Development of restoration system based on human performance model

Electrical Engineering in Japan, Vol. 117, NO. 4, 1996 Translated from Denki Gakkai Ronbunshi, Vol. 115-B, No. 7, July 1995. pp. 803-810

Development of Restoration System Based on Human Performance Model

KJN’ICHI SHINOHARA, JU”ICH1 NAGATA and HIDEKI SAITOH Toshiba Corp.

ISAO KOZAKAI Chubu Electric Power Co.

AZUMA OHUCHI and MASAHITO KURIHARA Hokkaido University

SUMMARY

A stable power supply is required from power systems as the importance of electrical energy increases. Particularly, in the case of high-voltage systems (500-, 275-, 154-kV bulk power systems), this requirement is high. However, there are cases when power system faults cannot be avoided. Accordingly, it is very important to restore power systems quickly and safely if these failures occur. The characteristics of the power system restoration (its combinatorial aspects, use of knowledge from a wide variety of origins and of different types, number of criteria to satisfy) make it a difficult problem, for which the use of expert systems to generate restoration plans is being considered by many researchers, and promising results have already been obtained. However, most systems are still in the prototype stage [ I ] . One reason seems to be the lack of studies of a support system for knowledge-based behavior (unable to describe with any rule).

This paper analyzes first power system restoration based on the human performance model [6] and discusses the knowledge-based behavior that is a high conceptual level of human performance to solve the problem from the combinatorial viewpoint. Then the application for the trunk line dispatching center is reported. Finally, the relations between knowledge-based behavior and the designed human interfaces are verified with a power networks restoration case-study.

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Key words: Power system restoration; human performance model; restoration procedure; expert system.

Introduction

Recently, social life has become increasingly dependent on electric power, and high-supply reliability is required from electric power systems. This is especially important for high-voltage systems (trunk networks), and one of the vital problems in electric power network operation is speedy system restoration after a failure.

The problem of power system restoration is a complex one, and requires wide knowledge for solution. However, a variety of limitations and interrelated conditions make this quite difficult. For this reason, optimization algorithms and other conventional methods do not offer practicable solutions in a reasonable time.

On the other hand, extensive studies have recently been made concerning the application of expert systems to system restoration problems, and the results are promising. Most of the studies, however, have only reached the prototype development stage [ 11. The following points are considered to be the most complicated factors of the problem, and are crucial to developing a workable system.

(1) When planning system restoration, adequate consideration must be given to the special features of the system, such as re-start and parallel connection to the

ISSN0424-7760/96/0004-0034 0 1996 Scripta Technica. Inc.

system of thermal or hydroelectric generators, buildup characteristics and output capacity of these generators, prior sectioning of the power system for the purpose of failure restoration, etc. [2].

(2) Since the load changes with time of day, season, weather, and so on, as well as during supply failure period, this fluctuation must be taken into account in the system restoration process [3].

(3) Objective fbnctions of system restoration may be represented by economics, reliability, etc., while respective factors are estimated normally with different standards, and interrelated by means of trade-offs. Since improvement of one objective may have a negative effect on others, power system restoration is a multiobjective optimization problem [4].

This problem includes many uncertain factors which makes automatic operation difficult. Therefore, a workable system should be a support system for human operators, rather than a fully automated system. With such a support system, the following interactive functions have been suggested to ensure dialogue with an operator PI:

(i) display of the current state of the power system and progress of restoration;

(ii) operator specifies usable equipment;

(iii) display of target power system as it is being built; and

(iv) working out and display of restoration algorithm up to target system completion.

Such restoration systems have not been put to wide use in spite of the results obtained through extensive studies because of the lack of specific developments aimed at such systems that could take a part of human performance in system restoration, and studies dealing with data processing intended for human perception.

This paper examines a power system restoration course based on interviews with veteran operators. Next, a methodology is explained to distinguish professional experience in the field between an explicitly expressible and documentable part including expertise and rules, and another part that cannot be expressed explicitly. lien a knowledge-based behavior support mechanism and knowledge-based behavior user interface (=I) are proposed.

(. Start I

I Stopgap connection

, Revision of restoration route

Working out of restoration procedure 1 c End )

Fig. 1. Processing flow for restoration.

Finally, an example is discussed using a real power supply control system and a case-study verification.

2. Process Flow of Power System Restoration

Basically, power system restoration means repairing the system equipment, and restoring the system to its original form (before failure). In real life, however, this often means simply minimization of supply interruption by separating the faulty equipment from the power system. In this paper, the restoration process flow is discussed beginning from identification of failed equipment.

Figure 1 shows a brief process flow of the restoration of a power system after a failure. This is a typical flow based on the experience of veteran operators that will be used in the following explanations.

2.1 Power system after failure

AAer a failure occurs in a power network, circuit breakers trip, faulty equipment is isolated from the operational network, and power supply is interrupted within the area covered previously by the faulty equipment. To provide uninterrupted supply for the remaining consumers, a so-called independent restoration schedule is provided. Hence, circuit breakers normally are set to

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“make” at receiving ends, and to “break” at load ends. This group of multiple equipment units connected through the closed circuit breakers is called “block.” while broken circuit breakers between blocks are called “tie points.” Initial phase of power system restoration implies identification of a still operational network, a failed network, and identification of blocks that are crucial for power system operation.

2.2 Search for restoration route

After a failure occurs, the faulty part is separated from the operational part of the power system, a route must be found to connect halted generators and supply- interrupted loads to the operational system. Hence, the following four guidelines may be specified. A pre-failure connection pattern will be referred to as “original” route, while any other routes will be referred to as “(failure- point) bypass routes“:

( I ) making is as close as possible to the original route;

(2) providing power source to generators to be

(3) putting reserve power sources to the best use, restored; and

and restoring as many loads as possible.

Given below are examples of operator experience to implement these basic guidelines:

( i ) use of the original route as a restoration route unless this route has faulty equipment;

( i i ) if there are multiple options for a bypass route, set a priority for that with less tie points;

( i i i ) give priority to routes that are connected to the operational system through tie points with higher voltage;

( iv ) select routes with less step-up points as seen

( v ) give priority to routes with shorter electric from the operational system;

distance (lower impedance); and

( v i ) give priority to routes offering larger capacity reserve.

2.3 Stopgao connection

At this phase, temporary values are set for generator outputs and loads in terms of power balance of the

network determined by search for a restoration route, which is called stopgap connection. This leads to construction of a virtual network based on respective priorities of generators, loads, and bypass routes.

2.4 Measures against overload

Here, power flow calculation is performed for the virtual network formed through the stopgap connection. If overloads are likely to occur, countermeasures are considered in the following order.

( 1 ) Countermeasures based on generator output regulation

For example, if overload-susceptible equipment has an emergency network at its upflow side, the output should be an increase of generators of downflow network. Hence, provisions must be made before new overloads arise, or existing overloads increase at other equipment units.

(2) Network switching

In case of network switching. closing switches and opening switches should be made the same voltage class. At tie points, priority is given to switches of the same power substation.

2.5 Revision of restoration route

In case of network switching taken as an overload countermeasure, it might be necessary to update the restoration route for generators and loads. Thus, based on the state which occurred after network switching (splitting into blocks), the aforementioned steps of search for a restoration route, stopgap connection, and measures against overload are performed repeatedly, and a target network is determined.

2.6 Development of restoration procedure

A procedure is elaborated to make the target network from the existing one. Given below are examples of related knowledge.

( 1 ) Elaboration of procedure

Simultaneous performance of two parallel operations should be made possible. However, only one operation should be allowed at a time at the same power substation or along the same restoration route.

(2) Power source restoration procedure

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Planning Identification Task definition +

Fig. 2. Simplified illustration of three levels of performance of skilled human operators.

-

Generators are restored in priority order. If the given block of the restoration route includes a load to be restored, power is supplied to the load at the same time.

Knowledge-based behavior

3.1 Human performance model

-

Human performance model [a] may be divided into three levels as shown in Fig. 2.

____________________----_-------_--------------

(3) Load restoration procedure

---------------------------.--

(1) Skill-based behavior

Skill-based behavior

Loads are restored in priority order. Power is supplied within available excess output of generators.

Spontaneous perception and action paltern

3. Meshanism to Support Human Performance Model and Knowledge-Based Behavior

Human performance model is a well-known method to analyze the decision-making behavior or skilled operators [6]. Thus, the way operators act when encountering a situation with no appropriate expertise or rules is represented by means of knowledge-based behavior.

Professional knowledge about system restoration includes a part that may be documented and expressed explicitly, and another part that might be called h z y . We investigate putting this fuzzy part into the aforementioned knowledge-based behavior. This section begins with brief explanation about the human performance model described in [a]. Then, a methodology is given to separate the part of system restoration that is appropriate for knowledge-based behavior. Finally, a knowledge-based behavior support mechanism and a knowledge-based behavior user interface (KBI) are proposed.

By skill-based behavior is meant operator actions that ensure smooth system operation even under emergency conditions. These actions are performed unconsciously, with the focus on targets to control, and information available.

(2) Rule-based behavior

By rule-based behavior is meant actions that can be defined in terms of known rules and procedures based on experience.

(3) Knowledge-based behavior

By knowledge-based behavior, is meant operator actions when a situation is encountered with no appropriate rules and expertise available. In this case, the operator defines the target precisely based on analysis of the situation and general goal. On this basis, an effective plan is chosen by the operator by trial and error.

As shown in Fig. 2, after receiving an input perception, the operator divides it into what can be dealt

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with unconsciously, what can be dealt with routinely based on rules and procedures, and what calls for trial- and-error treatment. Then, according to this preliminary analysis, the operator acts.

Normally, expert systems also are called knowledge-based systems because of the great importance attached to special knowledge of veteran operators. This special knowledge may be related to the knowledge-based behavior and rule-based behavior of Fig. 2, and some of it can be actually loaded into an expert system in explicit form. Therefore, it is safe to say that knowledge that can be loaded into the expert system, falls within the range of rule-based behavior

3.2 Application of human performance model to system restoration

Process flow of power system restoration was explained in section 2. Consider, for example, the search phase for a restoration route. A human operator plans a bypass route based on good knowledge of the system such as voltage, capacity and other characteristics of equipment, restoration priorities of generators and loads, etc. This knowledge about equipment characteristics or restoration priorities corresponds to the perception input of Fig. 2. and knowledge about pre-failure system configuration, i.e., original route, corresponds to skill-based behavior, while comprehension of block structure, of spatial relations between operational part and failed part, and of restoration to the original route, corresponds to rule-based behavior.

If the original route cannot be restored, a bypass route is planned. In this case, there are some fuzzy rules such as making the bypass route as close to the original one as possible, and some other competing rules such as preference for a bypass route with less tie points and preference for a bypass route with high-voltage tie points. The first of these two competing rules is aimed at fast completion of the system restoration procedure, while the latter aims at higher reliability of the restored system. Thus it is difficult to establish a universal solution of this conflict. Such fuzzy guidelines and conflict solutions that cannot be expressed explicitly are treated as knowledge- based behavior as shown in [6].

Table I gives analysis of all phases of the restoration process based on the method under consideration. Items classified with rule-based behavior in Table 1 are rules and expertise that can be documented explicitly. By contrast, classified with knowledge-based behavior

are those rules and expertise that cannot be defined and documented explicitly. Therefore, Table 1 may be considered to include two parts: the first, rule-based behavior, can be automated using computers, while the second, knowledge-based behavior, implies using computers not to automate operation, but to support the operator in trial-and-error actions.

3.3 Knowledge-based behavior support mechanism and user interface KBI

As was explained above, the knowledge-based behavior cannot be documented on the basis of expertise and rules expressed explicitly, and relates to multiobjective optimization problems which incorporates multiple evaluation standards interrelated with trade-offs. To perform this knowledge-based behavior using computers, objectives and evaluation criteria of the operator should input into the computer and then the computer output should be obtained to correlate these objectives and evaluation criteria. The operator objectives and evaluation criteria may be described as follows:

( i ) eliminate power supply interruption as soon as possible;

(ii) restore major loads as soon as possible; and

(iii) determine the proper restoration route and bypass route.

Therefore, a necessary condition for a knowledge- based behavior support mechanism and user interface is to allow the operator to find a persuasive solution while changing objectives and criteria through interaction with the computer. We propose the knowledge-based behavior support mechanism and user interface as shown in Fig. 3 .

4. Development of Restoration System

Figure 4 shows configuration of the failure restoration task within a trunk line dispatching center (Chubu Electric Power Co.).

The function of estimated calculation of overall demand deals with forecast of the current power demand, and the calculated value is used as target value for after- failure load in the system restoration procedure.

The function of working out of restoration guidance offers the operator an outline of the bypass route and restoration route, and in doing so, displayed on the CRT

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Phase of system restoration

Input in form of perception

Symbols of circuit

Messages about breakers

protection relay operation

Symbols of circuit breakers

Capacity and other specifications of equipment units

Restoration priorities of equipment units

Estimated demand curve Operatiodstop state

of generators Restoration priorities

of equipment units

Evaluation of system after failure

Skill-based behavior

Evaluation of circuit breakers’ status

Evaluation of failed network

Evaluation of original route

Estimation of emergency network

Search for restoration route

Stopgap connection

Measures against overload

Working out of restoration procedure

Table 1. System restoration analysis based on human performance model

Capacity of all equipment units

Restoration priorities of equipment units

Buildup characteristics of generators

Minimum unit of load restoration

Evaluation of prohibited items within working procedure

screen are a list of power substations to be restored on the source side, a list of lines to be employed for power transmission, a list of paralleled power plants and substations, etc.

The function of the procedure of the working out of restoration offers the operator a restoration procedure with regard to restoration priorities of generators and loads, and in doing so, a detailed chart, including time

Rule-based behavior

Evaluation of blocks

Evaluation of operational network

Evaluation of blocks

Evaluation of operational network

Load forecast Specifying gen-

erators to be restored

Evaluation of necessity of overload measures

Elaboration of working procedure

Evaluation of excess power of generators

Knowledge-based behavior

Planning of failure bypass route

Outlining of restoration route

Planning of measures against overload

Evaluation of restorat ion priorities

Prediction of how range of intempted supply will change

schedule, lists of power stations, substations, generators, and transmission lines involved, as well as individual operations, etc., is displayed on the CRT screen.

The function of plotting a supply interruption graph offers a polygonal line diagram to show the development of supply intemption based on implementation of the aforementioned restoration procedure. Moreover, the

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, I I I I I I I I I I I I I I I I I I

-

c+l HBI

Estimated calculation of overall demand

Power flow calculation Training simulator

* Supply intemption estimation

__---_-- --------- 1 I

Evaluation by operator’s Evaluation by default settings, I

objectives and criteria or by objectives and criteria indicated by operator I

I - - - _ - - - - -k - _ _ - - - - _- J

Graphic representation of restoration framework

indication of operator’s objec- t Graphic representation

visory dispatch system I l l I of restoration details - I I , I

I I I I I I I I I I I I I

Fig. 3. KBI conceptual chart for knowledge-based behavior.

Operator

4 User interface G=l I Basic functions:

L Functions related to localization of faulty equipment and system restoration:

* Localization of faulty equipment Working out of restoration guidance Working out ofrestoration procedure Ploaing of supply intemption graph Storage of restoration profile

H

Expert system

Fig. 4. Configuration of the restoration task for the dispatching system.

operator is provided with a user interface allowing a change of restoration priorities for specific localities.

developed. Here, support for knowledge-based behavior provided by these functions is verified through a power system restoration case study.

5. Case Study of Power System Restoration Fig. 5 shows a power network in its normal state

before failure; A, B, C, denote power substations with buses of 500 kV and 275 kV, while with B,, multiple

In the previous section, sample functions were cited of the power network restoration support system we

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I 500kV ‘ Bss Ass

500kV

275kV 275kV 275kV

Fig. 5. System configuration before a fault occurs.

I I *F I < -

Fig. 6. System configuration after a fault occurs.

275-kV power substations are connected in a loop. Besides, L, through L, are loads, G, and G2 are generators, while “x” symbol marks broken circuit breakers. If a failure occurs on the 275-kV bus B, all the 275-kV power substations intermpt their supply, and due to the independent restoration schedule, the power network becomes as shown in Fig. 6.

In Fig. 6, there are two bypass routes from C, to 0, and from A, to J,, and there are further multiple restoration routes to restore E, F, G, H, and I,. From these multiple options, a single combination of bypass route and restoration route is selected using expert system. This expert system contains a part of the operator knowledge that can be represented with definite rules as

explained in section 2. The expert system, while checking for overload, revises the restoration route as necessary until a single solution is achieved.

Shown with errors in Fig. 7 are the bypass route and restoration route, and Fig. 8 gives sample messages displayed as restoration guidance.

The restoration guidance of Fig. 8 tells the following. Taking the C,-D, line for bypass route, fiom C, to D, and further on restoration power source is sent. This power is sent to generator G, through D, E , F,, and then through G, H, I,. This restoration guidance offers an outline of the bypass route and restoration route which allows the operator to grasp possible directions for restoration.

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Using C,,-D,, line from C,, restored are

Using Ar,c-4tr line from A,, restored is J,,.

Fig. 8. Restoration guidance.

Here, the order of load restoration is not defined. For example, whether the load L, will be supplied with power after G, is restored or directly from G, depends on the restoration priority of L , and buildup time characteristic of GI.

When the bypass route and restoration route are chosen, as shown in Fig. 7, the expert system produces the restoration procedure with regard to restoration priorities for generators and loads along those routes. For example, with loads, restoration priorities, and equipment capacities as shown in Table 2, the restoration procedure is as shown in Fig. 9. The record “0:03:00 D, C,-D, line IN” means that at the time of 0:03:00 counted from the failure occurrence, power substation D, becomes connected in parallel to the C,-D, line. The record “supply to load L , 200 (MY” means that power of 200 MW is supplied to load L , .

With this restoration procedure, G, becomes connected in parallel at 0:03:30, and after it builds up, at 0:23:00 power supply commences to L,, L,, L,. This delay of about 20 min is necessary for G, to achieve the rated output of 300 MW after being connected to the

0 : 03 : 00 D,,

A ,

0:03:30 Ess

0:04:00 F, 0: 23 :00

C,-D, line supply to load L, A,-J, line supply to load L, 4- K s . 7 I i ne G, supply to load L,

FSS-GJS

supply to load L,

supply to load L, supply to load L, supply to load L,

Fig. 9. Restoration procedure.

IN (200 MW) IN (200 MW) IN IN (200 MW) (100 MW) IN (200 MW) (30 MW) (20 MW)

power network. In this manner. the operator can grasp the sequence and time schedule of the load restoration planned by the expert system.

By the way, load restoration priorities tend to change depending on local social events, and so on. Assume that in view of such a local situation, the operator is not satisfied with what is seen in Fig. 9, and wishes to set top restoration priority for L,. After inputting this in the expert system, the operator is offered alternative plans for restored network, restoration guidance, and restoration procedure as shown, respec- tively, in Figs. 10 to 12. With this alternative plan, load L, acquires top restoration priority, and power supply is provided from A, through splitting the bus G,s. Figure 13 offers comparison between the initial and alternative plans in terms of supply interruption graphs.

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Table 2. Load, restoration priority, equipment capacity

'Restoration Equipment designation Load dcsig- value (MW) riori

nation

L1 200 1 C,-& b e

LZ 100 3 a,-&, line

h 200 2 KS - F,, line

L, 200 4 FSs- G,, line

L 30 5 Ass-Jss line

h 20 6 Jss - G,, line

Ll 200 7 generator I

capacity (Mw)

500 400 300 300 400 300 300

I t I t r T T css Ass

Y

(Bottom priority

Iss

Fig. 10. Another system configuration after restoration.

Using C,-D, line from C,, restored are

Using A,-J, line from A, restored is J, G, bottom priority.

Fig. 1 1. Alternative restoration guidance.

Summing up the aforementioned, the operator has to make a final decision about a restoration plan through comparison and evaluation of the following points.

GI

(1) The alternative plan offers restoration of L, to occur 20 min sooner.

(2) As to the restoration route, the alternative plan suggests using J,-G, line while splitting the bus at G,.

(3) The alternative plan offers faster recovery of power supply.

Thus, this case study confums the effect of the knowledge-based behavior support mechanism and user interface KBI.

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0:03:00 A ,

0:03:30 G,r, G,,

i According to restoration procedure of Fig. I2 I -___- -_ 1 _ - _ _ _ _ _ _ _ _ _ _ _ _ - _ _ _ _ _ -

0:04:00 E,, G:

~

0.04:30 F , 0:23:00

A,-J, line B,, sect Jsx-G,

supply to load L, supply to load L, CIS-0, line supply to load L , D,,-E,,

supply to load L, supply to load L, FSS-G,, supply to load L, supply to load L,

IN OFF IN (200 MW) (200 MW) IN (200 MW) IN IN (200 MW) (100 MW) IN (30 MW) (20 MW)

Fig. 12. Alternative restoration procedure.

5. Conclusions

The reason for the unavailability of practicable solutions for power system restoration is attributable to insufficient R & D in the field of knowledge-based systems and user interfaces. To solve this problem, we did the following:

( I ) offered a methodology to separate the part of the system restoration procedure that can be entrusted to computers, and performed analysis of its application to power system restoration;

(2) offered a user interface for solving problems that cannot be entrusted to computers, by way of coopera- tion of the human operator and computer, and tested it in a real power system.

The results of (1) allow relieving veteran operators in planning power system restoration, and speed up of the development.

As to (2), real world tests found operator satis- fact ion.

Chubu Electric Power Co. and Toshiba Corp. have been working on a joint project of power system restoration focused on trunk lines since 1987. The results have been incorporated in the trunk-line dispatching center that went into operation in January 1994.

In this paper, a simple power network is used to facilitate explanations; the actual trunk-line dispatching

Fig. 13. Interruption of the supply of electrical power.

center, however, deals with the large-scale Chubu power network including the whole subsystems of 500 kV and 275 kV as well as a part of the 154-kV network. The examples of restoration guidance and supply interruption graphs were given in section 4, but those have been produced, in fact, under strong demand from veteran operators.

As shown in Fig. 4, the system includes a training simulator which allows arbitrary power networks to be imitated and a variety of restoration algorithms to be investigated. If the system configuration changes due to maintenance of equipment or for some other reasons, operators should update promptly their restoration procedures. Such updates are supported by the proposed system which means that the system is useful not only during failures but also in normal operation.

REFERENCES

1.

2.

3.

4.

5 .

CIGRE SC 38 WG 38.08 TF04. The Use of Expert Systems for Power System Restoration. Final Report (Draft), 1994. M. M. Adibi et al. Power system restoration-A task force report. IEEE Trans. Power Systems, Vol.

M. M. Adibi et al. Special Consideration in Power System Restoration-The Second Working Group Report. 93 WM 202-2 PWRS, 1993. Special committee for power system restoration. Restoration of power systems after failure. Trans. I.E.E. (Part II), No. 354, p. 5 5 , 1990. Y. Kojima et al. Development of a guidance method for power system restoration. IEEE Trans. Power Systems, Vol. 4, No. 3, 1219, 1989.

PWRS-2, NO. 2, 271, 1987.

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6. J. Rasmusen. Skill, Rules, and Knowledge: Signals, Signs, Symbols, and Other Distinction in Human Performance Models. IEEE SMC-13-3, No. 3,257, 1983.

7. I. Takeyasu et al. An Expert System for Fault Analysis and Restoration of Trunk Line Power Systems. Symposium on Expert Systems Applica- tion to Power Systems, Stockholm-Helsinki, 1988.

AUTHORS (from left to right)

Jun’ichi Shinohara graduated in March 1972 from Hokkaido University, Electrical Engineering Department. Since April 1972, he has been with Toshiba Corp. He has been engaged in R & D of supervision and dispatch systems for power networks, expert systems, and so on. In 1992, he received the Trans. I.E.E., Japan Prize. He is a member of IEEE.

Jun’ichi Nagata completed graduate studies at Niigata University, Electrical Engineering Department in March 1983. In April 1983 he joined Toshiba Corp. He is engaged in R & D of supervision and dispatch systems for power networks.

Hideki Saitoh graduated in March 1987 from the University of Tokyo, Fundamental Science Department. In April 1987, he joined Toshiba Corp. He is engaged in R & D of supervision and dispatch systems for power networks.

Isao Kozakai graduated in March 1963 from D i d o Technical College, and joined Chubu Electric Power Co. He is engaged in development and maintenance of power supply and dispatch systems. Chief of control systems section.

A m a Ohuchi received his Ph.D. degree in 1974 from Hokkaido University. He is engaged in research on information systems, applied A1 systems, medical systems. He is a member of A1 Society; The Institute of Electronics and Communication Engineers of Japan; Society of Instrument and Control Engineering; OR Society Japan; Society of Medical Information; Society of Hospital Management; IEEE-SMC.

Masahito Kurihara received his Ph.D. degrez from Hokkaido University in 1980 and became an Assistant Professor there. He is engaged in research on systems engineering, and other fields. He is a member of IECE Japan; Software Development Society Japan; OR Society Japan; IEEE.

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Development of restoration system based on human performance model