The Application of Expert Systems and Fuzzy Logic to Electric Power Systems in Japan - Experience, Problems and Solutions-

Copyright © IFAC Control of Power Plants and Power Systems, Munich, Germany, 1992

THE APPLICATION OF EXPERT SYSTEMS AND FUZZY LOGIC TO ELECTRIC POWER SYSTEMS IN JAPAN - EXPERIENCE, PROBLEMS AND SOLUTIONSM. Kato l Toshiba Corporation . Heavy Apparatus Engineering Laboratory. Fuchu. Tolcyo. Japan

Abstract

first. practical expert system for power syst.ems in the world.

Thi~

paper descrihes the current. stat.t' of at.t.empt~ to introduce expert syst.ems and fuzzy logic method~ t.o elect.ric power ~yst.em~ in Japan . First experience~ and problems in t.he introduct.ion of expert syst.ems are presented frolll the st.andpoints of on-line and off-line syst.ems . Next. experienct's anel problems regarding t.he int.roeluct.ion of fuzzy lo~ic. which are fewer. are present.eel . Last.ly. we consider t.he fntu re prospect.s for the applicat.ion of new informat.ion processing t.echnologies such a..c; artificial int.elligence (AI). fuzzy logic and neural networks .

ahollt 1987 . . . Many off-line expert systems for engineering work support were reported . Applicat.ions include planning scheduled outage. operationaJ planning and power network analysis . Also many restorat.ion expt'rt syst.ems for larger networks(hulk power net.works. distribu t.ion uet.works) were report.ed.

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Characteristics of on-line and offline systems

On-line systems ... These ha.ve rather strict re-

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strict.ions on processing time since they generally have t.o deal with time series data.

Expert Systenls

The applicat.ion of expert. syst.ems t.o elect.ric power syst.ems in Japan wa.c; summarized in the report. of The Society of Elect.rical Cooperat.jve Research[I] . There 48 syst.erns developed for con t.rol, planning and diagnosis were report.ed from power ut.ilities. some of which were in operation. The numher is much greater if we include ~ystems developed at. universit.ies and IT\anufact.urers but. unpuhlished . This section describes t.he present. I
1.1

History

1982 . . . The first paper on the application of AI t.o fault diagnosis and restorat.ive operation in the transactions of IEEE[2].

Off-line systems ... These may have t.o process rat.her large amounts of data, aJthough the rest.riction on processing time is relaxed . Input. data are considered invariant. with time .

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Problems

Some problems requiring solution have heen discovered as A I is applied to on-line systems for larger scale networks such as hulk power networks . Some of these are essential for off-line systems as well . 1. Processing speed

In t.he ca.~e of restoration syst.ems. as the target network hecomes larger alld more complicated and various net.work operatio,,~ hecome acct'ptahle. processing t.ime t.o creat.e a restoration plan becomes \'('r~' 101lg. Possible solutions • Software . . .

1985 ... The field test.ing of the expert system on fault diagnosis and restorative operation in Kyushu Electric Power Co.[3] This was for a. ~ma.1I sca.le regional network. and may be the 1 currl"ntly at AI Application~ Institut.l". llniv/"rsity of Erlinburgh. 111{ . E-mail addrl..Ss :kato
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- Application of high-performance reasoning technique like t.he Rete algorit.hm[1j - Application of a universal software language such as C

- Applicat.ion of efficient. ~earch t.echlIiqlH'~ using heurist.ics

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• Hardware . ..

Methods llsed by recent on-line expert systems

Improvement. of processlllg speed of CPU

I will use t.he restoration expert system developed as a pilrt of Int.egrated Control Centre of Kyushu Electric Power Co. as an example.

Application of Al dedicated processor

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2. Knowledge acquisition Ext.ract.ion of knowledge from domain experts is a difficult t.ask which takes a long t.ime and is not efficient. for large-scale knowledge ha.c;es. This is closely connect.ed to knowledge represent.ation issues. Possible solut.ions Development of domain shell[5] . .. • Expert syst.em shell (1001) suit.able for each applicat.ion such a.c; diagnosis problems and planning prohlems and so OIl.

This control centre was commis!'>ioned in March. 199B. I t covers urban sections with 220kv. 11 Okv and 66k\' networks . It monitors and controls 45 suhstations and power plant.s . This expert system is int.egrat.ed with the control centre. The system has three super-minicomputers with To~hiba Artificial Intelligence Processors(AIP) as host corn pu ters . The AI P is used for high ~peed AI processing in parallel with conventional monitori ng and con trol processing.[i] 1.4.2

• Adequat.e knowledge represent.ation enahles t.he engineer t.o acquire expert.ise int.eract.ively and ea.c;ily. .1 . Maintenance of the consistency of the

knowledge base The larger the knowledge hase becomes. the harder it is to maintain the consistency of the knowledge base. It is also important who maintains the consistency of the knowledge base - and how this is done. Possible solutions

Outline

Solutions

Processing speed . . . As for soft.ware. applicat.ion of Rete Algorithm can bring high-speed reasolllng . As for hard ware, use of A I P as a hackend processor can improve t.otal processing. Knowledge vAlidntion . . , Validation has heen done for 'iO typical fault cases for reliahilitv . Processing time to idenlif~' fanlt~' devices and to make a rest.oration plan has heen estimated 20-30 and 60 - \ .50 seconds re~pec­ lively. How to trent time-series dAtA ... This system makes operational plans . nsing da.ta at. a certain moment. after a fall It. as input. Dnring the restoration process. if there are some differences hetween on-line data and estimat.ed data hy simulation. then this system can remake plans. using d at.a at. this time as an inpnt. at an operator 's request .

• Analysis of knowledge hase to detect errors (contradiction, redundancy and so on) . • The use of ATMS-based t.echniques[6] to maintain and ensure t.he consistency of rules in t.he knowledge base. 4. Knowledge validAtion

There are still prohlems remaining which t.he system cannot. address. these are currently heing studied . There are some on-line s~'stems which are realized with conventional methods like decision tahies which have heen made clear hy a prot.ot.ype expert. syst.em.

The reliahility of reasoning results is important. for off-line systems, still more for on-line ~~' st.ems.

Possible solutions No general methods . Validat.ion by simulation for t.ypical cases is most practical and u~u al.

2 .5. How to treat time-series dAta On-line system is required to treat time-series data . new input of which may cause contradiction during t.he reasoning process. Possihle solutions Applicat.ion of temporal reasoning and Truth Maintenance System{TMS) . These may result in increased process time and required memory size.

Fuzzy Logic Application SystelTIS

Fuzzy logic represents human feeling b.y a numerical vallle. or more accnrat. ('I~' by t.he memhership fll nction . Recently ma.ny pract.ical syst.ems with fllzzy cont.rollers. which enahle control of the same t'\ualit .v as domain expert.s. have been developed for a \'ariet~' of a.pplication fields .

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2.1

History

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1965 Proposal of fuzzy llets by Prof. Zadeh[8]

I. How to rledde a memhel'ship funct.ion Thf're are no general methods t.o decide memhership function which represents subject.ive valups like /cH'ge, mlnll. !'ef'Y inl'ge. etc. The membership function should alt.er if the structure of a S.\'stPIII ma.\' var.\'. At present. e:ocperts adjust. memhership funct.ions by t.rial and error. this is not desirable fro III the standpoint. of automat.ion and emcienc~'. It, is most important, for practical usp that membership functions are decided and adjust.ed automatically.

1974 A pplication of fuzzy logic to llt.eam engine controller by Prof. Mamdani[9]

Many application systems for cement kiln ,boilers and so on have heen report.ed since then. In Japan, systems ranging from underground. t.o washing machines and vacuum cleaners apply fuzzy logic now.

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Prohlems

Application to electric power systems ill Japan

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There are currently few application!' of fuzzy logic to elect.ric power sYlltems. because it is not so diffindt. t.o express electric power syst.ems hy numerical models . On t.he ot.her hand. many applicat.ions t.o power plant. control have been reported. such as starting procedure support. !'yst.ems and boiler control systems for power plant.s , because power plants are difficult, to express by numerical models. All of these remain prototype systems. unlike the expert. systems to electric power syst.ems. I will outline three applicat.ions to electric power syst.ems which are considered nearly practical and t.hen discuss some problems to be solved for applicat.ions t.o he of practical use.

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Possihlf~

solutions Methods using neural networks t.o adjust. membership functions adapt.ivcly are now being studied and seem t.o be promising.[14]

Future Prospects J. As applicat.ions of new information technologies t.o electric power syst.ems are now being actively studied. many application 5."Stems for practical use will a pppar in t he near future. 2. As for AI. particularly expf'ft. systems, t.hey ;'vill he developed so that t.hey may fulfill high performance. They will be intpgratpd with convent.ional systems so t.hat. t.hey may be ea.c;ier for operat.ors 1.0 use . The ATMS t.echniques cited parlier have t.he possihilit.y to solve some problems in clevploping expert systems. while requiring longer reasoning t.ime and more memory size.

1. Water inflow forecast system [I 0] In t.his syst.em, water inflow t.o a reservoir forecast, model is organized by applying fuzzy logic. using many years' data. Manyapplicat.ions are used for control, although t.his one is not . It is report.ed t,hat more accurate foreca.c;t result!!! can he obtained hy this system t.han h)· ones wit.h regression models.

3. At present. , there are no practical applicat.ions of fuzzy logic in electric power systems in Japan . In the near future. some practical application systems must be developed froin systems stated earlier. There seems t.o be no practical syst.em using only fuzzy logic. Integration of fuzzy logic and other technologies like neural net.works which help to decide and adjust membership functions may be essential for practical use.

2. Generat.or exci I.ation con trol system[ll, 12] This s.vsl.em realizes high performance integrated AVR and PSS. Unlike conventional syst.ems hased on control I.heory. it is report.ed to he robust regardle:o:s of the operat.ing conditions of the generat.or and length of t.ransmission lines.

4. Although there are many reports on the applicat.ion of neu ral net.works to electric power systems and t.hey are t.u rni ng ou t, t.o be promising for future syst.ems. · they seem t.o npeel further research for pract.ical usp . Systems based "solely" on by neural networks are u nlikel~' to be practical. Moreover another problem is that users feel uneasy a.bout. reliability of the output from neural networks. Such problems have to be overcome for practical use.

3. Rest.oration system[13] This system. which is not. for control lIse like the first example above, integrat.es fuzzy logic with expert systems . This may he cla.c;:o:itied in to expert systems. This s.vstem applies fuzzy logic, hy which satisfaction degrees of each operation for operators are expressed, in order to get the most satisfact.ory operat.ional procedures for operators. because t.here are some uncertain factors like load pick-up during restoration process,

5. There is no general method to develop systems which apply fuzzy logic and neural net483

works. Met.hodologies t.o develop applicat.ion svstems have to he established as they have been for expert systems .

References [I} Y.Arai et al. "The Report of Rf..I,D Group for .H in POII'er Utilities~ Society of Electrical Cooperative Research. Sep . 1990 (in Japanese)

P] T.Sakaguchi. KMatsumot.o. "Development of a knowledge-based System for' Power Sy.,lem Re .• 'orntion" IEEE Trans. of Power Appar. s.:. Syst.. Vol. PAS-102. No.:!, 198.3

[3J S. l\foriguchi et. al. "A n Erpe'" System for Power' System Fault A nalysis and Reslor'alion" CIGRE SC39 Tokyo Meeting , Oct.. 198i [4] C.L .Forgy "Rete : A fast algor'ifhm for the many patterns/many object pottern match problem" Art.ificial Intelligence, 19, 1982 [5J S. Moriguchi et al. "Expert Shell for Power System Diagnosis" Proc. of International Workshop on Artificial Intelligence for Industrial Applications 1988, May, 1988 [6] T.Tanaka et al. "An ATMS-haMd l\nowledge Verification System for Diagno .• i.. A pplicnlions" Proc. of The World Congress on Expert Systems, Dec., 1991 [iJ M.Knnugi "Practical Applications of Exper·t Sy~tem~ in Power Sy~tems and Future Trends - A M anufacturer '~ Viewpoint -" Panel Discussion. Third Symposium on Expert. System!' Application t.o Electric Power S~'st. ems, April. 1991 [8J L.A.Zadeh "Fuzzy Sets" Control, Aug .. 1965

Information and

[9] E.II .Mamdani "Application of Fuzzy ..1lgorlfhm~ for Control of SimT)le Dynnmie Plnnt" Proc . of lEE. Vo1.l21. No .912. 19;4 [10] Y.Shimakura et al. "Foreca.! t Methods on Wnfer [nflow to A Re.! er"oir" IEE.J Proc. of Technical Meeting on Power En~ineering, Sept.. 1991 (in Japanese) [11] T .Hi.vama "Rule-baud Stnbi/iznfiorl for' Multi- mnchine System" IEEE Trans . of Power Systems. Vol. 5. No.2. May, 1990 [12J Y.Kitallchi . H.Taniguchi "Generntion Ercitalion Control Method U.• in9 Fuzzy Theor'y Del1f'lopment of Fundamental Configurntion"Technical ReportsoCCnIEPI. March. 1991 (in Japanese)

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[13] Y .Shimakura et al. "A l\nowlpdge-bnsed Approneh to Power Sysfem Operational A;~ Proc . of Third Symposium on Expert S~'s­ tems Applicat.ion to Power Systems. April. 1991 [14] H.Takagi "Fu., ion Technology of Fuzzy Theory nnd Neural Networks" Proc. of Int.ernat.ional Conference on Fuzzy Logic & Neural Networks. IIZUI