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An expert decision support system for assisting power system operators to correct real power flow violations
Electric Power Systems Research, 16 (1989) 47 - 51
47
An Expert Decision Support System for Assisting Power System Operators to Correct Real Power Flow Violations S. A. KHAPARDE and A C H U T H S A N K A R S. NAIR
Department of Electrical Engineering, Indian Institute of Technology, Bombay 400 076 (India) S. BISWAS
Department of Computer Science and Engineering, Indian Institute of Technology, Bombay 400 076 (India)
(Received July
25, 1988)
ABSTRACT
2. PROGRAMMING THE EXPERT SYSTEM
This paper presents an expert decision support system which incorporates operator knowledge for correcting real power flow violations in a power system. Details of the implementation are given and the potential of the system is assessed. Rule-based programming techniques are used, the tool being the production system language OPS5+.
OPS5+ is a Lisp-based shell for implementing expert systems. Figure 1 depicts the basic components of an expert system implemented in OPS5+. The three basic blocks are: (i) the working m e m o r y , (ii) the production memory, and (iii) the inference engine. The working m e m o r y contains data about the problem at hand (domain) represented as attribute lists which are of the general form: (item-name
attr: name-1 attr: name-2
attr: value-1 attr: value-2
attr: name-n
attr: value-n)
1. INTRODUCTION
Expert system techniques are already becoming well-established in the power system field. A comprehensive review of the recent developments in this direction is given in ref. 1. Some of the earlier works have been on power system restoration [2, 3], a typical domain for which expert system application is justifiable. These were followed up by works in areas like voltage control (reactive power flow control), fault diagnosis, operation, maintenance, planning, design, operator training, HVDC control [ 4 - 15], etc. Basically, programming operator heuristics have been the challenge in most of these works and the outlook is promising. In this paper we present an expert system which will suggest operator action to be taken in the case of minor real power flow violations in the power system. We begin with a short description of the design and implementation of an expert system in the production system language OPS5+. 0378-7796189/$3.50
Such attribute lists, consisting of pairs of attribute names and attribute values, are used to portray the objects involved in the problem. The production m e m o r y contains the procedural knowledge cast into the form of I F - T H E N rules, or production rules. The general form of a production rule is: (IF
THEN
((al) and ( a 2 ) . . . (an) and (-bl) and ( - b 2 ) . . . ( - b n ) ) : iaction-1 (action-2)... (action-n))
Working
Memory
Production Memory
I
I < Inference Engine' >
Focts Inferences
Fig. 1. Expert system components in OPS5+. © Elsevier Sequoia/Printed in The Netherlands
48
The rules m a y be considered as actions to be taken at each state-point in the ndimensional space defined b y the attributes of different objects. This, however, does not degenerate the expert system into an exhaustive look-up table which contains the actions to be taken at all state-points. It is the intelligent selection of the state-points for which actions are to be programmed that makes the expert system superior to the 'glorified programming' of a look-up table. The control mechanism for the inference engine is inbuilt in the OPS5+ language. Once certain rules are found to have their conditional parts true, those rules are instantiated into the so-called conflict set. Inbuilt conflict resolution strategies in OPS5+ (LEX/ MEA) help to select a rule from the conflict set, which is then executed. Reference 16 deals in detail with the finer aspects. OPS5+ provides some language extensions to OPS5, such as the order of rules and working m e m o r y elements, new right-handside actions; c o m m e n t protocols, and a major new language facility -- rule priorities.
3. REAL POWER FLOW CONTROL
It is not necessary to stress the importance of real p o w e r flow control in monitoring power system networks. A linear programming (LP) package for flow redistribution is resorted to in severe cases. However, under minor violations of line-loading (alarm state), the control facilities at disposal have to be put into use to bring the system back to normal. Assuming that the violations are minor, these actions will not seriously affect the economic dispatch which might have been on the system. In practice, the possibility of cascade tripping can be counteracted by such operator actions. Storing the knowledge relevant to the problem and mimicking the operator action are the topics of interest in this paper. The knowledge pertaining to the real power flow problem is represented by the control options available in the event of power flow violations: (a) adjustment of the phase-shift transformer; (b) generation shift of the most sensitive generator to redistribute power flows;
(c) switching of the transmission network; (d) adjustment of interchange with neighbouring systems. We will focus our attention on the first three actions, assuming that we are handling a single-area system. For generation shift and line switching processes, we will make use of two approximate factors, used normally for contingency analysis [ 17 ]. 3.1. Generation shift factor The generation shift factor ali is defined as all = AfzlAPi where Afz is the change in real power flow on line l when a change in generation, APi, occurs at bus i. It is assumed that AP i is exactly compensated b y an equal and opposite change in generation at the reference bus, all other generations remaining fixed. The a , factor represents the sensitivity of the flow in line l to a change in generation at bus i. In our case, if we are interested in reducing the power flow in a particular line l, we select the generator with greatest a , and raise or lower the generation according to the sign of a , . The amount b y which generation must be raised is overload at line l air
To limit the number of rules, we will limit the generation adjustments to the two most sensitive generators only. If overload cannot be relieved by these adjustments, other actions have to be taken. 3.2. Line-outage distribution factor The line-outage distribution factor dzk is defined as dlk = Afl/f~ where hfz is the change in real power flow in line l and f~ is the original flow in line k before outage. For each line in the network, the line with the highest dzk is located and is switched ON or OFF, depending on the sign of dlk, as a remedial measure for correcting overload of the original line. Switching of lines is done only if the line is of low capacity (lines are ranked depending u p o n their capacity), otherwise it may lead to cascade tripping.
49
The sensitivity factors can be calculated and stored beforehand. The sensitivity factor will change if any significant switching operations are carried out. Either the changes have to be neglected or on-line updating of the sensitivity factors has to be provided. For simplicity, in the present system these changes are neglected.
4. FORMULATION IN THE EXPERT SYSTEM FRAMEWORK
We identify the basic objects involved in our problem as generators and lines. Finalizing the attributes is a gradual process, as they cannot be decided u p o n without finalizing the rules. The rules themselves cannot be finalized w i t h o u t deciding the attributes of the objects. Thus, it is a mutually interactive process. The following is a list of some of the attributes for LINE, declared in the expert system described in this paper: LINE line-id
l.i_ne_--.c, h~g_e. .1.m..e:.f ." _a..ul.t_ 1.i.n..e:~. F.m..e.:_q !i_n.e:.s. line-s-max line~verload most-sensitive-gl next-sensitive-g2 line-ol-value phase-shifter-availability
.s.h."ff.t.er_:_se.t.t_i_.nz shifter-minimum shifter-lowerability gl-shift-factor g2-shift-factor line-rank line-with-highest-dlk line-monitor-status Similarly, a list of attributes is also declared for G E N E R A T O R . The broken underlining indicates that the values of the respective attributes are to be renewed from time to time from the on-line data bank representing the system. The full underlining indicates that the values of these attributes are to be inferred by the expert system itself.
5. PRODUCTION RULES
As pointed o u t earlier, rules are actions to be taken at selected state-points of the attribute space. Using the knowledge listed in § 3, the actions can be worked out. Selected state-points are derived by picturing different situations through 'knowledge trees', as shown in Fig. 2. This process decides the efficiency and flexibility of the expert system. After all such required state-points are located, the best possible action under each situation is decided u p o n by using the available knowledge, in consultation with an expert if necessary. Once this process is complete, the rules are ready to be programmed. The state-point 1 in Fig. 2 can be cast into an OPS5+ rule as follows:
Line is Overloaded
PhaseFshift Transformer Available
Ph~s! shift Transformer not Avodable
L Shifter Transformer Setting is adjustable
I 1
Shdter Transformer Setting is not adjustable
Genera ion of Most sens=hve Genr adJustable
Generation of Most sensitiv~ Genr
ad jistable
2 Fig. 2. An example of locating state-points of the attribute space for the real power flow control problem.
50 (p
--->
Bus3
Rule-1 (line ^line-id (idl) ^line-monitor-status yes ^line-overload yes ^phase-shift-transformer available ^ shifter-lowerability yes)
Bus 2 50.OE
t
44.9~
~24.8 ~16.2
(write(crlf) ... Line overload on line no.
47
An Expert Decision Support System for Assisting Power System Operators to Correct Real Power Flow Violations S. A. KHAPARDE and A C H U T H S A N K A R S. NAIR
Department of Electrical Engineering, Indian Institute of Technology, Bombay 400 076 (India) S. BISWAS
Department of Computer Science and Engineering, Indian Institute of Technology, Bombay 400 076 (India)
(Received July
25, 1988)
ABSTRACT
2. PROGRAMMING THE EXPERT SYSTEM
This paper presents an expert decision support system which incorporates operator knowledge for correcting real power flow violations in a power system. Details of the implementation are given and the potential of the system is assessed. Rule-based programming techniques are used, the tool being the production system language OPS5+.
OPS5+ is a Lisp-based shell for implementing expert systems. Figure 1 depicts the basic components of an expert system implemented in OPS5+. The three basic blocks are: (i) the working m e m o r y , (ii) the production memory, and (iii) the inference engine. The working m e m o r y contains data about the problem at hand (domain) represented as attribute lists which are of the general form: (item-name
attr: name-1 attr: name-2
attr: value-1 attr: value-2
attr: name-n
attr: value-n)
1. INTRODUCTION
Expert system techniques are already becoming well-established in the power system field. A comprehensive review of the recent developments in this direction is given in ref. 1. Some of the earlier works have been on power system restoration [2, 3], a typical domain for which expert system application is justifiable. These were followed up by works in areas like voltage control (reactive power flow control), fault diagnosis, operation, maintenance, planning, design, operator training, HVDC control [ 4 - 15], etc. Basically, programming operator heuristics have been the challenge in most of these works and the outlook is promising. In this paper we present an expert system which will suggest operator action to be taken in the case of minor real power flow violations in the power system. We begin with a short description of the design and implementation of an expert system in the production system language OPS5+. 0378-7796189/$3.50
Such attribute lists, consisting of pairs of attribute names and attribute values, are used to portray the objects involved in the problem. The production m e m o r y contains the procedural knowledge cast into the form of I F - T H E N rules, or production rules. The general form of a production rule is: (IF
THEN
((al) and ( a 2 ) . . . (an) and (-bl) and ( - b 2 ) . . . ( - b n ) ) : iaction-1 (action-2)... (action-n))
Working
Memory
Production Memory
I
I < Inference Engine' >
Focts Inferences
Fig. 1. Expert system components in OPS5+. © Elsevier Sequoia/Printed in The Netherlands
48
The rules m a y be considered as actions to be taken at each state-point in the ndimensional space defined b y the attributes of different objects. This, however, does not degenerate the expert system into an exhaustive look-up table which contains the actions to be taken at all state-points. It is the intelligent selection of the state-points for which actions are to be programmed that makes the expert system superior to the 'glorified programming' of a look-up table. The control mechanism for the inference engine is inbuilt in the OPS5+ language. Once certain rules are found to have their conditional parts true, those rules are instantiated into the so-called conflict set. Inbuilt conflict resolution strategies in OPS5+ (LEX/ MEA) help to select a rule from the conflict set, which is then executed. Reference 16 deals in detail with the finer aspects. OPS5+ provides some language extensions to OPS5, such as the order of rules and working m e m o r y elements, new right-handside actions; c o m m e n t protocols, and a major new language facility -- rule priorities.
3. REAL POWER FLOW CONTROL
It is not necessary to stress the importance of real p o w e r flow control in monitoring power system networks. A linear programming (LP) package for flow redistribution is resorted to in severe cases. However, under minor violations of line-loading (alarm state), the control facilities at disposal have to be put into use to bring the system back to normal. Assuming that the violations are minor, these actions will not seriously affect the economic dispatch which might have been on the system. In practice, the possibility of cascade tripping can be counteracted by such operator actions. Storing the knowledge relevant to the problem and mimicking the operator action are the topics of interest in this paper. The knowledge pertaining to the real power flow problem is represented by the control options available in the event of power flow violations: (a) adjustment of the phase-shift transformer; (b) generation shift of the most sensitive generator to redistribute power flows;
(c) switching of the transmission network; (d) adjustment of interchange with neighbouring systems. We will focus our attention on the first three actions, assuming that we are handling a single-area system. For generation shift and line switching processes, we will make use of two approximate factors, used normally for contingency analysis [ 17 ]. 3.1. Generation shift factor The generation shift factor ali is defined as all = AfzlAPi where Afz is the change in real power flow on line l when a change in generation, APi, occurs at bus i. It is assumed that AP i is exactly compensated b y an equal and opposite change in generation at the reference bus, all other generations remaining fixed. The a , factor represents the sensitivity of the flow in line l to a change in generation at bus i. In our case, if we are interested in reducing the power flow in a particular line l, we select the generator with greatest a , and raise or lower the generation according to the sign of a , . The amount b y which generation must be raised is overload at line l air
To limit the number of rules, we will limit the generation adjustments to the two most sensitive generators only. If overload cannot be relieved by these adjustments, other actions have to be taken. 3.2. Line-outage distribution factor The line-outage distribution factor dzk is defined as dlk = Afl/f~ where hfz is the change in real power flow in line l and f~ is the original flow in line k before outage. For each line in the network, the line with the highest dzk is located and is switched ON or OFF, depending on the sign of dlk, as a remedial measure for correcting overload of the original line. Switching of lines is done only if the line is of low capacity (lines are ranked depending u p o n their capacity), otherwise it may lead to cascade tripping.
49
The sensitivity factors can be calculated and stored beforehand. The sensitivity factor will change if any significant switching operations are carried out. Either the changes have to be neglected or on-line updating of the sensitivity factors has to be provided. For simplicity, in the present system these changes are neglected.
4. FORMULATION IN THE EXPERT SYSTEM FRAMEWORK
We identify the basic objects involved in our problem as generators and lines. Finalizing the attributes is a gradual process, as they cannot be decided u p o n without finalizing the rules. The rules themselves cannot be finalized w i t h o u t deciding the attributes of the objects. Thus, it is a mutually interactive process. The following is a list of some of the attributes for LINE, declared in the expert system described in this paper: LINE line-id
l.i_ne_--.c, h~g_e. .1.m..e:.f ." _a..ul.t_ 1.i.n..e:~. F.m..e.:_q !i_n.e:.s. line-s-max line~verload most-sensitive-gl next-sensitive-g2 line-ol-value phase-shifter-availability
.s.h."ff.t.er_:_se.t.t_i_.nz shifter-minimum shifter-lowerability gl-shift-factor g2-shift-factor line-rank line-with-highest-dlk line-monitor-status Similarly, a list of attributes is also declared for G E N E R A T O R . The broken underlining indicates that the values of the respective attributes are to be renewed from time to time from the on-line data bank representing the system. The full underlining indicates that the values of these attributes are to be inferred by the expert system itself.
5. PRODUCTION RULES
As pointed o u t earlier, rules are actions to be taken at selected state-points of the attribute space. Using the knowledge listed in § 3, the actions can be worked out. Selected state-points are derived by picturing different situations through 'knowledge trees', as shown in Fig. 2. This process decides the efficiency and flexibility of the expert system. After all such required state-points are located, the best possible action under each situation is decided u p o n by using the available knowledge, in consultation with an expert if necessary. Once this process is complete, the rules are ready to be programmed. The state-point 1 in Fig. 2 can be cast into an OPS5+ rule as follows:
Line is Overloaded
PhaseFshift Transformer Available
Ph~s! shift Transformer not Avodable
L Shifter Transformer Setting is adjustable
I 1
Shdter Transformer Setting is not adjustable
Genera ion of Most sens=hve Genr adJustable
Generation of Most sensitiv~ Genr
ad jistable
2 Fig. 2. An example of locating state-points of the attribute space for the real power flow control problem.
50 (p
--->
Bus3
Rule-1 (line ^line-id (idl) ^line-monitor-status yes ^line-overload yes ^phase-shift-transformer available ^ shifter-lowerability yes)
Bus 2 50.OE
t
44.9~
~24.8 ~16.2
(write(crlf) ... Line overload on line no.
- ...) (write(crlf) Lowering of phase-shift transformer suggested))
70.
Bus 1 Bus 5 ~1
•
~
0,3
~33 1 --~ 41-6
The production m e m o r y of the expert system consists of more than 80 rules grouped into different modules to speed up the inferencing process. A few rules in the production m e m o r y are listed below to give an overall view of the expert system implementation. They are followed in the case of minor overload in any line under monitoring. (1) If a phase-shifter transformer is available on the line and the shifter setting is adjustable, then adjust the shifter setting to reduce the line flow. (2) If a phase-shifter transformer is not available and the generation of the most sensitive generator (generator having highest a , with respect to the line) is adjustable, adjust its generation, making an equal and opposite change in the slack bus generator. (3) If a phase-shifter transformer is available and the shifter setting is not adjustable and the generation of the most sensitive generator is adjustable, adjust its generation, making an equal and opposite change in the slack bus generation. (4) If a phase-shifter transformer is not available and the generation of the most sensitive generator is n o t adjustable and line switching is also not possible, suggest running the LP package for reallocation of line flows.
Bus 6
~44.9
~ 4 , 1 I ~16 9
70.0
Bus 4
Where ---~MW (~generator
-~3
I-~
Iood
70 0
Fig. 3. Samplesystem for illustration. (LINE line-id 2 phase-shifter-availability most-sensitive-g1 2 next-sensitive-g2 3 gl-shift-factor-0.31 g2-shift-factor-0.29 line-with-highest-dr k 1 highest-dzk-value 0.41 fault-status no charge-status yes line-p 41.6 line-p-max 42.0)
no
To test the working of the expert system, an overload was created in line No. 2 by setting a suitable line-s-max value in the working memory. The expert system, after detecting the overload, responded with the following suggestion:
6. ILLUSTRATION AND RESULTS This expert system was not tested as an on-line system, but the conditions were simulated b y suitable data changes in the working memory. Hypothetical data for the system shown in Fig. 3 [17] were fed into the expert system as attribute lists. For example, the data pertaining to line 2 were entered in the expert system program as the following working m e m o r y element:
" T r y increasing the generation of gen. no. 2 to maximum value, compensating the change on the slack bus, to relieve the overload of line no. 2 (Rule-42)". The effectiveness of this action was checked b y running a power flow program with the system data changed to simulate the effect of the action. The results were positive.
51 T h e possibility o f a c o r r e c t i v e a c t i o n creating a n o t h e r overload m u s t a l w a y s be c o n sidered. F o r each o v e r l o a d p r o b l e m , t h e exp e r t s y s t e m s h o u l d k e e p t r a c k o f t h e corrective a c t i o n s t a k e n , a n d if it is f o u n d t o initiate an u n r e a s o n a b l e n u m b e r o f a c t i o n s o r if a n y l o o p i n g b e t w e e n t h e rules is f o u n d t o o c c u r , t h e e x p e r t s y s t e m s h o u l d h a n d over t h e p r o b lem to t h e LP package. M a n y cases similar t o t h o s e stated a b o v e were simulated and tested. T h e results were encouraging.
7. CONCLUSIONS This p a p e r gives a p r e l i m i n a r y i n t r o d u c t i o n t o t h e c o m p l e x a n d challenging j o b o f d e v e l o p i n g e x p e r t decision s u p p o r t s y s t e m s (EDSSs) f o r p o w e r systems. T h o u g h t h e testing o f t h e E D S S was d o n e o n a small s y s t e m a n d in off-line m o d e , t h e results w e r e promising. M a n y r e f i n e m e n t s are envisaged b e f o r e t h e E D S S g u a r a n t e e s flawless operation. A well-established m e t h o d f o r testing a n d r e f i n e m e n t o f an e x p e r t s y s t e m is t o let it w o r k in t a n d e m w i t h a h u m a n e x p e r t w h o will r a t i f y its suggestions. T h r o u g h this e v o l u t i o n a r y process, t h e k n o w l e d g e base will b e c o m e m o r e a n d m o r e c o m p l e t e , a n d such an E D S S will p r o v e a b o o n t o p o w e r s y s t e m o p e r a t o r s . Training a n d s i m u l a t i o n can also be d o n e effectively using E D S S s w i t h some m i n o r changes. T h e learning capacities, if e x p l o r e d , can c e r t a i n l y i m p r o v e t h e e f f i c i e n c y o f an EDSS, especially in t h e e v o l u t i o n o f t h e k n o w l e d g e base. M u c h m o r e research r e m a i n s t o be d o n e in this d i r e c t i o n .
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-- a review, Proc. Int. AMSE Conf. on Simulation and Modelling, New Delhi, India, 1987, Vol. A, Assoc. for Advancement of Modelling and Simulation Techniques in Enterprises, pp. 147 - 158. 2 T. Sakaguchi and K. Matsumoto, Development of a knowledge based system for power system
restoration, IEEE Trans., PAS-102 (1983) 3 2 0 329.
3 E. D. Tweed and R. M. Butler, The use of PROLOG in power system restoration, Electr. Power Syst. Res., 9 (1985) 293 - 295. 4 C. C. Liu and K. Tomsovic, An expert system assisting decision-making of reactive power/ voltage control, IEEE Trans., PWRS-1, (1986) 195 - 201. 5 J. Kawakami and S. Tamura, An expert system for voltage-VAR scheduling, Proc. 9th Power System Computation Conf. (PSCC), Cascais, Portugal, 1987, Butterworths, London, pp. 702707. 6 K. Komai and T. Sakaguchi, Artificial intelligence methods for power system fault diagnosis, Proc. Int. Conf. on Power System Monitoring and Control, Conf. Publn. No. 266, Inst. Electr. Eng., London, 1986, pp. 355 - 360. 7 S. N. Talukdar and E. Cardozo, Artificial intelligence technologies for power system operations, EPRI Rep. EL-4323, Electr. Power Res. Inst., Palo Alto, CA, 1986. 8 S. N. Talukdar, E. Cardozo and T. Perry, The operator's assistant -- an intelligent expandable program for power system trouble analysis, IEEE Trans., PWRS-1 (1986) 182 - 187. 9 B. J. Cory and R. B. I. Johnson, Expert decision support in power system operation, Proc. Int. Conf. on Power System Monitoring and Control, Conf. Publn. No. 266, Inst. Electr. Eng., London, 1986, pp. 361 - 366. 10 C. A. Podbury and T. S. Dillon, An intelligent knowledge based system for maintenance scheduling in a power system, Proc. 9th Power System Computation Conf. (PSCC), Cascais, Portugal, 1987, Butterworths, London, pp. 708 - 715. 11 U. G. Knight, Expert systems in power system planning and operation from the viewpoint of a utility engineer, Proc. 9th Power System Computation Conf. (PSCC), Cascais, Portugal, 1987, Butterworths, London, pp. 687 - 694. 12 J. J. Jansen and H. B. Puttgen, ASDEP: an expert system for electric power plant design, IEEE Expert, 2 (1) (1987) 56 - 66. 13 M. Stalder and A. J. Germond, An expert system in PROLOG for alarm handling in a substation, Proc. 9th Power System Computation Conf. (PSCC), Cascais, Portugal, 1987, Butterworths, London, pp. 973 - 978. 14 C. C. Liu, K. Tomsovic and S. Zhang, Efficiency of expert systems an one-line operating aids, Proc. 9th Power System Computation Conf., (PSCC), Cascais, Portugal, 1987, Butterworths, London, pp. 695 - 701. 15 P. K. Kalra, Feasibility study for development of expert systems for power system control, Electr. Power Syst. Res., 12 (1987) 125 - 130. 16 L. Brownston, R. Farell, E. Kant and N. Martin, Programming Expert Systems in OPS5, AddisonWesley, London, 1985. 17 B. F. Wollenberg and A. J. Wood, Power Generation, Operation and Control, Wiley, New York, 1984.