674 IEEE Transactions on Energy Conversion, Vol. 8, No. 4, December 1993

.4N OPERATION SUPPORT EXPERT SYSTEM BASED ON ON-LINE DYNAMICS SIMULATION AND FUZZY REASONING FOR STARTUP SCHEDULE OPTIMIZATION IN FOSSIL POWER PLANTS

H. Matsumoto Y. Eki S. Nigawara A. Kaji

Hitachi Research Omika Works Hitachi Works

Hitachi, Ltd., Laboratory

4026 Kuji-cho, Hitachi-shi, Ibaraki-ken, 319-12 Japan

Abstracr : An expert system which can support operators of fossil power plants in creating the optimum startup schedule and executing it accurately is described. The optimum turbine speed-up and load-up pattem is obtained through an iterative manner which is based on fuzzy reasoning using quantitative calculations as plant dynamics models and qualitative knowledge as schedule optimization rules with fuzziness. The rules represent relationships between stress margins and modification rates of the schedule parameters. Simulation analysis proves that the system provides quick and accurate plant startups.

INTRODUCTIO N

Recent growth in both capacity and complexity of fossil power plants demands advances not only in machine design, but also in plant operation system. On one hand, in accordance with the larger share nuclear plants are assuming as a power source, fossil plants are required to go into heavy duty operation, involving frequent startup and shutdown, and quick load following. Consequently, time reduction for startup, accuracy of startup completion time, and safe and economical operation have become important needs. One of the decisive factors for quick startup is thermal stress which develops in turbine rotors. An important topic in plant operation systems, then, is finding a way to reduce the startup time without shortening the machine lifetimes.

To meet this requirement, off-line scheduling and on-line control methods are used as state-of-the-art techniques. As for the off-line scheduling method developed by Hanzalek and Ipsen[ 11, a startup schedule is created through a metal matching chart using steam and metal temperatures just before startup. In the on-line control method developed by Livingston[2], speed-up rate and load-up rate are periodically optimized using the estimated rotor stresses from measured temperatures of turbine casings. In order to improve the accuracy and the stability of stress control, one of us has developed a turbine automatic startup system (HITASS)[3] which is based on predicted stresses using a plant dynamics model which consist of an autoregressive boiler model and a turbine heat transfer model. Through the off-line scheduling method, while punctuality of startup can be obtained, time required for startup tends to be prolonged and while the on-line control method effectively reduces the time needed, accurate estimation of startup completion time is difficult.

93 WM 143-8 EC by the IEEE Energy Development and Power Generation Committee of the IEEE Power Engineering Society for presentation at the IEEE/PES 1993 Winter Meeting, Columbus, OH, January 31 - February 5, 1993. Manu- script submitted August 11, 1992; made available for printing December 28, 1992.

A paper recommended and approved

M. Tokuhira Y. Suzuki

Yokosuka Power Station Goi Power Station

The Tokyo Electric Power Co., Inc. 1-3 Uchisaiwai-cho, 1-chome, Chiyoda-ku, Tokyo 100 Japan

It is important, but difficult to solve these two problems at the same time. Additionally advances in man-machine systems which Can assess startup characteristics when the schedules are modified in accordance with commands from the control center or plant operators have become strong needs. It is difficult to find some solutions for such problems within the scope of conventional computer control technology, because of complicated physical relationships among process variables in power plants.

To meet these requirements, we focused on advanced computer technology, especially with control computers as hardware, and knowledge engineering as software. Knowledge engineering which has been advanced mainly for medical applications is now being viewed with interest for process control applications. Knowledge engineering is looked upon as a promising technology for solving problems by utilizing knowledge and the way experts, such as operators, plant engineers, and control engineers, think.

In this paper, an expert system is described which consists of functions of startup scheduling and its on-line modification to which fuzzy reasoning is applied in order to reduce the work burden of plant operators. The system effectiveness is verified through simulation analysis.

SYSTE M DESIGN CONC EPT

Obiectives

follows. This expert system is designed to realize four major objectives as

(1) Reducing startup time (2) Reducing schedule execution error (3) Improving operational flexibilities (4) Lightening operators' work burden The startup schedule should be as short as possible and executed

accurately. It should allow flexible modification when unexpected schedule errors and anomalities occur during startup sessions. Also, the opertors work burden should be lessened.

m a t i o n of critical stress There are many operational constraints in power plants. Turbine

thermal stress is an especially decisive factor for reducing startup time. Stress occurs on the rotor surface and rotor bore at the labyrinth packings behind the first stage of the HPT (High Pressure Turbine) and IPT (Intermediate Pressure Turbine). In these areas, a heavy heat flux appears from the surface toward the bore during the startup period, because of leaked steam having a high temperature and high velocity. Then, the more rapid the startup is, the greater the stress developed, and the more serious is its effect on shortening the turbine service life. It is very important to know and manage these stresses in connection with startup schedules.

S P Here, we think about an optimization problem of the startup

schedule in relation with the turbine stress dynamics. After the boiler

0885-8969/93/$03.00 0 1993 IEEE

is light off, the turbine is rolled off, and its speed increases toward the rated speed. After that, the generator is paralleled in to the power system, and loaded up toward a target level. Then, the startup session is completed. The word optimization is used here in the meaning of making the shortest schedule without exceeding any stress limit as the operational constraint.

In general, experienced operators and plant engineers are able to make an appropriate modification of the schedule, if a stress pattem is provided before hand. They may reduce time periods where stress margins exist, and may extend them where excess stress appears. But how can this system solve the problem automatically?

System concept A well known mathematical method like nonlinear programming

using dynamics simulation is too time consuming, and impractical for getting the optimum solution by ordinary computers, because plant properties are large scale, dynamic, nonlinear and quantitative. Also the mathematical method cannot easily handle object knowledge efficiently. So, dynamics simulation and modeling of human expertise such as heuristic, fuzzy and qualitative thinking process are integrated by an AI technique in order to obtain quick solutions. In this approach, fuzzy reasoning is introduced into a qualitative evaluation of stress patterns and schedule modifications.

FUNCTIONAL STRUCTURE OF THE SYSTEM

The functional structure of this system is shown in Figure 1. A schedule which can minimize the time required for startup and deviation of the startup completion time is created from the startup scheduling part. The operators can be supported in making startup schedules and guided in operations to cope with unexpected schedule errors and stress monitoring and prediction control by the operation support part. The deviation means a time difference between the time commanded by the control center and the actual startup completion time. A mathematical model of plant dynamics is introduced to reflect uncertainties of plant dynamics, which depend on the initial conditions, onto the startup schedule accurately. After initiation of plant startup along with the schedule, the operators can be guided to modify the schedule appropriately when its modification is necessitated after the turbine speed and load are held by unexpected causes. In the major parts of the system, fuzzy reasoning is introduced to represent the knowledge and the way experts think, and to utilize them to improve computational abilities.

The startup scheduling part consists of functions for initial scheduling prior to schedule optimization, schedule optimization, and

675

rescheduling just before turbine roll off. Quantitative startup characteristics of the plant which is assumed to be started in accordance with a given schedule can be calculated through a dynamics model. The initial schedule is created in accordance with initial conditions of the plant using cooling curves. The initial schedule can hasten the convergence of schedule optimization. In the optimization process through fuzzy reasoning, turbine thermal stresses obtained from the dynamics model are qualitatively evaluated, and the schedule is modified. After the modification, startup dynamics are calculated and evaluated again. Through this iterative manner, fast convergence to the optimum schedule makes it possible to predict startup characteristics accurately, using the dynamics model with complex equations. In the same manner, fuzzy reasoning is adopted to the function for rescheduling.

herahon SUDDOQ

The turbine stresses are periodically predicted and controlled by the function for monitoring and prediction control during the actual startup process. The turbine speed or load is held when an unexpected excess stress appears. The unexpected speed or load hold is released after the stress goes to within the allowable limit.

Then, the startup schedule is appropriately modified through the function for schedule modification. The modification helps to minimize the error of startup completion time between the original schedule and the schedule after the unexpected hold occurred. The modification rate is determined by means of stress prediction using the dynamics model.

Man-machine interface The system is provided with a user-friendly man-machine

interface by interactive graphics displays. The displays support the operators with information which shows trends of controlled process variables, process of schedule optimization, schedule execution, schedule modification, and schedule optimization rules stored in the knowledge base.

ALGORITHM OF STARTUP SCHEDULING

Initial scheduling As the initial schedule is used for the first step in the iterative

optimization process, it should be as close to the optimum point as possible. Here, the plant stoppage period designates the time difference between the parallel in time demanded from the control center and parallel off time when the generator is separated from the power system. Then the specific two startup modes are selected from four modes which are pre-defined in accordance with the length of the stoppage period. The pre-defined four modes are cold mode, warm

Startup Scheduling

Man-machine Interface

Fig.1 FUNCTIONAL STRUCTURE OF THE SYSTEM

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mode, hot mode and very hot mode. Schedule parameters which define each startup pattern from the boiler light off to the load-up completion are given by the following.

t 1 : Boiler startup time(from light off to roll off) tz : 1st speed hold time t 3 : 2nd speed hold time t 4 : 3rd speed hold time

t 5 : 1st load hold time t 6 : 2nd load hold time t 7 : 3rd load hold time

Speed-up and load-up rates are also pre-defined. The two steps of schedule parameters which belong to the selected two modes are linearly interpolated or extrapolated in accordance with the stoppage period. After determination of the initial schedule parameter, the boiler light off time can be calculated. Then, initial process values which correspond to the boiler light off time are estimated, using the cooling curves. The initial values which are used here are super heater outlet steam temperature, reheater outlet steam temperature, main pipe metal temperature, reheat pipe metal temperature, water wall inlet liquid temperature, economizer inlet liquid temperature, high pressure turbine metal temperature, intermediate pressure turbine metal temperature, and main steam pressure.

The estimation of the initial process values is as follows. The process values at the present time TOP when the startup schedule is going to be made are estimated using the following equation which represents the cooling curves.

TOP = ( TSD - T A ) EXP ( - - )+TA (1) tc TSD : Temperature at parallel off TA : Ambient temperature top : Present time t SD : Parallel off time tc : Cooling time constants

The process values at the boiler light off TIC are estimated as follows.

TIC = ( TSD - T A ) EXP ( - - ) + T A tc

0 if (m2 is PM and m is PS)

The process values at the boiler light off TIC can also be estimated without consideration of TSD and tsD, using equation( 1) and (2) as follows.

TIC = (TOP - T A ) EXP ( - - ) + T A (3) t c t IG : Boiler light off time

The main steam pressure is obtained in the same manner as equation( 3).

Schedule outimization Figure 2 shows the procedure for schedule optimization.

Schedule parameters ti are time periods which define the startup pattern from boiler light off to startup completion. The optimum schedule means the shortest schedule which makes the summation of ti a minimum, keeping the turbine stresses within the allowable limit during the whole startup process. Textbookish optimization algorithms which need hundreds of iterative calculations for optimization of seven parameters are not practical. A heuristic optimization applying fuzzy reasoning is attempted to obtain rapid convergence. By knowing the stress pattern which develops in the metal of the turbine rotors, experienced operators can modify the startup schedule easily. Such heuristic knowledge is used for the schedule optimization process.

The startup process is divided into seven time sections as Figure 2 shows. The minimum stress margins mi for each section are calculated. The margins are used to obtain the stress pattern. The schedule optimization rules give fragmentary knowledge for determining schedule modification rates in relation to stress patterns. In this paper, this system is described for a case in which the parallel in time is commanded by the control center. But this system can also easily respond to other time commands, such as turbine roll off time, startup completion time, etc.

Corresponding to the initial schedule, the turbine stress is obtained from the dynamics model. The stress margins are evaluated qualitatively using membership functions to extract qualitative features of the stress pattern. There are 150 rules used for determining the qualitative modification rates of the schedule parameters Dti in qualitative relations with Ms( j ) or MB( j ). Here, Ms( j ) and MB( j ) are respectively defined as smaller stress margins of the rotor surfaces and bores between the high pressure turbine and the intermediate pressure turbine. Then, each stress margin is classified into a

Modification Rate Calculation

( Fuzzy Reasoning )

Dti

I Turbine Stress Calculation I

I n a n Liaht off Roll off Parallel In I

ml is PB o m 2 is PM 0 m 3 is PS

1 Qualitative Evaluation

Fig.2 SCHEDULE OPTIMIZATION PROCEDURE

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Table 1 EXAMPLE SCHEDULE OPTIMIZATION RULES

qualitative expression by membership functions. These qualitative features of stress pattem are compared with the schedule optimization rules by fuzzy reasoning. An example of the rules is as follows.

IF ( Ms(5) is PB and Ms(6) is PM ) THEN ( Dt4 is NM and Dts is NM and Dt6 is NS )

PB, PM, PS, ZO, NS, NM, NB are symbols attached to the They have the following qualitative membership functions.

meanings.

PB : Positive Big PM : Positive Medium PS : Positive Small ZO : Zero

NB : Negative Big NM : Negative Medium NS : Negative Small

After pattern matching of the qualitative features of stress pattem and schedule optimization rules, qualitative schedule modification rates are defined through membership functions. The plant startup characteristics, according to the modified schedule, can be predicted again through the dynamics model. Through the iterative manner described before, fast convergency to the optimum schedule can be expected. Convergence of the optimization is judged when the reduction rate of startup time by schedule modification becomes sufficiently small.

Table 1 shows some example schedule optimization rules which are actually used by the fuzzy reasoning. The rules in the table are used for determining the modification rates of three schedule parameters in qualitative relations with two stress margins. Taking rule No.51 for instance, if the stress margins Ms(5) and Ms(6) are PB and NS, respectively, then the modification rates D k , Dt5 and Dt6 are defined as NM, NS and PS, respectively.

The boiler light off time may be changed in accordance with each iterative optimization process. The new initial process values nc' at the new boiler light off time tIG' are estimated in the same manner as equation(3). The values are represented as follow.

Besides hold times of speed and load, changing rates of speed and load can also be introduced as extended modification parameters for the schedule optimization which is already described.

Thermal stresses of the turbine rotors are generated by temperature differences between the rotor surface and the bore. If the rotor temperature distributions are known, the stresses can be calculated. But it is difficult to measure the rotor temperatures directly, and the corresponding casing temperatures do not always coincide with the rotor temperatures. Furthermore, even measurement of the steam temperature behind the turbine first stage is difficult because of its mechanical structure. Considering these difficulties in measuring the temperatures, a new calculation method for the dynamics model of the rotor stresses is developed. Figure 3 shows the main steps in the calculation. Both current stresses and future stresses are obtained through the dynamics model. A detailed calculation procedure of the model can be found in reference[3].

Rescheduling The optimum schedule is intended to be able to keep the stresses

within their limits. Even then, there are some possibilities of deviations between predicted process dynamics and actual ones. Because of the deviations, an unscheduled speed hold or load hold might be caused by excess stresses generated after the turbine is rolled off. In order to avoid the phenomena and maintain the optimum schedule, the startup schedule is reviewed by the function for rescheduling just before turbine roll off. The schedule optimization procedure described before is adopted to this function in the same manner using current status as the initial conditions for the dynamics model.

Stress monitoring and control In the function for stress monitoring and control, the speed or

load is controlled along with the startup schedule, as well as simultaneous stress monitoring of predicted stresses. When one of the predicted stresses happens to reach the limits, speed or load hold control is applied. In this way, the predictive stress calculation is effective in avoiding excess stresses. During the unexpected speed or load hold, stresses are reduced enough to raise speed or load again. Then, the startup schedule is appropriately modified by the function for schedule modification.

I

Surface Stress I Press.

Stress Calculation + Bore Stress

Temp. Main Steam Temp. Main Steam Press. 4 Distribution 9 Calculation

! Reheat Steam Temp. - I Rotor S L s Calculation] Till----.-------------------------------------

I" > Predicted Surface Stress Expected Speed Change Calculation . > Predicted Bore Stress Expected Load Change

Fig.3 MAIN STEPS IN ROTOR STRESS CALCULATION

678

Schedule modification An unexpected hold of speed or load is caused by anomalous

conditions of the turbine such as differential expansion, vibratory level, condenser vacuum level. After the anomalous conditions are recovered, the original schedule with the unexpected hold is released and appropriately modified according to the following steps.

(Step 1) Stress dynamics is predicted with the assumption that the, turbine speed or load is raised along the modified schedule which neglects the unexpected hold time tUH, as shown by the dotted line in Figure 4. The minimum stress margin Si during the whole startup process is calculated.

(Step 2) Stress dynamics is pr,edicted with the assumption that the startup session is continued along the schedule which is equally shifted as the unexpected hold time tUH, as shown by broken line. The minimum stress margin S2 is calculated.

(Step 3 ) If the stress margin Si has a positive value, the schedule used in step 1 is adopted for actual use. On the other hand, when the SI has a negative value, the startup schedule is created using a modification factor k which is obtained by interpolation as follows.

The results of the modification are shown by the chain line in Figure 4. The modified schedule can be expected to make the stress margin positive and a minimum. Consequently, the error of the startup completion time is expected to be a minimum

d 7 Speed

Hold Release Unexpected Hold

Time

tl Modification Facto-

SI SI - s2

k =

S 1, S 2 : Stress Margin

Fig.4 SCHEDULE MODIFICATION

HARDWARE SYSTEM

The expert system is installed in the hardware system, as one of applications of an expert system shell, HITREX, the Hitachi Tree- Based Realtime Expert System[4]. The HITREX is specially tailored for fossil power plant engineers, with full system support for easy, simple installation and automatic linkage to process variables. The system ensures efficient plant operation in a wide range of system configurations from large to small. The system consists of a control computer (HIDIC-90/25) for on-line use and an engineering workstation (ES330) for off-line maintenance of the knowledge base. Implementation of the fuzzy rules is fast and easy through an on- screen matrix in which logical relations can be simply defined. Input

variables used in fuzzy reasoning can be easily assigned and modified through interactive screen dialog.

SIMULATION STUDY

Hardware svste m for simulation A simulator which is developed to verify and evaluate the system

consists of a control computer (HIDIC-90/25) and an engineering workstation (ES330) the same as the expert system mentioned before. Causes of schedule errors can be entered into the dynamics model installed in the control computer through the keyboard of the engineering workstation.

Schedule outimization and resc heduling A typical example of the simulated schedule optimization process

is shown in Figure 5. In this figure, numbers correspond to the iteration steps, where the seventh is the converged optimum schedule. Time required for the startup is prolonged by 24 minutes compared with the initial schedule in order to reduce the stresses within the limits. The modification parameters adopted in this case are hold times of speed and load. In the figure, only the larger stresses between HPT and IPT are shown, but actually in the simulation, four stresses are taken into consideration.

37. ...............................................................................................................

B.B

__.__... ~-. .. ........................................... .... r ..........................

Fig.5 SCHEDULE OPTIMIZATION PROCESS

K W W 7 1 4 1 ibii,.L : i + t ? -

............................................................................................................... Bo= --O.W < K G / M 2 >

SURFACE -0.00 < K G / M M Z > E?.

Fig.6 RESCHEDULING PROCESS

679

(Stoppage Period)

Figure 6 shows a simulated rescheduling process just before the turbine roll off. Time delay of the startup completion is reduced to 4 minutes even though the time for turbine roll off and parallel in are delayed by 5 minutes and 7 minutes, respectively.

Simulation results prove that the fuzzy reasoning works effectively for schedule optimization and accurate startup.

(8 h) 1 (32 h) (150 h)

Figure 7 shows a simulated schedule modification when the parallel in time is delayed by an anomalous step change in the main steam temperature by 1O'C after the turbine reached the rated speed. Time delay of the startup completion is reduced to only 3 minutes even though the parallel in time is delayed by 10 minutes, because of some stress margins which exist during the loading up process. Simulation results under the assumptions that deviations from normal temperatures of the main steam, the reheat steam, and the turbine metal exist, prove that the fuzzy reasoning works effectively for rescheduling and accurate startup.

Conventional Mothod Proposed Method

..... ~:::.;.\ -25, ............................................. :>...>e?,-.dzc.-:-: ................

t a 164 287 565 f b 164 273 488

Fig.7 SCHEDULE MODIFICATION

Time reductr 'on for startuD In comparison of required startup time between conventional

methods and the proposed method, evaluations under the same stress level developed at the maximum point are necessary. Here, startup time is evaluated along the following steps.

(Step 1) The maximum stress level is obtained through a simulation of the conventional method which introduces a metal matching chart to create startup schedules as described in reference[l].

(Step 2) The optimum schedule is obtained through a simulation of the proposed method. In this step, speed and load hold times are modified as schedule parameters under a stress limit which is the same level as the maximum stress obtained in the step 1.

(Step 3) The optimum schedule is obtained through the extended proposed method. In this step, besides speed and load hold times, changing rates for speed and load are also modified under the same stress limit which is used in the step 2.

As for the first step, a startup schedule and stress characteristics are obtained through a simulation of the conventional method used in a startup case for the warm mode. In this case, the maximum stresses of the rotor surface and the rotor bore are -3 1.29 kg/mm and 36.54 kg/mm', respectively. The required startup time is 287 minutes.

As for the next step, the optimum schedule is obtained by the proposed method which modifies speed and load hold times as schedule parameters when the stress limit for the rotor surface is -31.29 kg/mm2. But, the rotor bore stress is limited to 37.8 kg/mm* which comes from an elasticity limit of the rotor metal. The boiler

light off time in this case is hastened by 7 minutes, and the startup completion time is also cut by 21 minutes. Consequently, the proposed method reduces the startup time by 14 minutes compared with the conventional method so its reduction rate is 5%.

Even in this case, some stress margins still remain in the loading up part. It seems that there is some room for time reduction in this part if the changing rates of speed and load are introduced as modification parameters for the schedule optimization. Figure 8 shows improved startup characteristics along with the extended proposed method applied to the case. Making good use of the stress limits, the startup time is reduced by 54 minutes. The time reduction rate comparing the conventional method is 23.7%.

Simulations of the schedule optimization in the cases for hot and cold modes are made in the same manner as for the warm mode described above. The simulation results are shown in Table 2. The startup tomes are remarkably reduced by the extended proposed method for the all startup modes. But no reduction is obtained for the hot mode by the proposed method. The reason is that a suboptimum schedule is obtained for this mode by the conventional method. Both times for light off and startup completion are not modified even though the time for roll off and parallel in are slightly modified.

The simulated plant has a one-through type super-critical pressure and a rated capacity of 1000MW.

K W I , / / e - , L L = , Ptbl 37. ................................................................ __ ...... -_ Z . ? ! . S _ ' r ? S -_ L"L'378Lg"m'

0 .

-25. ...................................................... ...................... ......................... Surface Sbess Limit -31 29ksimm'

I

I I I

Fig.8 OPTIMUM SCHEDULE OBTAINED THROUGH EXTENDED PROPOSED METHOD

Table 2 COMPARISON OF REQUIRED STARTUP TIME

I Startup Mode I Hot I Warm I Cold I

I I Extended Proposed Method 1 t c I 150 1 219 1 399 I

680 Comuutation hme

Computation time depends on the simulated time range of the startup process and the iteration numbers of the schedule optimization. Using a control computer(HID1C-V90/25) in the simulation study, the CPU time is about 70s for hot mode, 170s for warm mode and 320s for cold mode.

CONCLUSIONS

An operation support expert system based on on-line dynamics simulation and fuzzy reasoning for startup schedule optimization in fossil power plants was discussed. This system introduced a function for iterative schedule optimization with fuzzy reasoning and a plant dynamics model, and a function for accommodation of unexpected schedule errors. Simulations with this system showed the following.

(1) Plants were started quickly and accurately. (2) Operators prepared the optimum startup schedule, even under

(3) Operators work was lessened in monitoring and executing

(4) Operators were provided with functions for on-line

( 5 ) Convergency of the schedule optimization was very quick

This system has advanced capabilities in early prediction of schedule errors caused by excess stresses, and in providing operators with guidance for plant preventive operation. This system is believed to be capable of meeting new needs in connection with middle load operations of fossil power plants.

unusual process conditions.

startup schedules.

assessment and off-line learning of the plant dynamics.

compared with conventional operations research techniques.

REFERENCES

[ l ] F. J. Hanzalek and P. G. Ipsen, " Thermal Stress Influence Starting, Loading of Bigger Boilers." Electrical World, Vo1.165, No.6, February 1966, pp.58-62.

[2] R. G. Livingston, " Computer Control of Turbine-generators Startup Based on Rotor Stress." Joint Power Conference ASME and IEEE, 1973.

[3] H. Matsumoto et al., " Turbine Control System Based on Prediction of Rotor Thermal Stress." IEEE Transaction on PAS, Vol. PAS-101, No.8, August 1982, pp.2504-2512.

[4] A. Kaji et al., " Development and Application of an Expert System(H1TREX) for Plant Operational Support." Conference on Expert Systems Applications for the Electric Power Industry, Sponsored by EPRI, June 5-8, 1989.

Hiroshi Matsumoto was born on October 8, 1947 in Japan. He received his B.S. degree in electrical engineering from Doshisha University, in 1970. He joined Hitachi, Ltd., in 1970 and has engaged in research and development of computer control systems for fossil power plants at Hitachi Research Laboratory. He is a senior researcher and manager of the 3rd Lab. in GREEN Center (Global Resources Environment and Energy System Center) of Hitachi Research Laboratory. His current research interests concern enhancement of operation and control systems for power plants based on operations research, artificial intelligence, fuzzy theory and neural networks. He is a member of the IEEE, the Institute of Electrical Engineers of Japan, the Japan Society of Mechanical Engineers, Society of Instrument and Control Engineers, and the Japan Neural Network Society.

Yurio Eki was bom on February 15, 1943 in Japan. He received his B.S. and M.S. degrees in electrical engineering from Kyushu University in 1966 and 1968, respectively. He joined Hitachi, Ltd., in 1968 and has engaged in development of control systems for fossil power plants at Omika Works. He is a senier engineer in Thermal and

Hydroelectric Power Control and Instrumentation System Engineering Dept. of the Works. His current interests concern development of opertion training simulators and operation support systems for fossil power plants. He is a member of Thermal and Nuclear Power Engineering Society.

Akira Kaii was bom on April 21, 1948 in Japan. He received his B.S. and M.S. degrees in mathematics from Tokyo University in 1971 and 1973, respectively. He joined Hitachi, Ltd., in 1974 and engaged in development and designing of software systems for computer control of the fossil power plants at Omika Works till 1981. He received his M.S. degree in electrical engineering from Carnegie- Mellon University in 1982. From 1983 he has engaged in development and designing of management and control systems such as operation support expert systems and man-machine systems in Thermal and Hydroelectric Power Control and Instrumentation System Engineering Dept. of the Works. He is a senior engineer in the Dept. He is a member of Thermal and Nuclear Power Engineering Society.

Seiitsu Nigawara was born on November 29, 1939 in Japan. He received his B.S. degree in mechanical engineering from Iwate University in 1962. He joined Hitachi, Ltd., in 1962 and has engaged in planning and designing of instrumentation and control systems for fossil power plants at Hitachi Works. He is the manager of Thermal Power Plant Information and Control Dep. of the Works. His current interests concern designing of hardware and software systems such as facility management systems and operation support systems. He received the Hatakeyama Award from the Japan Society of Mechanical Engineers in 1962. He is a member of the Japan Society of Mechanical Engineers, and Thermal and Nuclear Power Engineering Society.

Makoto Tokuhira was born on July 15, 1952 in Japan. He received his B.S. and M.S. degrees in instrumentation engineering from Keio University in 1975 and 1977, respectively. He joined Tokyo Electric Power Co. in 1977 and engaged in designing of electronic telecommunication systems in Power System Operation Dept. at Matsumoto Power Offices till 1978. He worked at Sinagawa Thermal Power Station, Ohi Thermal Power Station and Hirono Thermal Power Plant Construction Offices as a instrumentation and control engineer from 1979 to 1983. From 1984 to 1990 he engaged in development and designing of control systems for thermal power plants in Thermal Power Plant Design Division of Thermal Power Dept. From 1991 he has engaged in designing of automation and control systems for thermal power plants as the manager of Maintenance Division of Engineering Dept. at Yokosuka Power Station. He is a member of the Society of Instrument and Control Engineers, the Japan Ergonomics Research Society, the Japan Society of Mechanical Engineers, and Thermal and Nuclear Power Engineering Society.

Yasuvuki Suzuki was born on January 8, 1958 in Japan. He received his B.S. degree in management engineering from Chuou University in 1981. He joined Tokyo Electric Power Co. in 1981 and engaged in operation and designing of control systems at Goi Thermal Power Station and Yokosuka Thermal Power Station till 1986. He worked for development and designing of control systems for fossil power plants in Thermal Power Plant Design Division of Thermal Power Dept. from 1987 to 1989. From 1990 to 1991 he engaged in designing of automation and control systems for thermal power plants as an assistant manager of Maintenance Division of Engineering Dept. at Yokosuka Power Station. From 1992 he has engaged in designing of automation and control systems for thermal power plants as an assistant manager of Maintenance Division of Engineering Dept. at Goi Power Station. He is a member of Thermal and Nuclear Power Engineering Society.