IEEE Transactions on Power Systems, Vol. 4, No. 4, October 1989

EXPERT SYSTEMS IN ELECTRIC POWER SYSTEMS - A BIBLIOGRAPHICAL SURVEY

Z.Z. Zhang G.S. Hope O.P. Malik Dept. of Electrical Engineering

University of Hohai, China Dept. of Electrical Engineering University of Calgary, Canada

Keywords: Artificial Intelligence, Expert Systems, Power Engineering, Power Systems.

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Abstract Application of expert systems to power engineering is an area of

growing interest. This paper gives a bibliographical survey of research, development and application of eapert systems in electric power systems based on over 80 published articles. General back- ground of the research, development and applications is described.

Requirements for expert system technology in power system appli- cations are clarified. A historical perspective of expert systems, an overview of their applications and potential future applications in power systems are presented.

INTRODUCTION In recent years expert system technology has captured interest in

many fields of electric power engineering and this trend is likely to continue. One facet of the current trend is to apply expert systems to various engineering problems and production activities.

Expert systems offer a number of advantages [ 12, 36, 49, 54, 561: (a) Assist Human Experts

An expert system can implement performance at the level exhi- bited by a person with recognized expertize in the problem domain. Therefore, it can reduce tedious and redundant manual mks and pro- vide a human expert with an environment that enhances his produc- tivity, thus leading to efficient operation. @) Flexibility

Each production rule represents a piece of knowledge relevant to the task. Hence it is very convenient to add, remove and modify a rule in the knowledge base as experience is gained. (c) Understanding

Production rules are close to natural language and, therefore easy to understand. The expert system can give the steps that led to the conclusion and explain the reasoning process. The user can confirm or correct the conclusion by examining the explanations given by the inference engine. (d) Universality

The knowledge base is problem domain dependent, but the infer- ence engine is domain independent. So, different expert systems can be developed by replacing the knowledge base. (e) Rapidity

The expert system can provide the right expertise whenever needed. Expert systems can provide more rapid reaction to emergency events than human operators. This is very useful in power system operation.

A bibliographical survey of the research, development and appli- cation of expert systems in electric power systems based on over 80

39 WM 212-2 PWRS by the IEEE Power System Engineering Committee of the IEEE Power Engineering Society for presentation at the I E E E / P E S 1989 Winter Meeting, New York, New York, January 29 - February 3, 1989. Manuscript submitted July 25, 1988; made available for printing December 1, 1988.

A paper recommended and approved

articles published since 1982 is given in this paper. Technological requirements for expert systems, a historical perspective of expert sys- tem developments and an overview of the applications in power sys- tems are given. A brief discussion of future trends in expert systems for power systems is also presented.

GENERALBACKGROUND In recent years research in the field of artificial intelligence(A1)

has achieved significant successes. Among the most significant of these is the development of powerful computer software systems known as "expert" or "knowledge-based systems. Advances in the AI technol- ogy have been spurred by research efforts at Universities, research organizations, large corporations with AI development divisions, and companies devoted almost exclusively to AI and expert system development. This is accompanied by vast literature in this area. For example, there were over 200 papers on expert systems presented or published as early as 1985 [l].

To aid the development of expert systems, many tools have been developed. These include: - programming languages such as PROLOG (a logic-based

language) and LISP (a procedure-oriented language). - expert systems shells that provide skeletal systems or general-

purpose systems. system-building aids and other support facilities, such as debug- ging aids, U 0 facilities, knowledge base editors, etc. Expert systems applications in industry have become widespread

as evidenced by the sale of over 10000 expert systems shells in the year 1986 in USA alone [2]. A survey of over 30 companies representing nearly 200 expert system applications in industry [3] showed that interest in this area is spread over a wide range of indus- tries. Generic roles for expert systems applications are given in Table I [2]. The types of applications are arranged in order of decreasing percentage of the applications. The predominant role of expert systems is for diagnosis, which accounts for over 30% of the applications. The predominant subject is equipment, single pieces or integrated units, which accounts for over two thirds of the applications. Some roles, like control, are actually a combination of several other roles. Over 25% of the applications were identified as having more than one role.

TECHNOLOGICAL REQUIREMENTS A common goal in the management of power systems is to

advance the state of the art to provide safer, cheaper and more reliable electric power. Therefore, the trend to advance the control and manage- ment technology of power systems towards full and integrated automa- tion at higher and higher levels is continuing.

Present Problems Power systems organized in an hierarchical form have become

very complex because of structure, status and relevant technical issues. This growing complexity is causing problems. Some of the more evi- dent problems are: (a) a rapid increase in the number of real-time messages has made

operator response more difficult. This difficulty, called "human cognitive barrier" [54] must be overcome. current numerical processing software cannot meet the operational requirements of power systems in some situations. Examples are processing during emergency conditions and using software in situations beyond their design limitation [46,54].

-

(b)

0885-8950/89/08OO-1355$01 .OO 0 1989 IEEE

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(c) most design. planning and control problems encountered are complex and time consuming because of multiple objective func- tions. multiple constraints, complex system interactions, the need for trade off, and so on 1531.

Table I Generic Types of ES Applications in Industry

Inferring the cause of a malfunction or deviation from available information

Type Diagnosis

Prescription

Situation analysis

Prediction

Selection

Design Planning & scheduling

Control

Instruction

Recommending cures for a system malfunction or deviation from design Monitoring available data and information; inferring the system's state by analysis. Inferring likely consequences of given situations. Identifying the most appropriate choice among a fixed number of possibilities. Configuring objects under constraints. Developing a sequence of actions and and their timing to achieve a desired end result at a specified time. Combination of a number of the above including situation analysis and prescription Teaching a skill, an expertise, or a body of knowledge to a student

Suitability of Expert Systems The suitability of expert system technology for a specific problem

can be determined by inspection of Table I1 on the basis of general experience in the field of expert system applications 141. If an applica- tion has more features on the left side of Table I1 than on the right side, it is a candidate for expert system technology.

Table II Features for Expert System Application

Diagnostic problem Calculative problem No established theory Human expertise scarce

"Noisy" data or information

Suitable features vs unsuitable features

Magic formula exists Low paycheck for human experts Facts are known Dreciselv

Various power systems problems are discussed below in the light of the features described in Table 11.

Dynamic status variation contains a large number of complex fac- tors. Therefore, "diagnostic" probIems are difficult to process numerically during system operation. Many important problems cannot be expressed in equation form because of a lack of established theory. Operators require long operational practice to be fully qualified because of the high complexity and severity of power system operation. They need to be retrained periodically. This means that sufficiently qualified human operation experts are valuable and in short supply. A power system consists of many sub-systems[5]. Various mes- sage communication devices are used between the sub-systems. Data and messages are "noisy" because of wide area and long distance, interference, large quantity and varying types of data. The above discussion shows that power systems are obvious can-

didates for the application of expert system &chnology. Expert sys- tems have a significant potential to aid beginners and operators in a number of procedures and operations that electric utilities frequently encounter [36].

A HISTORICAL PERSPECTIVE OF APPLICATIONS The real beginning of AI is often quoted as 1958 when John

McCarthy coined the phrase[6]. An expert system was first broadly

researched by Feigenbaum et. al. in the early 1970s 171. They developed an expert system called "DENDRAL" which inferred the structural formula of organic compounds. Since then, expert system technology has received increasing attention, and has been applied to medicine, chemistry. geology, electronics, manufacturing, military sci- ence. general surveys, space technology, computer systems, physics, agriculture, and so on.

The power industry was slow to adopt expert systems technology widely as compared to other industries because of the importance of maintaining the securi~y of power supply. Although the first articles on expert system applications in the field of electric power engineering appeared in 1982, some research and development in power engineer- ing had been done earlier.

EPRI's efforts in the application of expert system technology have played an important role in the advancement of this technology to power engineering [36]. The Nuclear Power Division (NPD) of EPRI initiated a project in 1976 with Shaker Research Corporation and Northeast Utilities to develop a system to monitor vibrations and diag- nose malfunction in large pumps in nuclear power plants. A knowledge base system associated with an operation contingency selec- tion procedure developed by Public Service Electric and Gas Company has been in operation since the late 1980 [SO].

The 1979 accident at the Three Mile Island nuclear power plant promoted application of expert system technology to power produc- tion[2]. Since then there has been a growing recognition that human factors can be as important a limitation in maintaining and operating power system and plants as the machines and computers involved. Expert system technology found its application first at nuclear power plant operation because of this accident. Examples of further applica- tions are: (i) an expert system called NPPC (Nuclear Power Plant Consultant)

to assist operators in the determination of the cause of abnormal events; it was developed at the Georgia Institute of Technology and reached the stage of a research prototype in 1982 [10,16]. an expert system called REACTOR for diagnosis and treatment of nuclear reactor accidents; it was developed by EG & G Idaho and reached the stage of a research prototype in 1982 [9,16]. In addition, NPD has supported many projects that apply expert

systems. For example: (a) the BWR shutdown analyzer. This is a demonstration prototype

designed to assist the reactor operator to quickly treat as many as 250 alarms. a series of projects aimed at producing commercial expert system software tools have led to a shutdown analyzer, for example, a knowledge engineering environment (EPRI-KEE).

Other nuclear plant applications of expert systems under study at EPRI include: on-line procedure analyzer; monitoring of technical specification for complying with regulatory requirements for surveil- lance and inspection; plant startup procedures; signal validation for monitoring systems; advanced training aids tied to plant simulators. EPRI's initiative in AI, mainly in expert systems, picked up steam in late 1983 with increased funding authorization in response to growing interest in the field.

An historically important paperr121 was published in Feb.1983. It was the first important application of expert system technology to power system operation. It indicates that this area is particularly suit- able to applications of expert system technology. Since then, a large number of papers on expert system applications to power engineering have dealt with power system operation with emphasis on operators' assistant in control centers (or energy management systems - EMS)[28, 31, 32, 35, 48, 50, 56, 59, 691.

EPRI's Advanced Power Systems (APS), Electrical Systems (ES) and Energy Management and Utilization (EMU) divisions are also involved in expert system projects. Some examples are [36]: (a) General Electric Co., Honeywell,Inc., and Arinc Research Corp.

weic the contractors on an APS project to develop an expert sys- tem for troubleshooting and maintaining gas turbines 1811.

Southern California Edison Co. and the APS division began

,

(ii)

(b)

(b)

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development in 1985 of an expert system as a means of preserv- ing the experience and knowledge gained in operating a cool water power plant. The Power System Planning and Operation Program in the ES division started study in 1985 with the Design Research Center of Carnegie-Mellon University to evaluate applications and develop an expert system for troubleshooting transmission relays and breakers. The EPRI AI Coordination Committee was established in 1984 in

order to coordinate several divisions’ efforts and moreefficiently use resources and research results mainly in expert systems.

Many companies in the USA besides EPRI supported or under- took expert system application projects mainly relating to power system operation. Some examples are:

New York Pool(1983): A prototype program used for fast vol- tage prediction [ l l ] . Control Data Corp.(Plymouth, Minnesota)( 1985): An important feasibility study for an EMS’S intelligent alarm processor [20]. Florida Power Corp. and Clarkson Univ.(1985): A research pro- gram on a power system operator emulator [22]. Allegheny Power Systems and Carnegie-Mellon Univ.( 1985): An intelligent expandable program for power system trouble analysis [311. Puget Sound Power and Light Co. and Univ. of Washing- ton(1986): An expert system as a dispatchers’ aid for isolating line section faults [48]. In addition to power system operation, some expert systems have

been developed since 1985 for the design of electric power plant [53,66,76], electrocenter [55] and turbine-generators [34].

Research on expert system applications to power engineering has been performed by universities independently [8, 14, 19, 30, 51, 52, 53, 55 , 56, 781. Expert system applications have become important in universities’ doctoral dissertations on electric power engineering. The results of some doctoral dissertations have been published [29].

Besides achievements in USA, similar research and development has progressed in other counties. Especially in Japan, relevant research began as early as 1982. Several main electric corporations (Mitsubishi, Hitachi, Toshiba), electric power companies (Tokyo, Kansai, Kyushu) and some universities (the Univ. of Tokyo) have attached importance to development work in this area [17, 21, 26, 27, 32, 33, 731. Recent Japanese work in this area is continually advancing on three levels: research, development and practical use. Practical uses of expert sys- tem technology such as load flow planning, contingency selection, sub- transmission network restoration and network fault diagnosis have already been realized [62].

Also, research and developments in the application of expert sys- tems to power systems have been progressing in Canada [45, 51, 64, 781, England [57, 711, Federal Republic of Germany [56], Australia [52, 741, Switzerland [70], chi^ and some other counmes.

OVERVIEW OF EXPERT SYSTEM APPLICATIONS Expert system technology has now obtained wide-ranging atten-

tion of rhe power engineering community, researchers at universities, research organizations, and engineers and operation staff of electric

,power companies. They have realized that many operation and planning problems are ideally suitable for expert system applications [36]. It is very interesting that some power system dispatchers are enthusiastic about and see the potential advantages of expert systems [48].

Currently most expert systems in power engineering are proto- types for demonstration, research or field tests. However, some expert systems have been in practical use since 1980 in USA [50] and in Japan [62]. Although some progressive plans for demonstration have been reported [12, 20, 22, 31, 34, 49, 50, 53, 55, 561, there is no clear demarcation between the practical-use stage and the development stage of expert systems in power system operation because of the following conditions: (i) The process of building an expert system is an iterative cycle of

development, improvement and expansion.

(ii) Proposed expert systems for power system operation are used only as dispatcher’s aid or consultant.

(iii) A production prototype of expert system is different from a com- mercial prototype for power system operation. Generic types of expert system applications in power industry are

given in Table HI. The following points are made in a comparison with the applications in other industries (Table I):

(a) The predominant role of expert systems in power industry is for diagnosis, accounting for 41% of the applications. This role is similar in Table I. The predominant subject of expert systems in power industry is not equipment but systems. This is a major difference from applications in other industries. Running in second place are applications dedicated to planning and scheduling in power industry. The order is quite different in Table I. An equal important role of expert systems is control in power system operation.

(b)

(c)

(d)

Table III Generic types of ES application in power industry

Type Percentage Reference index number Diagnosis 4 1 % 9,20,28,3 1,32,34,4O,41,45,

46,47,48,49,5 1,54,57,59,65, 67,68,70,75,77,80,83,87.

Planning & 19% 26,27,33,37,42,52,58,63,71, Scheduling 73,74,87.

Control 18% 12,13,15,19,21,30,51,60, 64,72,78.

Design 11% 29,53,55,66,76,77,79.

Prediction 8% 11,31,61,69,85.

Instruction & Training 3% 18.56.

NOTE: Roles of prescription situation analysis and selection are com- bined with diagnosis or control in Table 111.

Applications in Power System Operation The operation of power systems has become very complex and

operators are unable to deal with the large amount of data associated with a modern energy management system (EMS). This has led to the severe difficulty of human cognitive barrier of power system operation [20,54]. Also power system operation experience is lost when experienced operators retire or change jobs. It is important to preserve this valuable experience as it is not contained in a textbook or manual [36,62]. Expert systems can assist in decision-making and minimize errors by human operators. Because of this, a large group of expert system applications is in the area of power system operation to assist dispatchers at the EMS.

To the present, expert system applications in power system opera- tion have included the following main aspects: 1.

2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Fault diagnosis of network [28, 31, 32, 40, 41, 45, 46, 47, 48, 50, 59, 67.68, 871. Alarm processing [20, 25, 35, 38, 44, 501. Load flow planning [26, 27, 33, 37, 42, 52, 61, 731. Reactive power and voltage control [15, 21, 30, 64, 72, 781. Switching operation [17, 32, 481. System restoration [12, 13, 601. Security assessment [22, 691. Transient stability problems [85, 861. U&t commitment [58]. Operator training [18, 561. Friendly interface [33, 541.

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12. Network maintenance scheduling [63,74]. 13. Substation automation (49,701. 14. Computer relaying [80,83]. 15. HVEC transmission systems [45,51]. 16.

In addition, expert systems are proposed for on-line diagnosis of turbine-generators 1341 and other equipment [65].

Utility elecmcal power plant systems [84].

Features in Applications

A. Languages

engineering applications. 1. 2. 3.

Three AI languages are common in the expert systems for power

LISP - a procedure-oriented language [12, 19,20, 34,53,58, 761. PROLOG - a logic-based language [32, 33, 52, 54, 56, 64, 701. OPS - a rule-based language OPS5 [30,41,54,67], and OPS83 [48,72].

These three languages are best suited to and in agreement with the pur- pose and ancestry of expert systems [46]. Other languages such as APL, PASCAL and FORTRAN are rarely used [45,51].

B. Knowledge Representation

The rule-based representation as IF-THEN statements is mainly used in the applications [12, 20, 30, 31, 32, 33, 34, 46, 47, 48, 50, 53, 56,58, 64,70,73,751. The frame representation is used rarely [74].

C. Knowledge Acquisition In general, knowledge used in the applications was acquired in

the following ways: 1. draw on literature in the relevant domain, 2. discuss with experts in the domain (30. 33,48, 52, 53, 581.

The Verbal Rotocol Analysis which is another promising way is men- tioned in some articles [55, 621.

D. Implementation

standard EMS computers t54, 60, 62, 691. 1. 2. 3.

There are three ways to embed expert system software in the

Implementation in a numeric programming language [20,36]. Implementation in an AI programming language [3 1 , 481. Implementation in an AI programming language in attached dedi- cated hardware [22, 33, 40,50, 561.

Some Issues of Concern

expert systems used in power systems:

(i) Interface between Expert System and Numeric Program Interaction between the symbolic computation in an expert system

and the numerical computation of a numeric program is of particular im.portance to power system operation. They can be linked by per- forming some rules [12], utilizing a common "blackboard" database for message-interchanging [31], or be run in parallel in a network of com- puters [67]. An approach for communication between a database and an expert system is developed in Ref. [48].

(ii) Enhancement of Symbolic Computation Speed Symbolic computation in expert systems is slow in general.

Decrement of data access time may be a key to enhancing the compu- tation speed 1481. There was an opinion that the speed of symbolic computation would not cause a bottleneck in the AI programming language(L1SP)-based computer [ 121, but in reality, another processor designed specifically for AI functions added to the present computer system may be required [50]. It was suggested that the enhancement can be achieved through distributed processing and hierarchical reason- ing [31], or special hardware [50].

The following prominent problems exist in the development of

(iii) Knowledge Acquisition Knowledge acquisition has been the major bottleneck in expert

system development. The issue of knowledge acquisition is a particularly important point because the usefulness of expert systems depends strongly on the quality of the knowledge put into the system

(iv) Consistence of Knowledge Base [12,53] A conflict resolution technique is required to keep a knowledge

base consistent when conflicts arise among the rules of a knowledge base, or between overlapping knowledge. This is an open problem in the knowledge base system approach.

(v) Maintenance of Expert Systems [48.50] Easy of maintaining expert system software is a common

requirement of power system operators, especially for dispatchers of the EMS. Without the ease of maintenance, the usefulness of expert systems in the EMS is greatly reduced.

ISSUES RELATING TO FUTURE APPLICATIONS

Lessons from Applications

future applications. Some of them are:

(i)

Lessons obtained from existing applications are very useful in

have a moderate attitude to expert systems. The expert system is a useful and intellectual tool, but can not replace human operators completely.

(ii) clearly d e h e , formulate and describe the business and technical aspects of the problem an application addresses.

(iii) start with a small prototype, but think and plan for big one. An evolutionary development is the most effective way to proceed.

(iv) carefully choose an AI programming language to match its characteristics with the solution and system features suggested by the problem's domain.

(v) use a suitable expert system shell of some sort, but avoid using new shells that are still under development.

(vi) attain users acceptan= throughout the application development phases by fitting the application into the users job functions.

(vii) consider the whole life cycle of the application, including the issues of maintaining, updating and supporting the application, from the beginning of the development

Some Potential Application Areas Many applications of expert systems in electric power engineering

are surveyed in this paper. Further application areas likely to receive increasing attention in the near future are: (a) Failure Diagnosis of Equipment

Diagnosis has proved to be one of the most valuable of the prac- tical applications of expert systems in industry. Safe and economic operation of a power system is eventually dependent on the state of various equipment. Detection and repair of equipment faults require much effort and moreover require special relevant knowledge and expertise. It is practicable to encode the knowledge and expertise to assist in off-line or on-line diagnosis of the equipment to improve secu- rity and economy. @) Intelligent Integrated Automation of Substation

It has become clear that the building of an integrated control and protective system for a substation is necessary for integrated substation automation. Addition of expert system technology to an integrated control and protective system will change it to an intelligent one. It is obvious that AI, mainly expert system technology, is necessary for integrated substation automation. (c) Economic Dispatch of Power Systems

Both safe and economic phases are principal and basic considera- tions of, the EMS'S dispatcher, but applications of economic dispatch are few. Current numeric programs can be combined with the dispatcher's knowledge and expertise. This may be a practical approach to maintaining a better balance between the two conflicting

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objectives of minimizing owrating cost and maximizing security.

(d) Intelligent Digital Protective Relay Adaptive digital protective relay capable of automatically revising

settings, changing characteristics and selecting algorithms for different conditions of protected facilities and their changing environment is one of the principal ways to improving performance of protective relays. Lack of available information related to knowledge and operational expertise of the facilities, and tight operation time of microprocessors, are the main obstacles to the realization of adaptive protection. Com- bining knowledge-based system technology with present digital protec- tive relays seems to be a promising approach to adaptive protection of power systems. (e) Process Control of Generator

Operational control of generators is quite complex because it must deal with multi-loop, multi-variable problem and lot of compli- cated dynamic factors during operational process. The need for expert system technology increases with increasing problem complexity, Thus, process control of generators is considered a good candidate for the application of expert system technology. An expert system can be. used as a tool for operators, as an element of the feedback loops in a single controller, and form a kind of expert control at the control sys- tem level---a promising approach to adaptive or self-organizing control at a high level. CONCLUSIONS

This paper gives an overview of applications of expert systems with a bibliographical survey of relevant background, practical require- menfs, historical events, the present state, techniques and applications currently being pursued. It is based on over 80 articles published since 1982 to the beginning of 1988. Some subsequent conferences with conmbutions in applications of expert systems in power are:

1. 2.

3.

IEEE Summer Power, Portland, U.S.A., July 24 - 29, 1988. Symposium on Expert Systems Applications to Power Systems, Stockholm, Sweden, August 22 -26, 1988. IFAC Workshop on Artificial Intelligence in Real-Time Control, Swansea, U.K., September 21-23, 1988. IFAC Workshop on Artificial Intelligence in Real-Time Control, Shengang, PRC, September 19 - 21, 1989.

4.

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Z.Z. Zhang (M'88) was born in Chongqing, China on January 27, 1945. He graduated in Electrical Engineering Department from Chongqing University in 1968. He received the M.Sc. degree in electrical engineering from NARI (Nanjing Automation Research Institute) of the Minishy of Water Resources & Electric Power of PRC, Nanjing, China in 1981, and the Ph.D. in electrical engineering from HUST (Huazhong University of Science and Technology, Wuhan, China in 1985. He was an electric engineer at substations and power stations during 1968-78.

Dr. Zhang is a member of the IEEE and CSEE. His interests are in microprocessor-based protection relaying of power systems, Integrated automation of substations, and application of knowledge- based system technology to power systems.

G.S. Hope (S'56 - M 6 7 - SM'72) was born in Edmonton, Alberta, Canada, on September 19, 1931. He received the B.Sc. degree in electrical engineering from the University of Alberta, Edmonton, Canada in 1957 and Ph.D. and D.1.C degrees in electrical engineering (power systems) from Imperial College, University of London, London, England, respectively in 1966.

From 1957 to 1961, he worked with Canadian General Electric Co. Ltd., Civilian Atomic Power Department, Peterborough, Ontario. In 1962 and 1963, he was employed as a Consultant to the Petrochemical Industry in Southern Alberta. He joined the staff at The University of Calgary, Department of Electrical Engineering, in 1967; at present, he holds the rank of Professor. Dr. Hope's current research interests are: Computer interfacing, digital relaying, system identification and power system's computer application.

O.P. Malik (M'66 - SM'69 - F'87) graduated in electrical engineering from Delhi Polytechnic, India, in 1952 and obtained the M.E. degree in electrical machine design from the University of Roorkee, India, in 1962. In 1965 he received the Ph.D. degree from the University of London, London, England, and D.I.C. from the Imperial College of Science and Technology, London.

From 1952 to 1961 he worked with electric utilities in India on various aspects of design, construction, and operation of power systems. For one year he was a Confederation of British Industries scholar in the United Kingdom. In 1965 he worked with the English Electric Company in England. He is now in Canada, where he taught for two years at the University of Windsor and is at present at The University of Calgary.

Dr. Malik is a fellow of the Institution of Electrical Engineers (London), a member of the Canadian Electrical Association, and the American Society for Engineering Education. He is a registered

'Professional Engineer in the Provinces of Alberta and Ontario, Canada

1362

Discussion

S . M. Shahidehpour, (ECE Dept, IIT, Chicago, IL): I would like to congratulate the authors for providing a comprehensive survey of the application of Expert System in Electric Power Systems. I would like to add the following papers to the list of references:

(1) M. Daneshdoost, S . Vijay, “Expert Systems as a Tool for Engineering Education-Application to the Power-Flow Control Problem”, Proceedings of the American Power Conference, Vol.

(2) S . Shah, S . M. Shahidehpour, “A Heuristic Approach to Load Shedding Scheme”, IEEElPES 1989 Winter Power Meeting, Paper No. 89WM193-4 PWRS.

(3) S . S . Shah and S . M. Shahidehpour, “Automated Reasoning: A New Concept in Power System Security Analysis”, IEEE International Workshop on Artificial Intelligence for Industrial Applications, Hitachi City, Japan, May 1988, IEEE Catalog No. 88CH2529-6. (There were more than 20 papers related to the application of expert system to power system presented in this conference).

50, April 1988, pp. 945-954.

(4) S . S . Shah and S . M. Shahidehpour, “Application of Expert System in the Design of Power System Security Analyzer”, Expert Systems and Applications-IASTED International Conference, Geneva, Switzerland, June 1988, pp. 14-18.

(5) S . S. Shah and S . M. Shahidehpour, “Application of Expert Systems to Security Analysis in a Power Network Environment”, Proceeding of the American Power Conference, April 1987, pp. 780-788.

(6) S . M. Shahidehpour and G. D. Kraft, “Applications of Artificial Intelligence to Distributed Processing in a Power System Environ- ment”, Proceedings of the 6th Power Plant Dynamics and Control, Knoxville, Tennessee, 1986.

Manuscript received February 27, 1989.

Z. Z. Zhang, G. S . Hope, and 0. P. Malik: The authors thank the discussor for the supplementary information.

Manuscript received April 3, 1989.