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A Model- and Rule-Based Intelligent Alarm Processing System
A MODEL- AND RULE-BASED INTELLIGENT ALARM PROCESSING SYSTEM
Cben Jingcbeng Wang Pingyang Fu Sbuti Wang Mingjun Yu Erkeng
Electric Power Research Institute, Qinghe, Beijing, China Cben Kaiyong Zeng Zbaoqi Hu Xiang
East China Power System Control Center, East Nanjing Road, Shanghai, China
Abstract: This paper presents an intelligent alarm processing system based respectively on network configuration model and Petri nets model. By analyzing breaker tripping messages, this alarm processing system can identify fault component or possible fault area. and by considering both relay actuating and breaker tripping messages, it can identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay. This alarm processing system will be implemented on East China Power System to serve as an assistant tool for the operators to take correct and swift actions during emergency conditions. Copyright © 1998 IFAC Keywords: Expert systems, Alarm systems, Reasoning, Petri-nets
alarms. The essential problem he finds is that certain events in the power system can give rise to multiple alarms, repeated alarms or ambiguous alarms and the system operator can experience information overload when a very large set of alarms presents in front of him. It has been pointed out that the expert system (ES) technology could be used to filter the alarms and interpret these, thereby providing the system operator with a more accurate picture and likely events occurring out in the system. .
1. INTRODUCTION The problem associated with alarms in an energy management system (EMS) is well known. In an emergency condition, an existing EMS tends to generate numerous alarms as a result of discrete and analogue data received by the SCADA computer. Among these alarms, some may be false; some may be missing; and some may be duplicate. In such a confusing situation, the operators need to understand the alarms, determine what events lead to these alarms, and take appropriate actions to restore service to the interrupted customers. Additionally, when a fault occurs, protection relays and circuit breakers should operate properly to isolate the fault portion of the network quickly, but relays and breakers may be malfunctioning, which makes the problem even more complicated.
Several different approaches to developing the expert system have been taken by different groups, ranging from ones using purely rule-based systems to others which consider both model-based and rule-based systems. However, no matter what approaches have been adopted, some essential problems for developing an efficient and powerful alarm processing system still exists, including the need to build an appropriate interface between the EMS and the ES, the difficulty to search through an extremely large space and sometimes incapability to find out
In the area of alarm processing the essential problem is one of trying to assist the control center operator by giving him a higher-level interpretation of the 497
For further explaining this approach, we can specify a network by the following set:
the solution from a set of incomplete information, etc. In this paper an intelligent alarm processing system based respectively on network configuration model and Petri nets model is introduced. An objectoriented database has also been developed to build an appropriate interface between the EMS and the ES. As for an energy management system without the ability to pick up relay actuating messages, this alarm processing system will based its reasoning procedures on network configuration model and identify the fault component or possible fault area by analyzing breaker tripping messages. Otherwise, it will base its reasoning procedures on Petri nets model and identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay, by considering both relay actuating and breaker tripping messages. Additionally, it uses logic rules to filter out false and duplicate alarms and takes classifying and hierarchical reasoning strategy to control the solution direction and procedure, which reduces the problem solution space and improves the efficiency and speed of the whole inference procedure. This alarm processing system will be used as an assistant tool for the operators to take correct and swift actions during an emergency condition.
NT={«cbj, s), cj, cj)lcbjeCB, se{O, I}, cjeC, cjeC, hej}
(1)
where CB is the set of all circuit breakers, C is the set of all electric components; and s is the state of a circuit breaker(O-open, I-closed). . Then we can determine the fault component or area by the following steps: Step 1: Choose all electric components associated with the tripped breakers as the fault candidates. Ne={cjl«cbj, 0), Cjo cj)eNTv «cbj, 0), Cj, cj)eNT}
(2)
Step 2: Choose the candidate whose boundary breakers has all been open as the true fault.
Step 3: If we cannot determine the fault by formula (3), choose the set of integrated candidates which are isolated by the tripped breakers as the fault area. Nt={ZJ'icje Zj, cjeNc"'cje Zj',\CkEZjI,\Cj:;tC/, «(cbm, 1), cj, cj)eNTv«cbm, 1), cj, cj)eNT)A «(cbn, 0), Cj, cJeNTv«cbn, 0), Cb cj)eNT)} (4)
2. NETWORK CONFIGURATION MODEL BASED REASONING PROCEDURE
Step 4: If we still cannot determine the fault by formula (4), first expand the candidate fault set.
2.1 Description o/the reasoning model
As for an EMS which has no relay actuating messages, the alarm processing system will base its reasoning procedure on network configuration model and breaker tripping messages. As the information is incomplete, the results might be rough. In a complicated situation such as a circuit breaker malfunctioning, it can only identify the possible fault area rather than the determined fault component. Additionally, it is apparent that this approach could not diagnose relay malfunctioning.
N'.= Neu{ CjICjENc"'cjeNc"'«(cbko 1), cj, cj) eNTv«cbko 1), cj, c)eNT)} (5) By replace of Ne with N'e in forotula (4) to further check the fault candidates. Step 5: If the fault still cannot be found by above steps, there may exist two possible cases: no fault has occurred in the power system, so the breaker tripping messages are false alarms, or a fault has occurred, but a breaker tripping alarm has been missing. Strictly speaking, we can not certainly distinguish between these two cases, but in a general sense, we can use certainty factor method to diagnose the most possible fault component. That is, as for an electric component which has a number of boundary breakers, if most of its breakers are tripped, the certainty factor for this component having a fault is maximum.
The basic idea of this reasoning approach is that any one of electric components in a high-voltage network has its own boundary circuit breakers and independent protection relays. Whenever an electric component has a fault, its main protection relay should operate correctly to trip all its boundary breakers and make it isolated. Thus, the minimum set of electric components isolated by the tripped breakers is the fault area. If the item number in the set equals one, a single fault has occurred and been isolated correctly. Otherwise, if the item number in the set is greater than one, multiple faults may have occurred or a circuit breaker may has been malfunctioning.
2.2 An example
To explain the reasoning procedure of this model, we give a simple example here. As for the network in 498
Figure 1, suppose the initial state of each breaker is closed and no candidate in the system. The alarm messages provided by an EMS is shown in Table 1. By the reasoning method mentioned above, the expert system will carry out its reasoning procedure on following steps:
Table 1 Alarm messages ~rovided b~ an EMS
Step 1: The first alarm means that the breaker 5021 in station RENZH has tripped. The system will fmd out this station object in the network configuratioon database, set curren station pointer to this object and change its breaker 5021 to open.
Time
Station
Device
Point
State
10:12:00 10:12:00 10:12:01 10:12:01
RENZH RENZH JIAND nAND
5021 5022 5022 5023
BREAKER BREAKER BREAKER BREAKER
OPEN OPEN OPEN OPEN
3. PETRI NETS MODEL BASED REASONING PROCEDURE
Step 2: The system sets the associated components with breaker 5021, i.e. line RJ_5021 and bus 1, as candidates and insert them into the candidate list. By now the processing of the first alarm has completed.
3.1 Briefreview ofPetri nels Petri nets were invented by Carl Petri in 1962 and have developed over the years as a major model for the study· of asynchronous systems. Petri nets are extension of directed graphs with two type of nodes. A class of nets known as place-transition net are shown in Figure 2. The places are presented by circles, while the transitions by bars or boxes. The interconnection rules are such that nodes of one type are connected directly only to nodes of other type. An arc from a place to a transition is called an input arc and one from a transition to a place is called an output arc.
Step 3: The second alarm is still a breaker tripping message from station RENZH. Similarly, the system will set its breaker 5022 to open and add a new component, transformer TRA 1, in the candidate list. Step 4: The third alarm is from another station JIANO, so the system first analyzes the candidates in the candidate list and find no component isolated by the tripped breakers. Then, as the same way in Step 1 and Step 2, the system will set current station pointer to station JIAND, change its breaker 5022 to open, and add its component TRA 1 as a candidate. Step 5: On receiving the fouth alarm, the system will change the breaker 5023 of current station JIAND to open and add its component BUS 2 as a candidate. Step 6: As there is no other alarms (or alarms are coming from another station), the system will analyze each candidate and fmd that line RJ_5201 has been isolated. So the system will choose this component as a true fault and insert it into a fault list. The candidate list will be set to empty.
Fig.2. Graphical representation of Petri nets In a Petri net a place can hold any number of tokens shown as dots. The tokens can be viewed as holding some condition represented by the place. Collectively the distribution of tokens among the places is known as the marking (state) of the net. If all the input places to a transition each contain at least one token, the transition is said to be enabled and an enabled transition may fIre. The frring of an enabled transition moves one token from each of its input places and adds one token to each of its output places. For example in Figure 2, the initial marking is that places p) and P2 each have one token. Here only transition Ij is enabled. The frring of I) removes the token in p) and puts a new token in P2 and P4 respectively.
In a special case, e.g. the third alarm is not received, the system will still choose line RJ_ 5201 as a possible fault on certainty factor method, but it will point out that the breaker 5022 in JIAND station may have tripped and the alarm may have been missing.
RJ_5201 BUS 1
5021
A formal defmition of a Petri net is given as follows: BUS 1
A Petri net is a 4-tuple, PN = (P, T, I, 0) where P={P),P2' ... ,p,} is a non-empty set of places, T={I),12 , ••• , tm lis a non-empty set of transition, I: PxT~N is the input function,
TRAl
HAND Fig.I. An example network connection
499
0: PxT--+N is the output function.
3.3 Inference procedure based on Petri net model
The marking of a Petri net is described by the mapping from the set of places P to the set of nonnegative integers N, ~: P--+N. It usually represented by a vector MeN", the ith entry M; = ~(PJ of which is the number of tokens in place Pi.
To fmd a fault by a Petri net, what is required is to detennine the initial marking of the Petri net which led to the fmal state we observed. One method is to defme a reverse Petri net. A reserve Petri net model (RPNM) is a model of a Petri net with the arcs reversed, as shown in Figure 5.
3.2 Petri net model ofpower systems
Assume the initial marking of a RPNM is relays actuating and breakers tripping messages. Through the transition firing of the net, the tokens finally arrive at the places which represent the network components. If there are one or more tokens in these places, the components corresponding to these places are called as fault candidates. The candidate with more tokens has the higher possibility of being a fault. The solution procedure of this approach is described as follows.
When a fault occurs in a power system, it likely results in a series of cascading events such as relays actuating, breaker opening or closing. The occurrence of these events depends on the state of various components in the power systems. So if we use the places in the Petri nets to represent the various components of the network such as buses, relays or breakers, and the token in a place to indicate the state of the components, we can thereby explain the behavior of the power system by Petri nets. Here: (I) The token in a reray place indicates that the relay has actuated. (2) The token in a breaker place indicate that the breaker has tripped. (3) The token in a component indicate that 'the component has a fault.
Fig.5 The reversed Petri net model ofFig.4. Assume P' is a set of places which represent the network components, R is the reachability set, ~ (p 'i) is the number of tokens in place P'i .
As for the network in Figure 3, the Petri net model .for the protection scheme for Bus El is shown in Figure 3. If a fault takes place in El, the bus protective relay Elm will actuate to trip the breaker CEl and CB3 . If Elm fails to trip CEl (or CB3), the backup relay LIAb (or L2Ab) of line L2 (or L2) will actuate to trip the breaker CBI (or CB4). In this model we have to specify some firing rules on some special transitions, e.g., the transition t3 (or t5) will fire if t2 has frred, CB2 (or CB3) has not tripped and there is no token in B2m.
B~I CBI
I
LIAml
LIAb
LI
If there is a tenninal node in the reachability tree M E R and ~(p 'i) > 0 in M, then the component corresponding to p 'i is a fault candidate. If there exists more than one places p ' I' P 'z, ... , p'j E P' and in R, ~(P'I) >~(P 'z) > ... ~(p';) >0. then the fault certainty factor of the related component have the relation CF(p \) >CF(p'z} > ...CF(p'J >0.
3.4 An example
L2
1---0
L2Cm
As for the network in Figure 3, suppose relays Elm, LIAb actuated and breakers CBI, CB3 have tripped,
L2Cb
the initial marking is
....--..., I I L..-_~~""';";;...;;...J
B2 ~o =
Fig.3. Protection scheme of a network
(0
B2m CBI
I
I
CB2
CB3
CB4
0
1
0
LIAb L2Cb
I
0)
We now use the reversed model shown in Figure 5 to simulate the dynamic process of the network. t4 frres once: ~I = (0 I 0 0 I 02 t3 frres twice: ~z= (0 3 0 2 1 0 0 tz fires once: 113 = (0 4 0 0 0 0 tl frres four times: 114 = (4 0 0 1 0 0
Fig.4. Petri net model of protection scheme for El
500
0) 0) 0) 0 0)
As there is no enabled transition in the marking ~4' so ~4 is the terminal node. In this sectional model, only mEP' and B2 has four tokens, and so B2 is the fault candidate. By the fuing sequences of transitions, we can also frod the malfunctioning re"lays or breakers. For example, as the transition t3 has fired, cm is a malfunctioning breaker.
REFERENCE A.H.Shoop and S.Silverman (1992), A real-time alarm processor, Electrical Power and Energy Systems, Vol. 14, No. 2/3, ppl08-113. C.C.Liu and T.Dillon (1992), State-of-the-art of expert system applications to power systems, Electrical Power and Energy Systems, Vol. 14, No. 2/3, pp86-96. C.Fukui and J.Kawakami (1986), An expert system for fault section estimation using information from protective relays and circuit breakers, IEEE Trans. on Power Delivery, pp83-87. C.Yang, H.Okamoto, A.Yokoyama and Y.Sekine (1991), Expert system for fault section estimation of power system using time sequence information, Proc. Third Symposium on Expert Systems Application to Power Systems, pp587591. D.B.Tesch, D.C.Yu, L.M.Fu and Vairavan (1989), A knowledge-based alarm processor for energy management system, IEEE/PES Summer Meeting Paper No.98 SM671-9 PWRS. D.J.Young, K.L.Lo, J.R.McDonald, R.Howard and J.Rye (1992), Development of a practical expert system for alarm processing, Proc. lEE, Vol. 139, No. 5, pp437-447. H.E.Dijk (1992), AI-based techniques for alarm handling, Electrical Power and Energy Systems, Vol. 14, No. 2/3, pp131-137. K.Komai, T.Sakaguchi, S.Takeda (1986), Power system fault diagnosis with an expert system enhanced by the general problem solving method, Proc. lASTED High Technology in the Power Industry. M.Wagenbauer and H.Bruggen (1991), Model and rule based intelligent alarm processing, Proc. Third Symposium on Expert Systems Application to Power Systems, pp27-32.
4. SYSTEM DESIGN AND IMPLEMENTA nON The proposed approaches mentioned above have been implemented on a personal computer. An object-oriented database has also been designed to build an appropriate interface. Besides, For reducing the problem solution space and improving the efficiency and speed of the whole inference procedure, a station-oriented inference control strategy is also adopted in this system,. The main control rule is:
IF THEN AND ELSE AND
the alarm is for current station object update state of current station object by the content of this alarm modify fault candidate list by the updated state of current station object calculate certainty factor of fault candidates modify the pointer of current station object
5. CONCLUSIONS An intelligent alarm processing system based respectively on network configuration model and Petri nets model has been developed. By analyzing breaker tripping messages, this alarm processing system can identify fault component or possible fault area, and by considering both relay actuating and breaker tripping messages, it can identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay. This intelligent alarm system will be implemented on East China Power System in 1997 or 1998.
501
Cben Jingcbeng Wang Pingyang Fu Sbuti Wang Mingjun Yu Erkeng
Electric Power Research Institute, Qinghe, Beijing, China Cben Kaiyong Zeng Zbaoqi Hu Xiang
East China Power System Control Center, East Nanjing Road, Shanghai, China
Abstract: This paper presents an intelligent alarm processing system based respectively on network configuration model and Petri nets model. By analyzing breaker tripping messages, this alarm processing system can identify fault component or possible fault area. and by considering both relay actuating and breaker tripping messages, it can identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay. This alarm processing system will be implemented on East China Power System to serve as an assistant tool for the operators to take correct and swift actions during emergency conditions. Copyright © 1998 IFAC Keywords: Expert systems, Alarm systems, Reasoning, Petri-nets
alarms. The essential problem he finds is that certain events in the power system can give rise to multiple alarms, repeated alarms or ambiguous alarms and the system operator can experience information overload when a very large set of alarms presents in front of him. It has been pointed out that the expert system (ES) technology could be used to filter the alarms and interpret these, thereby providing the system operator with a more accurate picture and likely events occurring out in the system. .
1. INTRODUCTION The problem associated with alarms in an energy management system (EMS) is well known. In an emergency condition, an existing EMS tends to generate numerous alarms as a result of discrete and analogue data received by the SCADA computer. Among these alarms, some may be false; some may be missing; and some may be duplicate. In such a confusing situation, the operators need to understand the alarms, determine what events lead to these alarms, and take appropriate actions to restore service to the interrupted customers. Additionally, when a fault occurs, protection relays and circuit breakers should operate properly to isolate the fault portion of the network quickly, but relays and breakers may be malfunctioning, which makes the problem even more complicated.
Several different approaches to developing the expert system have been taken by different groups, ranging from ones using purely rule-based systems to others which consider both model-based and rule-based systems. However, no matter what approaches have been adopted, some essential problems for developing an efficient and powerful alarm processing system still exists, including the need to build an appropriate interface between the EMS and the ES, the difficulty to search through an extremely large space and sometimes incapability to find out
In the area of alarm processing the essential problem is one of trying to assist the control center operator by giving him a higher-level interpretation of the 497
For further explaining this approach, we can specify a network by the following set:
the solution from a set of incomplete information, etc. In this paper an intelligent alarm processing system based respectively on network configuration model and Petri nets model is introduced. An objectoriented database has also been developed to build an appropriate interface between the EMS and the ES. As for an energy management system without the ability to pick up relay actuating messages, this alarm processing system will based its reasoning procedures on network configuration model and identify the fault component or possible fault area by analyzing breaker tripping messages. Otherwise, it will base its reasoning procedures on Petri nets model and identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay, by considering both relay actuating and breaker tripping messages. Additionally, it uses logic rules to filter out false and duplicate alarms and takes classifying and hierarchical reasoning strategy to control the solution direction and procedure, which reduces the problem solution space and improves the efficiency and speed of the whole inference procedure. This alarm processing system will be used as an assistant tool for the operators to take correct and swift actions during an emergency condition.
NT={«cbj, s), cj, cj)lcbjeCB, se{O, I}, cjeC, cjeC, hej}
(1)
where CB is the set of all circuit breakers, C is the set of all electric components; and s is the state of a circuit breaker(O-open, I-closed). . Then we can determine the fault component or area by the following steps: Step 1: Choose all electric components associated with the tripped breakers as the fault candidates. Ne={cjl«cbj, 0), Cjo cj)eNTv «cbj, 0), Cj, cj)eNT}
(2)
Step 2: Choose the candidate whose boundary breakers has all been open as the true fault.
Step 3: If we cannot determine the fault by formula (3), choose the set of integrated candidates which are isolated by the tripped breakers as the fault area. Nt={ZJ'icje Zj, cjeNc"'cje Zj',\CkEZjI,\Cj:;tC/, «(cbm, 1), cj, cj)eNTv«cbm, 1), cj, cj)eNT)A «(cbn, 0), Cj, cJeNTv«cbn, 0), Cb cj)eNT)} (4)
2. NETWORK CONFIGURATION MODEL BASED REASONING PROCEDURE
Step 4: If we still cannot determine the fault by formula (4), first expand the candidate fault set.
2.1 Description o/the reasoning model
As for an EMS which has no relay actuating messages, the alarm processing system will base its reasoning procedure on network configuration model and breaker tripping messages. As the information is incomplete, the results might be rough. In a complicated situation such as a circuit breaker malfunctioning, it can only identify the possible fault area rather than the determined fault component. Additionally, it is apparent that this approach could not diagnose relay malfunctioning.
N'.= Neu{ CjICjENc"'cjeNc"'«(cbko 1), cj, cj) eNTv«cbko 1), cj, c)eNT)} (5) By replace of Ne with N'e in forotula (4) to further check the fault candidates. Step 5: If the fault still cannot be found by above steps, there may exist two possible cases: no fault has occurred in the power system, so the breaker tripping messages are false alarms, or a fault has occurred, but a breaker tripping alarm has been missing. Strictly speaking, we can not certainly distinguish between these two cases, but in a general sense, we can use certainty factor method to diagnose the most possible fault component. That is, as for an electric component which has a number of boundary breakers, if most of its breakers are tripped, the certainty factor for this component having a fault is maximum.
The basic idea of this reasoning approach is that any one of electric components in a high-voltage network has its own boundary circuit breakers and independent protection relays. Whenever an electric component has a fault, its main protection relay should operate correctly to trip all its boundary breakers and make it isolated. Thus, the minimum set of electric components isolated by the tripped breakers is the fault area. If the item number in the set equals one, a single fault has occurred and been isolated correctly. Otherwise, if the item number in the set is greater than one, multiple faults may have occurred or a circuit breaker may has been malfunctioning.
2.2 An example
To explain the reasoning procedure of this model, we give a simple example here. As for the network in 498
Figure 1, suppose the initial state of each breaker is closed and no candidate in the system. The alarm messages provided by an EMS is shown in Table 1. By the reasoning method mentioned above, the expert system will carry out its reasoning procedure on following steps:
Table 1 Alarm messages ~rovided b~ an EMS
Step 1: The first alarm means that the breaker 5021 in station RENZH has tripped. The system will fmd out this station object in the network configuratioon database, set curren station pointer to this object and change its breaker 5021 to open.
Time
Station
Device
Point
State
10:12:00 10:12:00 10:12:01 10:12:01
RENZH RENZH JIAND nAND
5021 5022 5022 5023
BREAKER BREAKER BREAKER BREAKER
OPEN OPEN OPEN OPEN
3. PETRI NETS MODEL BASED REASONING PROCEDURE
Step 2: The system sets the associated components with breaker 5021, i.e. line RJ_5021 and bus 1, as candidates and insert them into the candidate list. By now the processing of the first alarm has completed.
3.1 Briefreview ofPetri nels Petri nets were invented by Carl Petri in 1962 and have developed over the years as a major model for the study· of asynchronous systems. Petri nets are extension of directed graphs with two type of nodes. A class of nets known as place-transition net are shown in Figure 2. The places are presented by circles, while the transitions by bars or boxes. The interconnection rules are such that nodes of one type are connected directly only to nodes of other type. An arc from a place to a transition is called an input arc and one from a transition to a place is called an output arc.
Step 3: The second alarm is still a breaker tripping message from station RENZH. Similarly, the system will set its breaker 5022 to open and add a new component, transformer TRA 1, in the candidate list. Step 4: The third alarm is from another station JIANO, so the system first analyzes the candidates in the candidate list and find no component isolated by the tripped breakers. Then, as the same way in Step 1 and Step 2, the system will set current station pointer to station JIAND, change its breaker 5022 to open, and add its component TRA 1 as a candidate. Step 5: On receiving the fouth alarm, the system will change the breaker 5023 of current station JIAND to open and add its component BUS 2 as a candidate. Step 6: As there is no other alarms (or alarms are coming from another station), the system will analyze each candidate and fmd that line RJ_5201 has been isolated. So the system will choose this component as a true fault and insert it into a fault list. The candidate list will be set to empty.
Fig.2. Graphical representation of Petri nets In a Petri net a place can hold any number of tokens shown as dots. The tokens can be viewed as holding some condition represented by the place. Collectively the distribution of tokens among the places is known as the marking (state) of the net. If all the input places to a transition each contain at least one token, the transition is said to be enabled and an enabled transition may fIre. The frring of an enabled transition moves one token from each of its input places and adds one token to each of its output places. For example in Figure 2, the initial marking is that places p) and P2 each have one token. Here only transition Ij is enabled. The frring of I) removes the token in p) and puts a new token in P2 and P4 respectively.
In a special case, e.g. the third alarm is not received, the system will still choose line RJ_ 5201 as a possible fault on certainty factor method, but it will point out that the breaker 5022 in JIAND station may have tripped and the alarm may have been missing.
RJ_5201 BUS 1
5021
A formal defmition of a Petri net is given as follows: BUS 1
A Petri net is a 4-tuple, PN = (P, T, I, 0) where P={P),P2' ... ,p,} is a non-empty set of places, T={I),12 , ••• , tm lis a non-empty set of transition, I: PxT~N is the input function,
TRAl
HAND Fig.I. An example network connection
499
0: PxT--+N is the output function.
3.3 Inference procedure based on Petri net model
The marking of a Petri net is described by the mapping from the set of places P to the set of nonnegative integers N, ~: P--+N. It usually represented by a vector MeN", the ith entry M; = ~(PJ of which is the number of tokens in place Pi.
To fmd a fault by a Petri net, what is required is to detennine the initial marking of the Petri net which led to the fmal state we observed. One method is to defme a reverse Petri net. A reserve Petri net model (RPNM) is a model of a Petri net with the arcs reversed, as shown in Figure 5.
3.2 Petri net model ofpower systems
Assume the initial marking of a RPNM is relays actuating and breakers tripping messages. Through the transition firing of the net, the tokens finally arrive at the places which represent the network components. If there are one or more tokens in these places, the components corresponding to these places are called as fault candidates. The candidate with more tokens has the higher possibility of being a fault. The solution procedure of this approach is described as follows.
When a fault occurs in a power system, it likely results in a series of cascading events such as relays actuating, breaker opening or closing. The occurrence of these events depends on the state of various components in the power systems. So if we use the places in the Petri nets to represent the various components of the network such as buses, relays or breakers, and the token in a place to indicate the state of the components, we can thereby explain the behavior of the power system by Petri nets. Here: (I) The token in a reray place indicates that the relay has actuated. (2) The token in a breaker place indicate that the breaker has tripped. (3) The token in a component indicate that 'the component has a fault.
Fig.5 The reversed Petri net model ofFig.4. Assume P' is a set of places which represent the network components, R is the reachability set, ~ (p 'i) is the number of tokens in place P'i .
As for the network in Figure 3, the Petri net model .for the protection scheme for Bus El is shown in Figure 3. If a fault takes place in El, the bus protective relay Elm will actuate to trip the breaker CEl and CB3 . If Elm fails to trip CEl (or CB3), the backup relay LIAb (or L2Ab) of line L2 (or L2) will actuate to trip the breaker CBI (or CB4). In this model we have to specify some firing rules on some special transitions, e.g., the transition t3 (or t5) will fire if t2 has frred, CB2 (or CB3) has not tripped and there is no token in B2m.
B~I CBI
I
LIAml
LIAb
LI
If there is a tenninal node in the reachability tree M E R and ~(p 'i) > 0 in M, then the component corresponding to p 'i is a fault candidate. If there exists more than one places p ' I' P 'z, ... , p'j E P' and in R, ~(P'I) >~(P 'z) > ... ~(p';) >0. then the fault certainty factor of the related component have the relation CF(p \) >CF(p'z} > ...CF(p'J >0.
3.4 An example
L2
1---0
L2Cm
As for the network in Figure 3, suppose relays Elm, LIAb actuated and breakers CBI, CB3 have tripped,
L2Cb
the initial marking is
....--..., I I L..-_~~""';";;...;;...J
B2 ~o =
Fig.3. Protection scheme of a network
(0
B2m CBI
I
I
CB2
CB3
CB4
0
1
0
LIAb L2Cb
I
0)
We now use the reversed model shown in Figure 5 to simulate the dynamic process of the network. t4 frres once: ~I = (0 I 0 0 I 02 t3 frres twice: ~z= (0 3 0 2 1 0 0 tz fires once: 113 = (0 4 0 0 0 0 tl frres four times: 114 = (4 0 0 1 0 0
Fig.4. Petri net model of protection scheme for El
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0) 0) 0) 0 0)
As there is no enabled transition in the marking ~4' so ~4 is the terminal node. In this sectional model, only mEP' and B2 has four tokens, and so B2 is the fault candidate. By the fuing sequences of transitions, we can also frod the malfunctioning re"lays or breakers. For example, as the transition t3 has fired, cm is a malfunctioning breaker.
REFERENCE A.H.Shoop and S.Silverman (1992), A real-time alarm processor, Electrical Power and Energy Systems, Vol. 14, No. 2/3, ppl08-113. C.C.Liu and T.Dillon (1992), State-of-the-art of expert system applications to power systems, Electrical Power and Energy Systems, Vol. 14, No. 2/3, pp86-96. C.Fukui and J.Kawakami (1986), An expert system for fault section estimation using information from protective relays and circuit breakers, IEEE Trans. on Power Delivery, pp83-87. C.Yang, H.Okamoto, A.Yokoyama and Y.Sekine (1991), Expert system for fault section estimation of power system using time sequence information, Proc. Third Symposium on Expert Systems Application to Power Systems, pp587591. D.B.Tesch, D.C.Yu, L.M.Fu and Vairavan (1989), A knowledge-based alarm processor for energy management system, IEEE/PES Summer Meeting Paper No.98 SM671-9 PWRS. D.J.Young, K.L.Lo, J.R.McDonald, R.Howard and J.Rye (1992), Development of a practical expert system for alarm processing, Proc. lEE, Vol. 139, No. 5, pp437-447. H.E.Dijk (1992), AI-based techniques for alarm handling, Electrical Power and Energy Systems, Vol. 14, No. 2/3, pp131-137. K.Komai, T.Sakaguchi, S.Takeda (1986), Power system fault diagnosis with an expert system enhanced by the general problem solving method, Proc. lASTED High Technology in the Power Industry. M.Wagenbauer and H.Bruggen (1991), Model and rule based intelligent alarm processing, Proc. Third Symposium on Expert Systems Application to Power Systems, pp27-32.
4. SYSTEM DESIGN AND IMPLEMENTA nON The proposed approaches mentioned above have been implemented on a personal computer. An object-oriented database has also been designed to build an appropriate interface. Besides, For reducing the problem solution space and improving the efficiency and speed of the whole inference procedure, a station-oriented inference control strategy is also adopted in this system,. The main control rule is:
IF THEN AND ELSE AND
the alarm is for current station object update state of current station object by the content of this alarm modify fault candidate list by the updated state of current station object calculate certainty factor of fault candidates modify the pointer of current station object
5. CONCLUSIONS An intelligent alarm processing system based respectively on network configuration model and Petri nets model has been developed. By analyzing breaker tripping messages, this alarm processing system can identify fault component or possible fault area, and by considering both relay actuating and breaker tripping messages, it can identify the determined fault component, and if needed, the malfunctioning breaker or malfunctioning relay. This intelligent alarm system will be implemented on East China Power System in 1997 or 1998.
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