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Nuclear Power Plant OperatorPerformance Analysis Using TrainingSimulatorsTsutomu OHTSUKA a , Sadanori YOSHIMURA a , Ryutaro KAWANO b ,Minako FUJIIE b , Hiroshi UJITA c & Ryuji KUBOTA da Nuclear Engineering Laboratory , Toshiba Corp. , Ukishima-cho,Kawasaki-ku, Kawasaki , 210b Nuclear Power Research and Development Center , Tokyo ElectricPower Co. , Uchisaiwai-cho, Chiyoda-ku, Tokyo , 100c Energy Research Laboratory , Hitachi, Ltd. , Omika-cho, Hitachi-shi , 319-12d Hitachi Works, Hitachi, Ltd. , Saiwai-cho, Hitachi-shi , 317Published online: 15 Mar 2012.

To cite this article: Tsutomu OHTSUKA , Sadanori YOSHIMURA , Ryutaro KAWANO , MinakoFUJIIE , Hiroshi UJITA & Ryuji KUBOTA (1994) Nuclear Power Plant Operator Performance AnalysisUsing Training Simulators, Journal of Nuclear Science and Technology, 31:11, 1184-1193, DOI:10.1080/18811248.1994.9735275

To link to this article: http://dx.doi.org/10.1080/18811248.1994.9735275

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Journal of NUCLEAR SCIENCE and TECHNOLOGY, 31[llJ, pp. 1184-1193 (November 1994).

Nuclear Power Plant Operator Performance Analysis Using Training Simulators

Operator Performance under Abnormal Plant Conditions

Tsutomu OHTSUKA*, Sadanori YOSHIMURA*, Ryutaro KAWANO**, Minako FU JIIE**, Hiroshi U JlTA$* and Ryuji KUBOTAZX

* Nuclear Engineering Laboratory, Toshiba Gorp. ** Nuclear Power Research and Development Center, Tokyo Electric Power co. t* Energy Research Laboratory, Hitachi, L td . :$ Hitachi Works, Hitachi, L td .

(Received July 6 , 1992), (Revised February 16, 1994)

This paper deals with a research on human factors performed by the Japanese BWR group, focusing mainly on the work done in Phase I (1984-86) of the research project. As a first simulator study of operator performance during accidents in the nuclear field in Japan, it was necessary to develop analysis methods to identify and quantify operator errors.

The performance of operators under plant abnormal conditions was analyzed using full-scale BWR plant simulators. By utilizing on-line plant data collection systems and audiohide0 devices, data were gathered from actual plant operators in the retraining courses on two full-scale simulators a t the BWR operator training center in Japan. More than a hundred cases of operator performance data were analyzed to identify human errors and to classify the types of errors.

For application to probabilistic safety assessment (PAS), error probabilities were estimated and compared with existing data. Quantification of cognitive errors was also performed based on the Time-Reliability Correlation. An assessment of the effects of human factors in PSA suggested the importance of errors in the operators’ cognitive processes, which accounted for the major effects of errors in tasks of high safety significance.

The results of the analysis esrve a s a basis for building a data bank to be utilized for multiple purposes of PSA, man-machine interface improvement and operator training.

KEY WORDS: operators, nuclear, performance, B WR type reactors, training, data collection, human factors, error identification, error classification, error quantifica- tion, error probability, time reliability correlation, PSA, aafety analysis, proba- bilistic estimation

I. INTRODUCTION Operators’ errors of judgment, misleading

signals and inadequate instruments in the central control room were considered to be the major causes of the TMI accident. Since then the question of human factors involved in nuclear power plant accidents has come to receive considerable attenti~n“’”‘~’.

The Japanese electric power industry has been conducting basic studies on human fac- tors since 1984, focusing on the analysis of operator performance under abnormal plant c ~ n d i t i o n s ( ~ ) ” ( ~ ) .

1- Ukishima-cho, Kawasaki-ku, Kawasaki 210. ** Uchisaiwai-cho, Chiyoda-ku, Tokyo 100. $* Omika-cho, Hitachi-shi 319-12. z: Saiwai-cho, Hitachi-shi 317.

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In Phase-I (‘84--’86), research was conducted by using data from training simulators to identify human errors and to classify the types of errors. Quantification of human errors was also performed to assess the effects of human errors on nuclear plant satety.

In Phase- II (’87-’89), operator performance and cognitive processes were evaluated by the analysis of verbal protocol data. A cogni- tive process model was developed by a stochastic network, based on Rasmussen’s decision making model.

In Phase-III (’90--’92), experiments were carried out for analysis of the relationship between communication types and team per- formances.

The work described in this paper was performed mainly in Phase I of the research project.

It is difficult to collect and analyze data on human behavior in sufficient detail and accuracy, especially those data which pertain to operator behavior in actual plant abnormal conditions.

Considering the above fact, in the present study, data on operators’ responses to tran- sients and accidents were gathered from ex- perienced plant operators in the retraining courses at a training center.

The data were analyzed to provide a basis for dealing with human factors problems such as those in probabilistic safety assessment (PSA), man-machine interface improvement and operator training.

The derivation of human error probability is one of the central problems in quantitative human reliability analysis. Two major sources of human error probability are (1) subjective expert judgment and (2) simulator data. Swain’s handbook‘’), belonging to the first category, is used in current PSA. T o improve the objectivity of data, simulator data are gathered and human error probability is cal- culated in some research projects. In a NUREG study(’), error probabilities for the selection of controls and the execution of the operation are derived from the simulator data. In our study, cognitive error probabilities are calculated in addition to error probabilities

for use in PSA. Aiming at building a human error data

base with sufficient background information about the characteristics of operators and operational situations, simulator data are col- lected with Japanese operating crews in this study.

In the following chapters, descriptions will first be made of the data collection and analysis methods. The results of data an- alysis will then be presented along with the assessment of the effects of human errors on plant safety.

IT. OPERATOR PERFORMANCE ANALYSIS

1. Data Collection T o analyze operator performance under

abnormal plant conditions, data have been obtained using two simulators a t the BWR Operator Training Center (BTC). One of the simulators, BTC Unit-1, is an 800MWe BWR-4 type plant with a conventional control panel and the other, BTC Unit-2, is an 1,100MWe BWR-5 with a control panel equipped with CRTs.

( 1) Participants The retraining course was chosen because

it is possible to obtain data on the perfor- mance of skilled operators responding to plant incidents and accidents. A crew in the re- training course consists of four operators having more than several years of experience in plant operation. Operators usually take the retraining course at an interval of two or three years.

( 2 ) Training Scenarios Training scenarios used in the retraining

course cover a wide range, from single-failure anticipated transients to multiple-failure events. Typical scenarios are shown in Table 1.

( 3 ) Apparatus T o analyze the behaviors of operators

undergoing simulator training in the retrain- ing course, three different types of data were obtained : on-line data from simulator com- puters ; and audio-visual data on operators ; data obtained through questionnaires. More

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1186

Table 1 Typical scenarios for retraining course

J . Nucl . Sci. Technol.,

System Scenario or malfunction

Reactor coolant recirculation

Condensate and feed water

Recirculation pump trip Recirculation flow control system failure

- Feedwater controller failure Leakage in the condensate and feedwater system Total feedwater loss

~

~~ -

Turbine, generator Turbine controller failure Turbine bypass valve inadvertent open Generator tr ip

- Loss of off si te power Reactor protection system power loss - Bus failure

-~ _ _ Electrical system

Leak or break Total feedwater loss with high pressure coolant injection failure Safety/Relief Valve stuck open Main steam line break . Loss of Coolant Accident

than 100 cases of data have been obtained from BTC Units 1 and 2. Here, a case de- notes that a crew undergoes one training scenario. Figure 1 shows the three types of data collected.

(1) Data from simulator computers Data-collecting devices were connected

with the simulators for BTC Units 1 and 2 and the following data were obtained:

(a) Plant parameters: Water level, pres-

sure etc. (b) Analog set values: A set value for

recirculation flow control etc. (c) Data on events

Changes in signals for instrument contact points, such as switch-on, switch-off, demand signals for sounding alarm, malfunctions etc.

These data have been put together in a timeline list corresponding to plant conditions and operational records in order to facilitate

[Simulator Training for Emergency Conditions

Questionnaires

Experience in Thermal Engaqed Plant

Power Generation 3 (a) Plant Parameters (a) Video I Lressure I/lb) Voice Reactor Water Level (Operation Position)

(b) Analogue Data [Report \ Instruction Experience in Nuclear Instruction by Paging )I/ \

IbEelf-disclosure of Error Power Generation

(Set V2ilues) (c) Data on Events

[Operation Switch\ (Experience in Similar

Events)

I Time Line 1 Interpretation of Event, Decision Making,

Information Transmission or Reception I I I I

Fig. 1 Analysis flow of collected data

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subsequent analysis. (2) Audio-visual data T o record the behaviors of operators,

(particularly how they acknowledge alarm signals and how they manipulate controls), audio-visual data were obtained.

Visual data were recorded using three video cameras and six video tape recorders (VTRs). These cameras covered the main panel and the auxiliary boardsof the nuclear steam supply system (NSSS) and the balance

I ((?/Main Console I-

‘U Main Confrol Room Observer‘s Room

BTC Unit 1 Simulator : Ceiling Microphone

of plant (BOP). Two VTRs were allotted to each video camera to continuously record data for more than 6 h without changing video tapes manually. Audio data were re- corded using 12 wired microphones. These microphones were located in the ceiling and each of them equipped with a plastic hemi- spherical reflector to enhance sound catching capability. The arrangement of video cameras and microphones is shown in Fig. 2.

BTC Unit 2 Simulator

€I : Ceiling VTR Camera

Fig. 2 VTR camera and microphone positions

(3) Questionnaires Questionnaires were filled out by the

operators to collect information on their home plants and experience, together with their comments on the training session.

2. Data Analysis ( 1) Error Identification Errors were regarded in this study as

operators’ behaviors which deviate from standard operational sequences. It was in- tended not only to cover errors that actually affect the plant, but also to include a wider range of errors having a potential for caus- ing significant effects on plant safety. With this wider definition, it was possible to gather a larger number of errors than with a nar- rower definition. Therefore more information could be obtained for application to the im- provement of man-machine interfaces and operator training programs.

In general, operators’ duties can be con-

sidered to consist of a series of operations which are dedicated to one single purpose, referred to here as “task” and the individual operations to be performed, referred to here as “task elements”. These series of opera- tions are further organized into a certain systematic flow which takes the form of a standard operation sequence. In other words, the standard operation sequence is a list of tasks and task elements arranged approxi- mately in a time series. It was developed by extracting individual operations according to the operational procedures and classifying these operations into tasks and task elements.

In Fig. 3, an error identification process is shown which will be described below.

Data gathered by the on-line collection systems were summarized into timeline des- criptions to facilitate analysis.

Based on the timeline, the analyst first identified the plant status. If the event caus-

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1188 J. Nucl . Sci . Technol.,

COLLECTED DATA 55 TIME LINE I i

TANDARD OPE. SEQUENCES EXIST ?

NO YES

COMPARE ACTUAL OPERATION WITH STANDARD OPE. SEQUENCES

i 1 IDENTIFY TASKS FOR I THE SITUATION

OPERATION WITH

Fig. 3 Error identification process

ing the accident had been identified and cor- responding standard operational sequences existed, actual operator performance was compared with the standard sequences to pick up errors.

If no standard sequences existed, the an- alyst identified the operational goal for the situation and tasks needed to achieve the goal. Then actual operator performance was checked against the tasks thus defined.

In the above error identification process, audio/video information supplemented the computer-collected data by providing infor- mation on what conversation they were mak- ing and on where each crew member was located at the time of interest.

( 2 ) Error Classification Operational errors identified above were

classified, in principle, according to the taxo- nomy of Swain and Guttmann“) to maintain compatibility with existing error data.

Two major types of operational errors are as follows:

(1) Lack of operations to be performed: This is referred to as an “omission error”.

Two types of omission errors, task and task element levels, were taken into consideration.

(2) Incorrect implementation of operations

to be performed : This is referred to as a “commission error”.

Commission errors are classified into task level errors and three types of task element level errors, namely “selection”, “direction”, and “system” errors. Here, “system” repre- sents an error in selecting a system and “selection” denotes an error in selecting a control within an adequately selected system. “Direction” represents an error of operating a control in a wrong direction.

( 3 ) Error Quantification Finally, the operational error probability

was determined by the following formula :

Error probability

- Number of errors - Number of opportunities ’

The number of opportunities for the omission error is defined as the number of required control actions, and for the commission error, it is defined as the number of control actions actually performed.

It should be noted here that task level errors were not included in the calculation of operational error probabilities, because task level errors were treated as cognitive errors estimated by the TRC method to be described later.

3. Results and Discussions Results were presented for a total of 129

cases, 77 for BTC Unit 1 simulator and 52 for Unit 2. They were summarized to show the percentages and contents of omission and commission errors and the locations of errors in the plant. Estimated error probabilities were presented for comparison with existing data. Time reliability correlations (TRCs) were also derived as an attempt to quantify cognitive errors.

( 1 ) Types of Errors The percentages of omission and commis-

sion errors were about 60 and 40 for both BTC Units 1 and 2 as shown Fig. 4(a).

About 80% of the omission errors are on the task element level and 20% on the task level for BTC Unit-1. For BTC Unit 2, the percentages were 70 and 30, respectively, for

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Commission

Unrecovered Commission

Omission

Unrecovered Omission

1189

m i 0

; a

mission errors were task element level errors, more than 65% of which are selection errors.

( 2 ) Plant Systems Related

The errors were classified according to the related plant systems. The percentage of the errors related to the reactor, the re- actor auxiliaries and the turbine systems were approximately 40, 30 and 30 respec- tively, for Units 1 and 2.

Among the errors related to the reactor system, about 75% were related to the feed- water and its control systems. This may be due to the fact that operations on the feed- water and its control systems were large in number and performed under a heavy work load after a reactor scram.

The errors in the reactor auxiliary systems were mainly caused by incorrect recall of procedures and interlocks.

Although the main and auxiliary steam systems had a larger percentage of errors, no single subsystem was particularly error- prone in the turbine system. The errors re- lated to the main and auxiliary steam systems were largely due to insufficient check of the panel status.

to Observed Errors

( 3 ) Error Probabilities To utilize for probabilistic safety assess-

ment and quantitative reliability analysis, task element and task levels (Fig. 4(b)). error probabilities were estimated using the

The contents of commission errors were error data obtained. The probabilities of almost the same for both Units 1 and 2 (Fig. commission and omission errors with 90% 4W). confidence limits are mown in Fig. 5.

It was shown that about 80% of the com-

Fig. 5 Error probabilities for commission and omission

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1190 J . Nucl. Sci. Technol.,

The commission error probability was 1/2 that of omission, while recovery rate of com- mission was smaller. It was also found that the commission error probability was greater on the reactor auxiliary control panel than on the other control panels. It is because the reactor auxiliary panel is seldom used in normal operation and operators tend to cause commission errors, most of which are selec- tion errors. For the BOP control panel, the omission error probability was higher than for the other panels. The reason for this is that operations on the BOP panel are not so urgent in emergency situations as those on the other panels and operators are apt to forget making these operations. Time delay is the major type of omission errors on the BOP panel.

A comparison was made of the above error probabilities with those of a previous study [NUREGICR-33091 (‘I. The commission error probability was almost the same in both stud- ies, while the omission error probability was lower in the present study. The reasons for the differences between the data of this study and NUREG were not clear, since many factors such as scenarios, error identification criteria, operator familiarity with the simulator and operator skills were affecting the results. It might be said that the balance between the demands from the scenarios and operator skills (considering the familiarity with the simu- lator) affected the difference in the results.

( 4 ) Time Reliability Correlation (TRC) As will be described in 3.5, operator errors

are divided into cognitive errors and opera- tional errors for the sake of quantitative analysis. T o quantify cognitive errors on the task level, the TRC method”) was employed. The response probability is defined as the pro- bability that operators successfully respond to the given situation within the time allowed. The non-response probability is defined as “1-response probability”. It is assumed in the TRC method that the cognitive process suc- cess probability, represented by the above defined response probability, increases with the time available for diagnosis and that the relation between the non-response probability

and the response time is approximately re- presented by a straight line when plotted on a logarithmic normal distribution probability sheet.

Response time data concerning TQU (tran- sient with high pressure cooling function loss) and T C (ATWS : anticipated transient without scram) sequences are shown in Fig. 6.

-

o.+ , , , , , , , , , ,,,,,,,,, , , , , , , 0.1

0.01 0.1 1 10 1:;

Response Time [minutes]

Time reliability correlation (TRC) Fig. 6

Phase I denoted the period 1984-86 and Phase II , 1987-89. Response time was defined as follows for both sequences.

TQU sequences : Time period from the moment when the HPCS and RCIC become unavailable to the start of re- actor depressurization for the initia- tion of low pressure injection systems.

T C sequences: Time period from incom- plete scram to operator’s actions back- ing up the insertion of control rods.

In TQU sequence, cognitive tasks mainly affecting the response time may be (a) the determination of the status of HPCS (High Pressure Core Spray system) and RCIC (Reactor Core Isolation Cooling system), and (b) the deci- sion of whether these systems can be re- stored in due course. Phase I TQU data in- cluded two quick responses of crew dataand the variance was larger for Phase I than for Phase II. For T C sequences, larger variance was seen than in the TQU sequence. In TC sequence, a major cognitive task affecting the response time may be the decision on the use of the standby liquid poison system in consideration for all negative effects on plant

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systems by contamination of the reactor selected plant was a typical 1,100 MWe BWR-5 water when all backup actions for control plant. Four representative sequences were rods insertion failed. selected as risk dominant. The outlines and

( 5 ) Effect of Human Factors on PSA related operator actions for the selected The effect of human factors on BWR plant

PSA (level 1) was estimated as follows: The sequences are shown in Table 2.

Table 2 Selected risk dominant sequences and operator actions

No. Notation Sequence outline Operator action

1 TQUX Feedwater loss with HPCS and RCIC failure Feedwater control ADS function lost HPCS, RCIC manual start

ADS or relief valve manual actuation

2 T W Transient with decay heat removal system lost Feedwater control Decay heat removal system operation

3 T B All the alternating current power lost High pressure injection systems manual start

4 T C Transient with failure of reactivity shutdown Manual scram Standby liquid poison injection system start

Operator errors during accidents can be :considered, for the sake of analysis, to be divided into cognitive error and operational error and to be described by Operator Action Trees (OATs) as shown in Fig. 7(']-@]. Cogni- tive error probabilities for TQU and T C sequences were obtained from TRCs as shown in Fig. 6 . For the TQU sequence, Phase I data were used to give more conservative

(safer-side) results. For T W (a transient with decay heat removal system lost) and T B (all the alternating current power lost) sequences, no cognitive errors were included in the present calculation. The allowable response time was calculated using plant transient analysis codes to obtain the time when a specified task was required based on plant status.

(OAT) lncidenl

OK

+ P C P c

Failure P a l

Failure Pa2 Spare Tima lor Operalion

Fig. 7 Quantification of operator's unreliability by using OAT

Operational errors, the classification of handbook. Error probabilities for checking which was already described in Sec. II-2, were estimated using the HRA handbook data were calculated by using the THERP method. and other error probabilities were calculated Human Reliability Analysis (HRA) trees were by using the data obtained in this study. developed and quantified by using error data The cognitive and operational errors calcu- obtained in this study and those of the HRA lated above were used to quantify OATs.

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1192 J . Nucl. Sci. Technol.,

Next, system fault trees and event trees were quantified, in which OAT results were used in two ways. One was in a recovery action after an automatic start-up system failure. The other was in a failure of the manual start-up system. Mechanical failure pro- bability was calculated, including maintenance and surveillance errors, if applicable, by using the THERP method and the HRA handbook.

Finally, the human factor importance HFI defined below, was evaluated for each operator action. The HFI indicates the effects of human errors on the core melt frequency.

where U the core melt frequency for the current sequence is defined as follows :

where F f : Human error probability of i-th task Qi : i-th equipment failure probability

AU : Difference in U AFi: Difference in Fi.

Results are shown taking TQUX (transi- ent with loss of high pressure cooling func- tion and depressurization) sequence, a type of TQU sequence characterized by the loss of depressurization, as an example in Fig. 8, where HFI for reactor depressurization nearly equals 1.0. This was because the mechanical failure probabilities of ADS or relief valves were negligible compared to

Fig. 8 Human factors importance evaluation for TQUX sequence

the probability of human error, especially for cognitive tasks and the effect of manual de- pressurization failure on the core melt fre- quency is very high. On the other hand, HFI for the recovery action of the high pres- sure injection (HPIS) systems were negligibly small, because both automatic initiation and manual initiation depend on the same hard- ware and the total failure probability of HPIS is determined by the hardware failure pro- bability.

Ill. CONCLUSIONS A study on operator performance was

conducted by the Japanese BWR group, based on full-scope simulator data taken from BWR plant operators in the retraining course at the training center.

This paper was mainly concerned with the work done in Phase I (1984-86) of the research project. As a first simulator study of operator performance during accidents in the nuclear field in Japan, it was necessary to develop analysis methods to identify and quantify operator errors.

Emphasis was placed on analyzing opera- tors’ behavior in plant abnormal conditions. Analyses were made to identify and classify errors. Error probabilities were estimated and compared with existing data. Quanti- fication of cognitive errors was also per- formed based on operators’ response times.

An assessment of the effects of human factors in PSA suggested the importance of errors in the operators’ cognitive processes, which accounted for the major effects of errors in tasks of high safety significance.

The results of the present study serve as a basis for building a data bank to be utilized for multiple purposes of PSA, man-machine interface improvement and operator training.

ACKNOWLEDGMENT This work was done as part of the joint

research carried out by the Japanese BWR Group of six utilities and two vendors, with the cooperation of the BWR Operator Train- ing Center (BTC).

The authors would like to acknowledge

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Prof. I. Kuroda of Waseda University, T. Yukimachi of Keio University, M. Aoki of Nihon University and Dr. Y. Nagasawa of the Aeromedical Laboratory of JASDF, who pro- vided invaluable advice for the simulator based experiment. They also wish to thank the participants from the utility operators, the BTC instructors, and the vendor operators.

-REFERENCES - (1) SWAIN, A. D., GUTTMANN, H. E. : Handbook of

human reliability analysis with emphasis on nuclear power plant applications : Final Rep., NUREG/CR-1278, (1983).

(2) BEARE, A. N., et al. : A simulator-based study of human errors in nuclear power plant control room tasks, NUREGICR-3309, (1984).

(3) VILLERMUR, A., MORINI, J.M. MOSNERON, F., MESLIN, T. : A simulator-based evaluation of operators’ behavior by electricite de France, Proc. of I n t . Topical Meeting on Advances in Human Factors in Nuclear Power System,

374-379 (1986). (4) YOSHIMURA, S., OHTSUKA, T., ITOH, J., MA-

SUDA, F. : An analysis of operator performance in plant abnormal conditions, Proc. of I E E E 4 t h Conf. on Human Factors and Power Plants, Monterey, June, 1988, p. 509-12.

(5) HAYASHI, M., et al .: A study on human fac- tors relating to BWR plant safety-2, Proc . of 3rd Nat l . Symp. on Probabilistic Safety Asses- sment, (1988), [in Japanese].

(6) KAWANO, R., et al .: Plant Operator’s be- havior i n emergency situation by using tran- ing simulators, I E A ‘92, Paris, July 15-20, 1991.

(7) HALL, R.E., FRAGOLA, J.R., WREATHALL, J . : Post event human decision errors ; Operator action tree/time reliability correlation, NUREG /CR-3010, (1982).

(8) BELL, B.J., SWAIN, A . D . : A procedure for conducting a human reliability analysis for nuclear power plants, NUREGICR-2254, (1983).

(9) U. S. NRC : Safety related operator action ; Methodology for developing criteria, NUREG/ CR-3515, (1984).

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