easily maintained. TROUBLE

III uses a standard reliability analysis tool--the failure

modes and effects analysis--to produce the symptom

and failure data base [13]. Symptoms are detected using rule-based classifiers which process static system

measurements.

3.3 The Arbiter

3.3.1 Software for Coordination

The coordination of the diagnostic, scheduling, and

plan execution tasks is implemented in the Arbiter

expert system. This software must make the power

system's tactical operating decisions. For example, it

must take a failure's cause from the diagnosis software;

search the list of plans to find whether or not one exists for this contingency and implement it. If there is

no contingency plan, then Arbiter must estimate the

failure's impact on power generation, storage, or distribution and negotiate through the OMA with the

power consumers to prepare a new operating plan. To

maximize secure operations, it must prepare contingency plans (by negotiating with the consumers) for

each of the risky failure modes evaluated with the security analysis software (Section 3.1). Our development plan for the Arbiter software is to bring automated security monitoring and analysis, failure-cause

diagnosis, and scheduling products together in a rapid

prototyping environment; to discover through experimentation with these products cardinal decision situations and their corresponding tactics; and to capture

this situation-response knowledge in an expert system.

3.3.2 Software for Scheduling

Automating Freedom's resource apportioning task

(Section 2.1) is most challenging. To achieve an

acceptable allocation of power requires cooperative

problem- solving among subsystems, payloads, and the

Operations Management Application software to maximize productivity. The decisions that .are made to

schedule power must take into account the preferences

of the power users, the constraints of the power system

operation, and the overall productivity of the power

schedule. We have been experimenting with a concept

for interaction among the OMA, the subsystems, and

the payloads which will produce decentralized, automated resource scheduling and which will reduce information transfer.

Space Station Freedom's functional agents can be

organized into three categories: 1.) the operations

management agent--the top-level project manager, 2.)

resource managing agents--those which supply basic
resourcesthroughoutthestation,and3.)resourcerequestingagents--thosewhoplanto usetheresources

to performotherfunctions.With our concept,each

agentisallowedto makeall thedecisionsaboutcontrollinganyof theactivitiesunderhispurview.Resourcerequestingagentswillplanactivitiesaccording

to whatis consideredanacceptabledischargeoftheir

functionalresponsibility. Theseactivities,inturn,will

requireresourcesto besuppliedbytheresourcemanagingagentswhomustorganizetosupportheactivitiesof all of theconsumers.Thisconceptualization

yieldsthefollowingdesiderata:

. A two-level hierarchy. Decision-making agents organized by function who either plan what is to

be done or who manage the required resources

constitute the bottom level. An operations management agent who integrates the decisions and

resolves deadlocks constitutes the top level.

. Explicit value systems. Decision-making agents

use cardinal measures of preference for trade-offs

among different resource consumption tasks to

maximize station productivity and to preserve equity among the consumers.

. Local constraints. Resource supplying agents

schedule demands using only the constraints in

their own subsystem. Resource requesting agents

plan activities that do not violate any of their operating constraints.

To automate these distributed activities, scheduling software is being developed which uses a freemarket economic model to coordinate the bidding for

resources. This free-market model provides a timedependent pricing mechanism that ensures a balance

between supply and demand. First, the operations

management agent acting as a free-market coordinator postulates trial prices for all resources and establishes a group value function for each consumer. Next,

the resource consumers produce a time profile of resource consumption that maximizes the difference between their task's value and its resource costs. Finally,

the resource managing agents poll the requesters, establish an integrated usage profile, and quote the fair

price they would charge to supply this demand. The

free-market coordinator compares the fair prices with

the initial trial prices and establishes a new set of trial

prices. The bidding cycle is repeated until the fair

price quoted by the resource supplying agents matches

the trial price [14].

3.3.3 Software for Battery Management

A battery management expert system is being designed which will extend battery life without sacrificing load scheduling flexibility. This system will perform life prediction and state-of-charge estimation by

compiling and analyzing trends in battery data. Using this information, the battery management software

will synthesize charging and discharging power versus

time profiles that maximize battery life. The preferred

charging and discharging profiles will be used by the

Arbiter coordination software (Section 3.3.1) during

the interactive resource scheduling described in Section 3.3.2. The Arbiter will adjust the fair price based

upon the battery's life as well as upon its charge.

3.4 Test-beds and Simulations

Two development paths are being pursued: The first

uses the APEX switchgear diagnostic system and a

zero-one, constrained optimization scheduling algorithm to produce load shedding or reconfiguration

commands for a small 20kHZ test-bed. This test-bed

(known as the 20kHz Brass Board) contains several

pieces of switchgear and a network of microprocessors

for gathering data and commanding the switchgear

[8]. The objectives are: to demonstrate switchgear

failure detection and diagnosis, to explore cooperative problem solving between planning and diagnostic systems, and to determine the difficulty of integrating a LISP-based development computer with an

Ada-implemented distributed control system. This information will be used to guide the integration of our

automation products into the Space Station Freedom

Power System Test-bed.

The second development path uses simulations of

the resource consumers and producers to create an

experimental environment in which to bargain for resources. Enough payloads and resource producing subsystems will be modeled to identify the intensity of the

communications. We intend to incorporate the Space

Station Freedom Electric Power Test-bed into the simulation environment to verify that the automation designs are competent for real hardware. This corroboration of simulation-based findings will use as much

power system hardware and software as practicable.

4 Postlude

The high cost of tile initial space station is a major

issue. A strong justification for any new technological

alternative is needed if it is to secure a berth aboard

Freedom [2]. Our objective is to design a competent, highly-automated electric power system that will
quicklyamortizeits development expenseandyield

thedividendof increasedproductivityforthebalance

of Freedom'slife. Webelievethat prudentlyintegratingexpertsystemswith conventionalgorithms

willsignificantlyreduceFreedom'soperatingcostsand

thatthisbenefitwarrantsourinvestigations. Ourapproachwill reducethegroundcrewneededto managepowersystemoperation,reducethestationcrew's

timeusedtosupportground-basedcommandandcontrol activities,andwill reducetheamountof extravehicular-activityrequiredformaintenanceandrepair.

Ourfirst challengeis to developa-prototype.Second,wemustdetermineourprototype'seffectiveness

asan alternativeto Freedom'sbaselinedesign.Finally,wemustascertainits development costs.We

will beginby:

1. Identifying"hooks"and"scars"for automation

applicationandgrowth.

2. Substantiatingthereadinessof knowledge-based

automationtechnologiesfor applicationaboard

SpaceStationFreedom.

3. Definingthe solutionfor cooperativeproblemsolvingamongthepowersystem,otherresource

producingsubsystems, OMA,andpayloads.

Only through concerted evaluations will we provide

the rationale for flying a productive, high technology

power system aboard Space Station Freedom.

References

[1] J. L. Dolce, "An integrated approach to space station power system autonomous control," in Proc.

22nd [ntersociety Energy Conversion Engineering

Conference, AIAA 1,499-508 (1987).

[2] J. L. Dolce and K. A. Faymon, "Automating

the U.S. Space Station's electrical power system,"

in Optical Engineering, 25(11), 1181-1185 (Nov.

1986).

[3] OMS Evolution Plan, NASA, Space Station Freedom Program, OMS Evolution Team, unpublished (Oct. 25, 1988).

[4] B. Stott and O. Alsac, "Fast decoupled load flow,"

in Transactions on Power Apparatus and Systems, IEEE, PAS-93,859-869 (May 1974).

[5] J. L. Dolce, D. J. Sobajic, and Y. Pao, "Automating security monitoring and analysis for

Space Station Freedom's electric power system,"

in Proc. _5th Intersociety Energy Conversion Engineering Conference, AIChE (1990).

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

L. V. Truong, J. L. Waiters, M. E. Roth,

T. Quinn, and W. M. Krawczonek, "Autonomous power expert fault diagnostic system

for Space Station Freedom electrical power system testbed," in Proc. Third Annual Space Operations Automation and Robotics, NASA, CP-3059,

(July 1989).

J. L. Wakers, E. J. Petrik, M. E. Roth, L. V.

Truong, T. Quinn, and W. M. Krawczonek, "Autonomous power expert system," 1990 Goddard

Conference on Space Applications of Artificial Intelligence, NASA, Goddard Space Flight Center,

Greenbelt, MD. (May 1990).

M. Ringer and T. Quinn, "Autonomous power

expert system (APEX)," in Proc. 25th lnlersociety Energy Conversion Engineering Conference,

ArChZ ( 99o).

L. M. Fesq and A. Stephan, "Advances in spacecraft autonomy using artificial intelligence techniques," in Proc. 12th Annual AAS Guidance and

Control Conference, AAS, Keystone, CO. (Feb.

4-8, 1989).

J. Doyle, Some Theories of Reasoned Assumptions: An essay in rational psychology, CarnegieMellon University, Dept. of Computer Science,

CMU-83-125, Pittsburgh, PA., (1983).

A. W. Holt, "Introduction to occurrence systems," in Associative Information Techniques,

E. L. Jacks, ed., American Elsevier, New York

(1971).

M. D. Zisman, "Use of production systems for

modeling asynchronous, concurrent processes," in

Pattern-Directed Inference Systems, D. A. Waterman and F. Hayes-Roth, eds., Academic Press,

New York, (1978).

J. L. Dolce and K. A. Faymon, "A systems engineering approach to automated failure cause diagnosis in space power systems," in Proc. 2end

Intersociety Energy Conversion Engineering Conference, AIAA 1,590-600 (1987).

P. A. Mellor, J. L. Dolce, and J. C. Krupp, "Electric power scheduling: a distributed problemsolving approach for Space Station Freedom," in

Proc. $5th Inlersociely Energy Conversion Engineering Conference, AIChE (1990).
Report Documentation Page NationalAeronautics and

Space Administration

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No.

NASA TM-103151

4. Title and Subtitle 5. Report Date

Automated Electric Power Management and Control

for Space Station Freedom

7. Author(s)

James L. Dolce, Pamela A. Mellor, and James A. Kish

9. Performing Organization Name and Address

National Aeronautics and Space Administration

Lewis Research Center

Cleveland, Ohio 44135-3191

12. Sponsoring Agency Name and Address

National Aeronautics and Space Administration

Washington, D.C. 20546-0001

6. Performing Organization Code

8. Performing Organization Report No.

E-5505

10. Work Unit No.

488-51-03

11. Contract or Grant No.

13. Type of Report and Period Covered

Technical Memorandum

14, Sponsoring Agency Code

15. Supplementary Notes

Prepared for the 25th Intersociety Energy Conversion Engineering Conference cosponsored by the AIChE, SAE,

ACS, AIAA, ASME, and IEEE, Reno, Nevada, August 12-17, 1990.

16. Abstract

A comprehensive automation design is being developed for Space Station Freedom's electric power system. A

joi.nt effort between NASA's Office of Aeronautics and Space Technology and._AA_SA's Office of Space Station

Freedom, it strives to increase station productivity by applying expert systems and conventional algorithms to

automate lYower system operation. An integrated approach to the power system command and control problem is

defined and used to direct technology development in: diagnosis, security monitoring and analysis, battery

management, and cooperative problem-solving for resource allocation. The prototype automated power system is

developed using simulations and test-beds.

17. Key Words (Suggested by Author(s))

Space electric power

Power system automation

18. Distribution Statement

Unclassified - Unlimited

Subject Category 63

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of pages

Unclassified Unclassified 8

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