Electric Power Systems Research, 25 (1992) 239-246 239

An electric control center operator's assistant expert system

Nilson L. GSes Federal University of Pard, Beldm, PA 66000 (Brazil)

Earl F. Richards University of Missouri-Rolla, Rolla, MO 65401 (USA)

Edward D. Tweed Union Electric Co. of St. Louis, St. Louis, MO 63166 (USA)

(Received June 25, 1992)

Abstract

This paper presents two expert systems designed to serve as a control center operator 's assistant. The first is designed to assist operators in solving overload problems and the second is to assist in solving voltage problems. Both systems use the dis tr ibut ion factor concept to create indices for the decision-making process. A new improved technique to obtain the distr ibution factors is presented. That technique circumvents part of the problems introduced by the l inearizat ion implicit in the use of dis t r ibut ion factors and makes them suitable to be used to solve voltage problems. A 634-bus model of an actual electrical system and its in terconnect ions is used so that the results can be realist ically compared with the action that would be taken in the control center. Results are reached in a manner consistent with the operation of the control center and in a time frame that makes these expert systems feasible for real-time applications.

Keywords: voltage control, generation dispatch, expert control, knowledge based system.

Introduct ion

Expert control using knowledge based systems is one approach being explored to improve the operat ion of electric uti l i ty systems. System restorat ion [1], energy management [2, 3], secu~ rity assessment [4], automat ic contingency selec- t ion [5], voltage control [6-10], alarm processing [11, 12], and fault location and diagnosis [13, 14] are some of the problems which have been re- cently addressed by this technology. A feature which makes knowledge based systems appropri- ate to be applied in the solution of those prob- lems is that they can explain their reasoning and produce partial solutions if an optimal one is computat ional ly infeasible or cannot be found.

The expert systems presented in this paper use the distr ibution factor concept to create indices for the decision-making process. A For t ran pro- gram calculates the distr ibution factors which are then used by the rule based expert systems writ ten in Prolog. The backtracking inference engine built into the Prolog language is used to solve any conflict which arises when a par t icular problem solution degrades the operat ional condi- tions at other locations of the system.

Overload problems are solved by shifting the generat ion pat tern of the system. As a first at- tempt to solve these problems, generat ion inside the area of interest is shifted. If these actions are not enough to solve the problems, available gen- erat ion in the neighboring areas is used. Then, if problems still remain, load shedding is used as a last resort.

Voltage problems are solved by switching ca- pacitors and reactors, and by dispatching reactive power (VARs) from the synchronous compensa- tors and from the generat ion plants.

Overload problems and the operator's expertise Power flows in excess of equipment rat ings are

usually caused by multiple outages. In such cases, the flow that was originally carried by the equipment on outage will redistribute itself to the adjacent circuits. This presents the possibil- ity of overloading the remaining lines and associ- ated equipment, causing thermal damage.

Generally speaking, there is no (or very lim- ited) automatic protect ion on the system to pre- vent equipment overloads. Protective relays that control circuit breakers are designed to quickly and automatical ly isolate equipment, but they

0378-7796/92/$5.00 ~) 1992 Elsevier Sequoia. All rights reserved

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sense only very high cur ren t levels associa ted with short-circuits . Al though a very severe over- load may be in te rpre ted as a fault by the relays, resul t ing in equipment isolation, it is usual ly a dispatcher ' s responsibi l i ty to moni tor and con- trol system overloads.

The flow of AC electric power is d is t r ibuted ins tant ly and au tomat ica l ly according to the rel- at ive impedance of the paral lel circuits. Thus, it is not so easy to direct. To relieve potent ia l ly dangerous overloads, d ispatchers will often re- dispatch generat ion. This procedure involves in- creasing genera t ion in the vicini ty of the load, and s imul taneous ly decreasing genera t ion at re- mote locations, to unload the t ransmiss ion lines. Dispatchers may also switch cer tain circuits manual ly to redirect the power flow, make use of phase angle shift regulators , or cause a reduct ion in consumer demand through load management schemes or a vol tage reduct ion on the distribu- t ion system.

It is impor tan t to not ice that, wha tever the opera tor ' s decision to correct an over load present in the system, it is t aken based mostly on heuris t ics derived from his experience. This fact per se makes this problem an excel lent candidate for the appl ica t ion of expert systems.

Voltage prob lems and the operator 's expert ise Voltage problems may develop quite slowly, as

compared to f requency problems. They may occur, for instance, several minutes following a distur- bance, or even over a period of several hours, as load builds to very high, unexpec ted levels. When the vol tage changes are slow enough, system dispatchers can take act ion to help improve the vol tage profile. The control of vol tage levels is carr ied out by control l ing the generat ion, absorp- tion, and flow of reac t ive power in the electr ical system. The pr imary method of vol tage control is by ad jus tment of the vol tage regula tor set points on the genera t ing units to control the units ' exci ta t ion [15]. This ad jus tment is under the direc- t ion of the system dispatcher , th rough the hands- on control of the power plant operator . Out on the system, other equipment opera tes au tomat ica l ly to keep vol tage levels within reasonable ranges; examples include load tap changing t ransformers (LTCs), synchronous condensers, and stat ic VAR compensa tors (SVCs). In addition, shunt capac i tor and reac tor banks may be swi tched automat ical ly , depending on loading condit ions, t ime of day, etc.

The dispatcher typical ly identifies developing problems th rough experience, suppor ted by for- mal t ra ining and avai lable sof tware in the con-

trol center. The sof tware may consist of a power flow program that al lows dispatchers to test the secur i ty of the present opera t ing condi t ion through s imulat ion of contingencies. This com- puter program usual]y can au tomat ica l ly se- quence through a set of "wha t if" cont ingencies tha t are known to create vol tage problems.

A more recent technique that is being applied is to incorpora te opt imal power flow (OPF) methods to dispatch VAR supplies at the same time that power genera t ion is being scheduled. However, even though OPFs have usual ly been employed in an off-line mode to invest igate and determine vol tage/VAR schedules, it is very difficult to use them in real-time applications. Thus, the basic tool for solving over load and vol tage problems in an electric ut i l i ty opera t ion center still relies on heuris t ics derived from the opera tor ' s knowledge of the system (based on his experience).

Expert system technology can incorpora te heuris t ics derived from opera tor ' s expert ise and also can make use of external programs to calcu- late numbers which are used in the decision-mak- ing process. These are the basic ideas behind the expert system presented in this paper. It uses the concept of d is t r ibut ion factors in conjunct ion with heuris t ics derived from operators ' experi- ence to solve vol tage problems in electric power t ransmission systems.

P o w e r s y s t e m m o d e l

Using the fast decoupled power flow assump- t ions as a s tar t ing point [16], the following equa- t ions can be writ ten:

[0(k,]_[ A0(,, 1 Lv(,,,j [AV(k'J

(1)

(2)

(3)

AO (k~ = B' 1 AP/V~k)

A V (t~) = B" ' A Q / V °~

where 0 is the vector of vol tage angles on the generat ion ( P V ) and load (PQ) buses; V is the vector of vol tage magni tudes on the load (PQ) buses; AP is the vector of real power var ia t ions (P) injected into the system; AQ is the vector of react ive power var ia t ions (Q) injected i n to the system; A0 is the vector of vol tage angle var ia t ions due to AP; AV is the vec tor of vol tage magni tude var ia t ions due to AQ; B' 1 is the matr ix of the sensit ivi t ies be tween A0 and AP; and B"-1 is the matr ix of the sensit ivi t ies be tween AV and AQ.

Stot t and Alsac [16] show tha t the elements of B' and B" are the imaginary parts of the corre-

sponding elements of the admittance matrix for the system.

If the matrices B '-1 and B "-1 have been ob- tained, then eqns. (2) and (3) can be rewritten, respectively, as

A0 (k) = Z' A P / V (h) (4)

and

AV (k) = Z" 5Q/V (h) (5)

Alleviating an overloaded device Suppose that, in a given system, the line i - j is

overloaded by AP~. How can the knowledge above help to decide the best generation shift in order to alleviate that overload?

From the power flow equations,

A0 o ~ APij X~i (6)

where Xij is the reactance of the line from bus i to bus j and A0~ is the variation of the difference between the voltage angles at bus i and at bus j.

From eqn. (4) it is known that a change equal to 1.0 p.u. in the real power generation at bus k leads Oi and Oj to change by Z'~k/V~ and Z~k/Vh, respectively. Therefore, O~j changes by (Z'ih- Zjk)/Vk when the generation at bus k changes by 1.0 p.u. Knowing this, it is easy to calculate the necessary generation change at bus k, APk, in such a way that Pi~ changes by an amount equal to the negative of APi j :

APk - APiJ (7) DFij, h

where

D F i j k - - Z ' ik - - Z~k

is the distribution factor which relates the change in the flow of real power on line i - j to the change in the real power injected at bus k.

By using the distribution factors relating a given line to the generation buses in the system, one can select the most sensitive buses where generation can be shifted in order to correct overload problems on that line. Also, if load shedding is an option to be considered, the distri- bution factors relating the given line to the load buses in the system can be used to select the bus where load shedding produces the greatest over- load reduction on that line.

Correcting voltage problems Consider that, in a given system, the voltage

on bus i is A Vi p.u. below/above the minimum/

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maximum acceptable level. How can the knowl- edge above help to decide which is the best bus to inject reactive power in order to solve that voltage problem?

From eqn. (5) it is known that a change equal to 1.0 p.u. in the reactive power injected at bus k leads the voltage magnitude on bus i to change by Z'i'k/Vh. Knowing this, it is easy to calculate the necessary reactive injection at bus k, AQk, in such a way that Vi changes by an amount equal to the negative of AVi:

Ayi AQh = - - - Vk (8) Z'i

As the reference and generation buses are con- sidered as having fixed voltage magnitude, the axes which correspond to those buses do not appear in eqn. (5). This feature makes that equa- tion unsuitable to be used in the derivation of the distribution factors for the voltage and VAR control. This problem is overcome by: - defining all the generation buses in the area of interest as load buses; - choosing for reference a generation bus in a neighboring area.

By using the distribution factors relating a given bus to each bus where reactive power is available, one can select the most appropriate bus where VARs can be injected in order t o correct voltage problems on the given bus.

T h e P r o l o g e x p e r t s y s t e m s

The basic components of the expert systems developed in this project are a network database and a rule based problem analyzer [10, 17, 18].

The network database contains the network parameters, status, and measurements. The de- scription of the characteristics of the electrical system, interconnections, generation, voltage pro- file, and line flow, written in a format suitable to be read by the Prolog expert systems, constitute some of the information found in that network database. Most of that information comes from the output of a power flow program (in the case of off-line simulations), or from the output of the state estimator of the system (in the case of on-line applications).

The rule based problem analyzer uses the Pro- log backtracking inference engine to apply a structured set of rules and actions to the network until a goal state is reached.

The first action of the expert systems is to scan the results of a power flow simulation and to

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produce a list of over loaded lines and vol tage problems. The user is queried to ascer ta in if lines are out of service. If there are outages, a For t ran program is l inked to rebui ld the Z' and Z" matr ices and the dis t r ibut ion factors are recalculated. An over loaded line is selected to s tar t the process of rel ieving over loaded lines. This select ion depends on the operator ' s expertise. It may be the most over loaded line, the least over loaded line, or any other line the opera tor selects. By default, the expert system uses the most over loaded line. The expert system now proceeds to find the most appropr ia te genera t ion plants in which to shift generat ion. In order to mainta in the energy bal- ance, each genera t ion shift ac t ion must be per- formed in a pair of genera t ion plants the same amount of power tha t is increased/decreased in one of them should be decreased/ increased in the other. The exper t system uses the elements of the Z' matr ix to ca lcula te the d is t r ibut ion factors re la t ing the most over loaded line (MOL) to all genera t ion buses in the system. They are then used to assemble a list of all possible combinat ions of two genera t ion plants in which a genera t ion shift will decrease the power flow in the MOL. Using the table of d is t r ibut ion factors, the genera t ion plant pair ( " ID" and "Id") to which the MOL is most sensit ive is selected and the maximum amount of genera t ion al lowed to be shifted in those buses is calculated.

Similarly, a bus present ing a vol tage problem is selected to s tar t the process of correct ing vol tage problems. It may be the bus with the grea tes t vol tage deviat ion, the bus with the smallest vol tage deviat ion, or any o ther bus the opera to r selects. By default , the expert system uses the bus with the greates t vol tage deviat ion. Using the table of d is t r ibut ion factors, the VAR source to which the problem bus is most sensit ive is selected and the amount of react ive power required is calculated. If this source bus does not have the required VAR capabil i ty, the amount required will be spread over the next most sensi- t ive VAR sources. Then the impact of this VAR dispatch is assessed at all o ther buses in the system. As soon as one bus is encounte red at which the proposed VAR dispatch results in a vol tage going beyond the es tabl ished limits, the amount of VAR dispatch is reduced unti l the vol tage is wi thin the limits. The impact of this reduced VAR dispatch is assessed at all remain- ing buses. On the next cycle, the new vol tage at all buses is calculated. This process is repeated unti l all vol tages are wi thin the limits or unti l all VAR sources are exhausted.

A real izat ion of the above s t ra tegy in the Pro- log language is given below:

solve vol tage problems: scan vol tage _problems, check configuration, a t tempt to s o l v e vol tage problems.

a t tempt to solve vol tage problems: problem bus selected(K), most sens i t ive_VAR source(K, L), maximum VAR can _be dispatched(L, VAR), update system data(L, VAR), a t tempt to__solve_ voltage._~problems.

Besides being flexible enough to allow rules to be changed, added and deleted easily, this struc- ture al lows rules to be enhanced by opera t ional experience.

In a t tempt ing to prove a goal (for example, " a t t e m p t _ t o solve vol tage problems"), the Pro- log inference engine tries to prove the suppor t ing condi t ions for tha t goal. The suppor t ing condi- t ions can be ei ther facts, which can be proved by consul t ing the ne twork database , or o ther goals, to which an inference process similar to tha t applied to the original goal will also be applied. It is wor th not ic ing tha t a goal can have itself as a suppor t ing condi t ion in a recurs ive process.

Resu l t s

The results from the appl ica t ion of the expert system to a model of a midwestern power system are presented next. The Electric Power Research Ins t i tu te power flow program (EPRI EL-599 RP 745) is utilized as the electr ical system simulator. A 634-bus model of the Union Electric Company electr ical system and its in terconnect ions* is used so that the resul ts obta ined can be realisti- cally compared with the ac t ion tha t would be t aken in the control center.

Ini t ial ly the da tabase for the power flow pro- gram is a l tered to reflect problems in the electri- cal system. The power flow program is executed and the results are passed back to the expert system. Most of the knowledge base is produced in the repor t genera t ion phase of the power flow program. As the repor t is formatted, selected facts are wr i t ten to disk files in a list format compat ible with the Prolog environment . Only

*A 304-bus representation of surrounding systems in five of' the seven NERC regional councils, which comprise the eastern

interconnection.

lines and buses under the control of the Union Electric Company and a few neighboring buses of particular interest are passed to Prolog.

Expert system for alleviating overloads In order to test the expert system's capability

to relieve overloaded facilities in the electrical system, an overloaded transformer at a bulk sub- station on the 345 kV transmission system is sim- ulated by an outage of three 345 kV lines. In addition, the ratings of several transformers and lines are reduced in order to produce these prob- lems. The problems created by this action are listed in Table 1.

Since transmission lines are outaged, the Z' matrix is recalculated. This produces a greater accuracy than using line outage distribution fac- tors. This step requires approximately 50 s on a workstation rated at 10 MIPS.

The strategy to be considered to relieve over- loads involves shifting the generation pattern, utilizing generation available in the intertie with adjoining utilities, and shedding load if desired. The most severe problem is addressed first by the expert system. For this hypothetical situation, the Tyson 1 transformer overload is the first problem analyzed. All of the problems will be analyzed before a plan of action is presented to the user if options are available. The recom- mended generation changes are given in Table 2.

At this point in the execution of the expert system, no other options are available. No changes in the generation pattern within the Union Electric system can be made without ag- gravating problems at another transmission fa- cility. There is no other generation available from adjoining utilities. The remaining problems for this example are listed in Table 3.

TABLE 1. Overload problems

From bus (kV) To bus (kV) Circuit a (%)

Tyson 1 345 Tyson 1 138 1 (T) 37.00 Clark Av 115 Clark Av 34 1 (T) 14.00 Cape Gir 161 Viaduct 115 1 (T) 10.00 Cape Gir 161 Viaduct 115 2 (T) 8.33 Labadie 4 345 Tyson 3 345 1 (L) 4.34 Tyson 2 138 Orgd 1 138 1 (L) 2.84 Tyson 3 345 Tyson 2 345 1 (L) 2.76 Marshal l 138 Marshal l 34 2 (T) 2.00 Berk 2 138 Berk 2&3 34 2 (T) 1.00 Marshal l 138 Marshal l 34 1 (T) 1.00 Marshal l 138 Marshal l 34 3 (T) 1.00

aT = transformer; L = line.

TABLE 2. Recommended generat ion

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Bus (kV) Shift (MVA)

Mer 3 138 Increase 100.0 Mer 1 138 Increase 100.0 Venic 4 13 Increase 100.0 Venic 3 13 Increase 100.0 Venic 1 138 Increase 100.0 Page 13 Increase 100.0 Sioux 2 138 Increase 100.0 Labadie 4 345 Decrease 308.6 Sioux 1 138 Increase 100.0 Area # 2 138 Increase 19.8 Rush 345 Decrease 511.3

TABLE 3. Remaining overload problems

From bus (kV) To bus (kV) Circuit (%)

Clark Av 115 Clark Av 34 1 (T) 14.53 Cape Gir 161 Viaduct 115 1 (T) 10.00 Cape Gir 161 Viaduct 115 2 (T) 8.33 Marshal l 138 Marshal l 34 2 (T) 2.32 Berk 2 138 Berk 2&3 34 2 (T) 1.98 Marshal l 138 Marshal l 34 1 (T) 1.39 Marshal l 138 Marshal l 34 3 (T) 1.02 Berk 2 138 Berk 2&3 34 1 (T) 0.81

The above situation of having no remaining action to take to alleviate the problems, other than shedding load, is hypothetically reached by greatly altering the state of the electrical system and by reducing the amount of power available in the intertie. At this point, the operator is queried to see if load shedding is an option to be considered. For the purpose of demonstrating the expert system, a positive answer is entered for this normally negative option. Distribution fac- tors relating load buses to the overload device are calculated in order to find the load buses to shed that will result in the greatest reduction in the overload (Table 4).

After this action is taken, all of the overloads are alleviated. After the distribution factors are recalculated, the expert system requires 25 s to

TABLE 4. Recommended load shedding

Bus (kV) Action (MVA)

Clark Av 34 Shed load 23.8 Marshal l 34 Shed load 21.3 Berk 2&3 34 Shed load 13.8 Mt Auburn 34 Shed load 4,6 Calawy 1 345 Decrease gen. 35.1 Area # 5 13 Decrease gen. 28.4

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develop the s t ra tegy for a l leviat ing the over- loads.

Expert system for correcting voltage problems The state of the electr ical system was al tered

so that widespread low vol tage problems existed. Capaci tor banks tha t normal ly would be in ser- vice are off line and the VAR dispatch from the genera t ion plants is inadequate to mainta in a desired vol tage profile. There are no transmis- sion lines out of service and all genera t ion plants are on line. The set of rules which controls the scan of the vol tage profile detects and lists (Table 5) the buses with vol tage problems. These problems are located primari ly in nor th Missouri and sou theas t Iowa.

The user is asked to enter the maximum and minimum voltages. For this example, 1.07 p.u. and 0.97 p.u. were selected. The recommended correct ive act ions are to switch off a reac tor bank (Table 6) and switch on several capac i tor banks (Table 7).

T A B L E 5. Low vol tage p rob lems

Bus Vol tage (p.u.) Bus Vol tage (p.u.)

Lee 0.9168 Bluff T 0.9172 Sawyer 0.9233 Viele 0.9277 Carbide 0.9330 Comm T 0.9331 M e s s e n g r 0.9370 B R K F 160 0.9428 Page 0.9482 M arce l i n 0.9486 KH a X F M R 0.9504 H a m i l t o n 0.9547 Mex 119 0.9567 Mex 87 0.9582 Ki rk 150 0.9631 Berk 2 0.9634 K e o k u k 0.9649 H a m i l t o n 0.9666 Dundee 1 0.9649 Calu T 0.9676

T A B L E 6. Reac to r b a n k sw i t ch i ng

Loca t ion (kV) Swi tch B a n k s (MVAR) Mober ly 161 Off 1 50.0

T A B L E 7. Capac i to r bank sw i t ch i ng

Loca t ion (kV) Swi tch B a n k s (MVAR)

Viele 69 On 1 24.3 Carb ide 69 On 1 33.6 Mob 6 34 On 1 1.0 Page 34 On 2 58.8 M a r i o n 34 On 1 21.6 H u n t e r 34 On 1 16.8 Berk l&4 34 On 2 58.8

T A B L E 8. Resu l t s of sw i t ch ing capac i to r s and a r eac to r

Bus Vol tage (p.u.) Bus Vol tage (p.u.}

Lee 0.9593 Bluff T 0.9621 Sawyer 0.9680 Viele 0.9815 Carbide 0.9886 Comm T 0.9331 M e s s e n g r 0.9883 BRKF 160 0.9620 Page 0.9617 Marce l in 0.9670 KH a XFMR 0.9944 H a m i l t o n 0.9957 Mex 119 0.9701 Mex 87 0.9701 Kirk 150 0.9780 Berk 2 0.9744 K e o k u k 1.0070 Hami l t on 0.9994 Dundee 1 0.9777 Calu T 0.9778

The resul t of this act ion is tha t 13 of the 20 low vol tage problems are el iminated (Table 8).

Low vol tage still exists at seven buses. The next act ion is to dispatch VARs from some of the genera t ion plants. The amount of VARs needed to be dispatched from the genera t ion plant to correct the low vol tage is ca lcula ted with the use of d is t r ibut ion factors and the new vol tage that results from tha t act ion is l isted in Table 9.

After these changes, the expert system then predicts tha t three buses still present low voltage problems (Table 10) and tha t there is no sui table plant where VARs can be dispatched wi thout the condi t ion of another bus deter iorat ing.

After developing the recommended s t ra tegies for a l leviat ing vol tage problems, a power flow program is executed to verify the strategy. The vol tage predicted by the power flow program at the problem buses is compared with the values predicted by the expert system (Table 11). In

T A B L E 9. G e n e r a t o r VAR d i spa t ch

G e n e r a t i o n bus (kV) Ini t ia l vo l tage (p.u.) New vol tage (p.u.)

K e o k u k 13 0.965 1.007 Osage 138 0.930 1.041 Page 13 0.948 0.971 Sioux 2 138 1.036 1.043 T h o m Hill 345 1.030 1.070 Thorn Hill 161 1.030 1.068

TABLE 10. R e m a i n i n g problems

Bus Vol tage ( p. u.)

Lee 0.9593 Bluff T 0.9621 Sawyer (}.9680

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TABLE 11. Comparison of predicted voltages (p.u.)

Bus Predicted by Predicted by power flow sensitivity factors

Sawyer 0.9759 0.9680 Viele 0.9876 0.9815 Carbide 0.9925 0.9886 Comm T 0.9908 0.9870 Messengr 0.9921 0.9883 BRKF 160 0.9603 0.9700 Page 0.9653 0.9707 Marcelin 0.9648 0.9770 KH 3 XFMR 0.9969 0.9944 Hamilton 0.9981 0.9957 Mex 119 0.9660 0.9761 Mex 87 0.9677 0.9766 Kirk 150 0.9830 0.9872 Berk 2 0.9753 0.9817 Keokuk 1.0060 1.0070 Hamilton 1.0023 0.9994 Dundee 1 0.9903 0.9777 Calu T 0.9904 0.9778

most cases the voltage predicted by the distribu- tion factors is approximately 1% lower than the value predicted by the power flow program. This is a measure of the effect of the linearization that occurs in decoupling the power flow equations. The established voltage minimum for this hypo- thetical case is 0.97 p.u. The result of using distri- bution factors is that more VARs are dispatched than the amount required to meet the established voltage minimum.

The expert system required 15 s to detect the problems and to develop the plan of action for the V/VAR dispatch strategy on a workstation rated at 10 MIPS.

Conclusion

A demonstration knowledge based system for alleviating overloaded devices and for voltage and VAR control has been developed in the form of an operator 's assistant. It has been applied to a realistic model of an electric utility and has demonstrated that the decisions are reached in a manner consistent with the operation of an en- ergy management center. In addition, the deci- sions are reached in a time frame that makes these expert systems useful in an on-line environ- ment.

The use of distribution factors as indices to assist in the decision-making process has proved to be very effective. The linearization implicit in this methodology does not seem to affect the

selection of the actions to be taken. Their use as quantifiers for the magnitude of the actions that the operators are to take also has proved to be very satisfactory. Further refinement of the mag- nitude change calculation could be made with a second application of the expert system.

For the purpose of demonstrating the expert system for overloaded line relief, it was assumed that a specified amount of generation is available from adjoining utilities at the given instant of time that it is needed. This information is not available at present in the energy management center. It is not clear if it is feasible to obtain this data. Without the intertie information, the amount of generation available from the adjoin- ing utilities is to be set to zero. The strategy for relieving overloads will then consist of shifting only the generation pattern within the Union Electric system. In addition, it is not a desirable situation to have system operators spend their time entering this type of information into the expert system.

The voltage and VAR control expert system can also be used to determine a set of reactive power compensators as required to achieve se- cure power system operations, in a manner simi- lar to that proposed by Godart and Pfittgen [9]. Control regions and efficient controllers are read- ily determined. Sensitivity factors also give an excellent estimate of the effect of these con- trollers.

Acknowledgements

This work was supported by the Electric Power Research Institute under a continuation of EPRI RP 2944-2 and the Union Electric Company of St. Louis, Missouri. One of the authors (NLG) was supported by a paid study leave from the Federal University of Par~, Brazil, and a scholarship from CAPES (Coordenadoria de Aperfei~oamento de Pessoal de Nivel Superior), a governmental agency in Brazil.

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