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Exoert Simulation Automates Space Electric Power System

Scott A. Starks1 and David LV Elizandro2

ASA has undertaken the task N of designing a manned space station. To decrease airborne and ground-support costs and t o achieve other benefits, subsystem designers have been encouraged to automate activities ordinarily performed by humans. NASA's Advanced Technology Advisory committee and the Automation and Robotics Panel of t h e California Space Institute pub- lished reports clearly identifying the space station electrical power system as a primary candidate for implementing new robotics/ automation techniques for monitoring, trend identification, fault diagnosis, system control/load management, automated mainte- nance, repair and replacement, and operations.

Conditions affecting a space

~

Though significant differences

exist between the operating environment of the

electric power system on a space station and

on a terrestrial power system, technology

innovation developed through space-based projects can and will

ultimately have an impact on earth-based power systems

power supply system are substantially different from those affecting typical terrestrial power supply systems. In the United States, practically all power systems are interconnected, so that the loss of a large generating unit is replaced from the entire interconnected system, retained by frequency bias generation control on all systems, until arrangements are made between the deficient system and its neighbors. Each terrestrial system has full- time dispatchers to operate the system and engineers and repair crews who can reach the affected lines or plant in a short time. In the space system, there are no interconnec- tions, and engineers not aboard the space craft would

An artist's conception of space station Freedom at the permanently manned configuration, which is scheduled to be achieved by late 1999. The configuration at this time consists o f U.S., European, and Japanese laboratory modules and the U.S. habitation module and three sets o f solar arrays providing more than 56 kilowatts o f electrical power. Freedom, in its permanently manned configuration, can support a crew o f four.

University of Texas at El Paso, Electrical Engineering Department

East Texas State University, Computer Science Department

require months to come aboard, rather than minutes or hours. The October 1989 issue of Computer Applications in Power featured a reliable power supply system for the space station proposed at that time. Because of budget constraints, the size of the space station has been reduced to contain only four people, instead of eight as planned originally, and this means that power supply should not call for activity from any of them, if possible. The new system is much more automated, and makes maximum use of expert systems techniques.

Many benefits accrue from the utilization of computer-based automation technology in terms of the management, operation, monitoring, and control of complex space subsystems. On the

space station, the electric power system plays a unique role in coordinated operation with other onboard subsystems in that it supplies a primary resource (electric power) upon which all other subsystems depend. This is a key interdependency because any degradation of the electric power system impacts other subsystems and ultimately the operation of the overall space station.

In part because of the unique and complex role that the electrical power system will play, there is a great potential for applying automation techniques. Automation can result in increased safety and reliability by providing rapid responses to faults or anomalous conditions in a fail-safe/fail+perational manner. An automated system may provide greater operational flexibility by allowing for the reprogramming of critical operating limits to conform to changes in system or component performing capabilities.

When effectively applied, automation provides the opportunity for lowering both initial and lifetime costs. Onboard automation can alleviate the large crew and ground support requirements substantially. Onboard analysis of electric power system data results in the reduction of the volume of data required to be down- linked for ground analysis, thus decreasing communica-

ISSN 0895-0156/91/$1.0001991 IEEE

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October 1991 11

tion requirements. In addition, increased reliability gained through automated trend and state monitoring techniques can reduce the hardware redundancy requirements. Resupply costs will be reduced by extend- ing the useful operating life of components by continual- ly monitoring and executing appropriate responses.

Space Station Electric Power System Concepts Space station Freedom has recently undergone a down- sizing and redesign. The redesign was driven by desire t o have a more cost-effective program, to reduce extravehicular activity, and to simplify the design and ultimately the operation of the station. Current plans call for an initial man-tended capability station to be functional by December 1996. Whereas the original plans for the station called for an aggregate power generating capacity of 75 kilowatts, the new design calls for a power generating capacity of 56.25 kilowatts in the station’s final operational configuration. This will be accomplished by the building of three solar panels supported on a preintegrated, seven-section, 31 7-foot truss, as opposed to the previous design, which incorporated four panels.

The initial man-tended operational capability will require six assembly flights using the space shuttle. After these flights, space station Freedom will have a capability of generating 19 kilowatts of electric power. This will be sufficient to support experiments dealing primarily with materials processing in a microgravity environment. The man-tended capability restricts the station to be fully operational only with the space shuttle attached.

By the end of 1999, the space station will be in transi- tion to a permanently manned configuration. The transi- tion is to be accomplished in discrete phases and will require eleven additional assembly flights of the space shuttle beyond the six needed to achieve the man-tended configuration. In this final state, space station Freedom will be suited for permanent habitation by a four man crew, instead of the original proposed crew of eight. This places additional demands of increasing the level of automation in the station’s various systems, including the electric power system. The crew will be interchanged at periodic intervals of time. In the permanently manned configuration, the electric power generating capacity will be 56.25 kilowatts.

The electric power system for the space station will be complex, requiring a vast amount of sophisticated software. The primary objective of the system will be to ensure a reliable supply of electric power for the various power uses on the space station. The nature of this task is largely shaped by a power distribution network design that will incorporate a high degree of redundancy in con- junction with a critical limitation on the availability of power and the concordant reluctance to waste it.

An artist’s conception of space station Freedom at the man-tended configuration. One of two nodes and the U.S. laboratory module provide pressurized work space for visiting astronauts carried to Freedom aboard the shuttle. The man-tended configuration is scheduled to be achieved by late 1996. The two large solar arrays deployed at the top o f the preintegrated truss furnish Freedom with 13.75 kilowatts of electric power.

To accomplish the task effectively, the following sub-

m The various demands for power must be scheduled flexibly.

m Proper management of the various sources of power must be maintained to assure the continu- ous health and operation of the station. Faults in the system must be identified and isolated. Unscheduled power demands must be anticipated and planned for so as to minimize their disruptive effects on the power available to other power demanding loads.

The power distribution network that will actually be incorporated into space station Freedom is currently in design, and, consequently, hands-on experience in operating and managing its various subsystems is nonexis- tent. At the present time, there are no firm strategies for dealing with many of the concerns and issues involved in the allocation and management of space station elec-

tasks must be worked simultaneously:

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tric power. Many of the issues that actual faults, isolating faults, - must be dealt with have, however, been identified in the context of high-level design constraints. facilitates the modeling of fault recovery actions.

Expert system technology recommending corrective actions, and implementing

Numerous projects have been initiated involving the development of knowledgebased systems integrated with actual electric power system breadboards and

complex reasoning in the

system operations

rn The Power-Distribution module is responsible for the recognition of any long term trends that could ultimately affect the health of

context of electric power

t es tbeds with the purpose of demonstrating in a real-time environment the viability of advanced automation approaches for space station onboard and ground-support applications. In addition, NASA Ames Research Center, in cooperation with NASA operation centers, initiated an ambitious long-term research and development program, the Systems Autonomy Demonstration program. The purpose of this program is to demonstrate in a real- time operational setting the benefits and capabilities achieved by applying advanced automation techniques (primarily those incorporating artificial intelligence) to system monitoring, operation, and control.

Distributed System Concept The size and complexity of the space station electric power system clearly favors a distributed software approach based upon a mixture of traditional automation with advanced artificial intelligence and simulation techniques. Such systems can effectively utilize concepts from discrete event simulation to assist in the monitoring, planning and coordination of the actions of the electric power system with a number of related subsystems. Several other factors contribute to the move toward distributed software architectures:

o A general trend toward smaller, localized proces- sors demands the development of distributed systems to support global coordination. A distributed architecture will reduce the software verification burden. Partitioning a system into operationally independent agents facilitates a robust verification effort by allowing agents to be verified independently.

Given that a distributed architecture is a viable approach, there are many issues that must be addressed for the proper operation of the electric power system. These are best described by using the following distinct

The Resource Management module must work with temporal data regarding the various qualitative and quantitative states of resources as well as power generation by each resource and power storage. The Demand Management module will work with temporally dynamic data regarding the states, scheduling, and power requirements of subsystems. The Fault Detection, Isolation, and Recovery module is tasked with identifying anomalies, diagnosing

the distribution hardware.

Role of Artificial Intelligence Technical elements from artificial intelligence, specifical- ly knowledgebased systems, present opportunities for assisting the task of automating the space station electric power system. Knowledgebased or expert systems use domain knowledge and inference procedures to solve problems of narrow domain requiring some degree of expertise. In such systems, knowledge is represented explicitly using AI techniques, and exists separate from the parts of the program responsible for inferencing.

Data and modulespecific knowledge is organized into knowledge areas (KAs), which can be accessed by heuristics. For power system applications, heuristics can be used in concert with knowledge bases and simulation models to generate schedules and automate operations. Listed below are sample heuristics that may be used for formulating decisions based on states of various resources, distribution networks, and subsystems.

R Power allocation: Determine a distribution path for the next subsystem waiting to receive power where the amount of power is determined by the strategic goals of the mission.

m Resource selection: Determine which resource, among those available, will be used to provide power to a given subsystem.

R Interval selection: Determine which time interval is most suitable to provide the necessary power to a subsystem. A trade-off in this situation is stored versus generated power.

Expert system technology facilitates the modeling of complex reasoning in the context of electric power system operations. This is illustrated with the following examples of heuristics, which may be utilized by the appropriate functional area modules mentioned previously.

R If there are intervals that will completely contain the power requirements of a subsystem, the interval that yields the most synchronous schedule over all primary resources is chosen.

R If insufficient power is available from primary resources, backup resources are considered. Backup resources are capable of powering a s u b system but are not the resources of choice. An operation is scheduled on a backup only when it cannot be scheduled on a primary resource in a desired time window. Both a complete fit and a

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An artist’s conception of space station Freedom’s US. and international laboratories and U S . habitation module. The U S . laboratory and habitation module are at bottom let?, the Japanese experiment module is top right, and ESA laboratory module is top left. Shown mated to the node between the U S . lab and the ESA module is the assured crew return vehicle used for emergency returns to Earth of space station crew members. An airlock is mated to the top of the other node, which also is outfitted with a cupola.

partial fit are considered on backup resources, sim- ilar to the approach taken for scheduling on primary resources.

L If a subsystem requires a certain amount of power, stored power may be used for a portion of the experiment until resources are available to generate the necessary power to complete the experiment. Here, trade-offs would be considered between stored versus generated power.

1 If more than one resource is equally well suited to meet power demands of a subsystem, the resource that minimizes setups is selected.

Tuning parameters will also be available that enable the designer to generate schedules closer to objectives in the strategic and operational environment. For example, through these parameters, the designer can indicate a preference toward acceptable levels of stored power, physical paths for supplying power to subsystem, or bal- ancing power generation on different types of resources.

Role of Simulation It is important to provide a capability that can be used to explore the ramifications of selected actions on the various component subsystems. The configuration of the electric power system and other associated subsystems of the space station will be subject to frequent changes. There will be a huge and persistent challenge involved with planning and replanning activities in this dynamic environment.

Discrete event simulation is a common tool for modeling these types of systems. (The text by Pritsker provides excellent background material on the development of discrete event simulations.) simulation models have a complex logical structure that requires scheduling and

sequencing of the many events that occur during the course of the simulation. To describe the use of simulation as a tool for modeling the apportionment of power on the space station, the following definitions are needed:

Primary Events are those events that can be scheduled at a specific point in time during the simulation. An example of primary events is a subsystem being scheduled for service completion. Secondary Events Any event that is not a primary event. Secondary events are not scheduled and occur as a consequence of one or more primary events, one or more other secondary events, or some combination thereof. An example of an event that is dependent on a primary and secondary event is a noncritical subsystem queued for access to the power system (secondary event) and the scheduled increase in capacity of the power system at a later time in the simulation (primary event). State variables are the properties of physical objects that are responsible in part for the overall response of a system or subsystem. State variables may change whenever primary or secondary events occur.

Basic components of discrete event simulations are the information lists, state variables, and control logic for processing the information lists. The control logic utilizes the state variables to affect the primary and secondary events of the model. The situation is represented in Figure 1. A brief description of the most relevant information lists are as follows:

Primary events list A time-ordered list of nodes that represent both the scheduled occurrence of an event and the transaction associated with that primary event. Examples of primary events are the

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Expert Systems Interface

Goals Interface

Schedule

I I

Figure 1. Simulation control logic

scheduled arrival of a new transaction in the model or the scheduled service completion of a transaction. Secondary events list: A list of nodes ordered by transaction priority, where within each priority class, nodes are ordered as a first-in first-out queue. Each node on this list represents a transaction in some phase of being processed, as determined by the source program logic.

The simulator does not explicitly trace individual transactions. A priori efforts to identify dependency relationship (i.e., as a result of the primary event, which secondary events can now occur) are determined by the implementation. Most secondary events are specified by the control logic by means of a source program in addition to state variables as the transaction moues through the program. The steady state segment of the simulation control logic alternates between what is known as the update phase and the scan phase. In effect, during the scan phase the simulator scans the secondary events list on a firstcome-first-serve basis, within priority class for dependent events that a re ready t o occur. Processing a secondary event may cause blocking conditions to be removed as well as to occur. As a result, the scan is repeated until only those transactions that are dependent on subsequent primary and/or secondary events, which have not yet occurred, are left on the secondary events list. When all transactions on the secondary events list are blocked, the simulator returns control to the update phase. In the update phase, the simulation time is advanced to the corresponding time of the earliest event on the primary events list.

The simulator described utilizes real-time information to generate schedules for power generation, storage, allocation, and distribution to meet the strategic goals while honoring power system constraints and taking advantage of subsystems flexibility. The following components of power system automation are the basis for identifying primary and secondary events necessary to model the system utilizing discrete event simulation.

Simulator

i 1 1 I (Schedule) 1

Figure 2. Expert system simulator

Forecasted demand, which is largely determined by strategic goals. Subsystems operating characteristics, which are described by quantitative and qualitative states. Rules for resolving conflicts between the current and future demand and the current and future subsystem operations.

Expert System Simulation (ESS) Simulation modeling and AI techniques share many concepts, and the two disciplines can be used synergistical- ly. Examples of some common concepts are the ability of entities to carry attributes and change dynamically (simulation, with transactions/parameters, versus AI, with frames/slots); the ability to control the flow of entities through a model of the system (simulation, with model logic and state variables, versus Al, with production rules). ESS uses the synergy between AI techniques and simulation modeling with advanced concepts from each of the areas.

ESS requires a knowledge of the various system states, some of which are often described qualitatively. For example, terms like standby, starting, failure is immi- nent, and abnormally low are often used to describe resources and subsystems. Artificial intelligence techniques can be utilized for identifying and coupling primary and secondary events of this type.

Other types of information that can be incorporated into a KA and used by the simulation are illustrated in the process of choosing the source and distribution path for the next subsystem to be activated. When multiple sources or distribution paths are available, the following selection criteria are used:

The simulator/scheduler determines the ideal time for a subsystem to complete its activities. A window of time in which a subsystem can be scheduled is determined for each waiting subsystem. All resources that can provide for the subsystem are eligible for consideration. The window is

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dependent on the power requirements and availability for that subsystem and various user-con- trolled parameters.

ESS utilizes a mixture of state variables and traditional algorithms, combined with expert system technology to infer conclusions about components of the system to generate the schedule for power generation, storage, allocation, and distribution. The relationship between the functional modules, expert system, and simulator/scheduler are shown in Figure 2.

The simulation is iterative in nature and based on the assumption that a power production environment can be divided in planning horizons (stages). The definition of stages is somewhat arbitrary in time, but stages are usual- ly based on the normal termination of subsystem. The important relationship between stages is that the state of the system at the end of one time period is the same as the state at the beginning of the subsequent time period. The simulation schedule is deemed optimal when the strategic goals are accomplished and the states of adja- cent stages are matched. The simulation can be modeled forward or backward in time. Forward or conventional simulation is described in previous sections.

Summary Simply stated, an automated space station power system apportions the available energy to various strategic goals and supervises the subsystems for a fixed planning horizon. An automated power system must consist of

R Forecasted demand, which is largely determined by strategic goals

R Subsystems operating characteristics, which are described by quantitative and qualitative states

IL Rules for resolving conflicts between the current and future demand and the current and future subsystem operations.

Conflicts are caused by strategic goals, which must be adjusted for normal and partially failed operations. All of these components are time dependent.

Traditional simulation techniques combined with rule-based expert system technology is a synergistic method for solving both the apportionment and supervi- sion problem. The ESS approach provides the capability to generate schedules quickly, apportion power and loads, and allocate resources based upon dynamic real- time information. Research in parallel simulation technology is just beginning to reap performance dividends. The fact that functional areas are divided into several modules indicates that there may be additional performance gains by incorporating parallel processing technology into the simulation.

Since its inception, the space station program has had as one of its mandates the responsibility of spinning off appropriate space-developed technologies for the bene- fit of terrestrial industries. Though significant differences exist between the operating environment of the

electric power system on a space station and on a terrestrial power system, technology innovation developed through space-based projects can and will ultimately have an impact on earth-based power systems. As the benefits of automation techniques such as expert system simulation are successfully demonstrated in space based applications, the likelihood of the acceptance and incorporation of these techniques in terrestrial systems will be increased.

For Further Reading J.J Keronen, “An expert System Prototype for Event Diagnosis and

Real-Time Operation Planning In Power System Control,” IEEE Tmnsactions on PowerSytems, Volume 4, Number 2, May 1989, pages 544450.

A.B.B. Pritsker, Introduction to Simulation and SLAM Il, New York Halsted Press (John Wiley & Sons) 1986.

RP. Shulte, S.L. Larsen, G.B. Sheble, J.N. Wrubel, and B.F. Wollenberg, “Artificial Intelligence Solutions to Power System Operating Problems,” IEEE Tmnsactions on Power Systems, Volume PWRS2, No. 4, November 1987, pages 920-926.

D.J. Weeks, and S.A. Starks, “Advanced Automation Approaches for Space Power Systems,” IEEE Computer Applications in Power, Volume 2, Number 4, October 1989, pages 1 3 1 7.

B.F. Wollenberg and T. Sakaguchi, “Artificial Intelligence In Power System Operations,” Proceedings of the IEEE, Volume 75, Number 12, December 1987, pages 16781685.

Z.Z. Zhang, G.S. Hope, and O.P. Malik, “Expert Systems in Electric Power Systems - A Bibliographical Survey,” IEEE Transactions On Power Systems, Volume 4, Number 4, October 1989, pages 1355-1361.

Biographies Scott A. Starks is associate professor of electrical engineering at the University of Texas at El Paso. He received a BS in electrical engineering from the University of Houston in 1973 and a PhD in electrical engineering from Rice University in 1978. Dr. Starks has been involved in space related research activities through research projects with NASA Ames Research Center and NASA Johnson Space Center. While on academic leave from 1985-86, he was initial project manager for the Systems Autonomy Demonstration Program at Ames. Based on his expertise in spacebased automation, he was selected as a member of the NASA Automation and Robotics Panel. He is a member of a number of technical and professional societies and is a senior member of IEEE.

David W. Elizandro is professor and head of the Computer Science department a t East Texas State University. He received the PhD in industrial engineering from the University of Arkansas. He has held faculty posi- tions in the Department of Industrial Engineering and the Department of Computer Science and Engineering at the University of Texas at Arlington. Dr. Elizandro is a recog nized leader in the field of discrete event simulation and has been a consultant in this area to numerous govern- ment agencies and to private industry. He is active in ASEE and ACM and is a senior member of IIE and IEEE.

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