An abductive fuzzy knowledge based system for fault diagnosis in a power system. Min Y Park and Martin Lefley. Bruce Ramsay. Ian Moyes. Bournemouth ...
An abductive fuzzy knowledge based system for fault diagnosis in a power system Min Y Park and Martin Lefley Design, Engineering, & Computing, Bournemouth University, UK
Ian Moyes Scottish and Southem Energy PLC Pitlochry, UK
Bruce Ramsay APEME, University of Dundee Dundee, UK
Abstract This paper presents the design and evaluation of a novel, AI (Artificial Intelligence) based alarm processing and fault diagnosis system, for a 132kv/12 bus-16line sample power system. The work has been conducted in conjunction with Scottish Hydro Electric PLC. The fault diagnosis system is based on a hybrid object-oriented AI technique. The method developed utilises abductive inference. This technique is demonstrated to realise some improvements when compared with fuzzy logic and takes into account the current practical limitations in the design. The method is based on processing SCADA (Supervisory Control and Data Acquisition) messages, extending the arrangement of the knowledge acquisition process and applicability of circuit breakers and relays. The potential benefits and implications of adopting such an abductive fuuy knowledge based system are demonstrated in this research, and include a user friendly inference engine, adaptability, and KBS update.
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The amount of information reported by the faults increases iteratively or irregularly, and in certain situation operators in the control centre are not able to analyse the problems satisfactorily. Some recent progress has presented intelligent diagnostic tools. These systems imply that if an outage occurs, the substation may behave abnormally. The discrepancy between the intended and the actual configuration is used as a clue to determine what events have occurred in their KBS process, as follows: 9 What events may have caused the substation to behave the way it does? What are logical implications of the current power system?
Solutions to these questions can be acquired from system and field engineers who develop operating guidelines. However, this knowledge is not available on a real-time basis to operators, as these engineers for the operating
0-7803-6338-8/00/$10.00(~)2000IEEE
Problem statement
The following report illustrates current problem in alarm management and fault diagnosis [2]: “Zn the event of the occurrence of a widespread power system failure it is essential that Hydro-Electric should have an adequate, regularly updated and effectively deployed system restoration procedure, this would be academic to attempt to deal with events of this nature.” First, we must determine whether certain types of AI technique are adapted with maximum effectiveness for an EMS (Energy Management System). This is summarised the following table 1 [3]:
Introduction
In power system protection, a back-up relay is operated when the main protection from the feeder fails to clear a fault. Such events cause alarms and could include the following outages [ 11: 9 Faults on transmission lines, transformers, bus sections, and series equipment 9 Failure of substation circuit breakers to operate Thermal overloads on protective equipment
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guidelines do not generally staff in control centres. Thus, the abductive fuzzy knowledge based system described in this paper attempts a step in that direction and helps operator to answer these questions.
Automated Knowledge Acauisition uncertainty Coping With
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High-level reasoning Low-level reasonine Real-time ooeration
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Table 1 Comparison with Expert Systems, Neural Networks, Genetic Algorithms, and Fuzzy Logic These advantages and drawbacks are hard to quantify and they are subjective by nature, but a common insight falls into hybrid systems that should provide better performance, in order to correspond with multiple intelligent manners Expert system based reasoning with particular SCADA (Supervisory Control and Data Acquisition messages), that are deduced from rules referring to the domain experts’ heuristic knowledge have proved to be quite efficient. However, improved expert system performance is desirable, particularly where the degree of uncertainty is unclear or large [4]. In this respect, the use of fuzzy logic developed by Zadeh (1965),[5], has attempted to improve tolerance for
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imprecision and uncertainty in many industrial applications. Thus, employing a --expert system attempts to improve how effectively the fault location can be found and interpreted in an optimum manner. This paper concludes by evaluating the complete sy'stem, evaluating the advantages from this hybrid solution to power system fault diagnosis and alarm processing.
The proposed system transmission Lines
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Figure 1 illustrates how the developed system corresponds to the partial transmission networks on the 132 kV transmission lines when the system connects to the SCADA. This sample feeder system shows a simplified substation networks in HE for the purpose of effective experiment. m-
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Non-urgent: faulted feeders which have standing nonurgent alarms such as battery earth fault and telemetry equip defective Minor : faulted feeders which have minor alarms such as auto reclose in progress and circuit breaker failure Unknown: faulted feeders which have no knowledge capture in KBS or uncertain knowledge acquisition A hybrid system, case-based and model-based reasoning, was employed to implement these proposals, as follows: {If ((Not (Local: Expert-rulel, 2,3 ...n #= Urgent) Or Not (Local: Expert-del, 2,3 .... n #= Non-urgent) Or Not (Local: Expert-del, 2,3 .... n #= Minor ) ) Or Null?( Loca1:Expert-rule 1,2,3,...n ) ) Then Setvalue( Pattem:Alarm-Summary, ? );
SetValue(Local:Expert-rule1,2,3,. ...n Pattem:Alarm-Summary );}; This algorithm was tested on a symbolic process which consists of these four categories as aforementioned. The test results appear to be very affective for operators who can determine more effective alarm management.
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Casestudy
A case study was carried out in conjunction with Scottish Hydro Electric PLC, which consists of four samples of the SCADA data, shows in table 2. These were collected to evaluate the following three requirements:
Figure 1 Simplified substation networks in Scottish Hydro Electric PLC Interviews with operators have revealed that expert systems are built based on the knowledge acquired from domain experts. Classical Boolean logic may not be efficient for the representation of such expertise. On the contrary, fuzzy logic is a natural choice which serves them more naturally. Testing the proposed system is based on the application development tool, Kappa-PC and falls into the following requirements: A user friendly inference engine for General Set Covers; Adaptability; KBS update; The results of testing requirements are discussed in section 3 Knowledge acquisition interface for uncertain alarms
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Discussions with operators have revealed that the most useful category of higher level data would be the circuit alarm status. An effective algorithm is considered, as follows [6]: Urgent: faulted feeders which have standing protection operation alarms such as Backup protection operated and Busbar protection zone trip
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Hypothesis 1.
A user friendly inference engine for General Set Covers;
Hypothesis 2. Hypothesis 3.
Adaptability to fuzzy set theory; KBS update;
Alarms type 1
Alarms1([14],p,-g,a,14,on) Alarms2([16],zl,-g,a,17,on)
Alarms3(U,cb_f,[l61,-,_, 18,on) Alarms4([ 1, , , , ,19,on) Alarms type 2
AlarmsS([l l],bus,,s,b,33,on) Alarms6(U,cb-f, [30],s,b, 28,on) Alarms7([33],z2,,s,b,3 1,on) Alarms8([ 1, , , , ,29,on)
Alarms type 3
Alarmsl(U,cb-f,[7],s,b,lO,on)
Alarms type 4
Alarms2( [4],z 1,s,b,3,on) Alarms7([2 l],z2,-g,a,24,on) Alarm&([ 1, , , , ,21,on)
These three hypotheses were used to test the performance of the KBS design. The results of the performance will be discussed in the proceeding sections.
3.1
A user friendly inference engine for Generalised Set Covers
An inference engine is the knowledge process which is modelled after the expert’s reasoning. The engine works with available information on a given problem, coupled with the knowledge stored in the knowledge base, in order to draw conclusions and make recommendations. Generalised set covers (GSC) is an extension of the classical set covering model since it can be shown that it finds all minimal covers [7]. Reasoning with the GSC model is known abductive and discussed in the proceeding sections, in conjunction with the user friendly inference engine.
Tasks Abductive reasoning is the process of reasoning from principles to facts under uncertainty using numeric measures. Since the GSC model reasons by explanations, it is classified as an abductive reasoning model. Building a set cover for the observables then performs the inference. The criteria of implementing the user friendly inference engine were taken into account, as follows: Is the GUI-based knowledge acquisition Criterion 1. interface controllable? Criterion2. How much is the displays of choosing the smallest possible solution/parsimony helpful to operators? These criteria were used to evaluate the benefits of employing an abduction based inference engine in objectonentation and several points were attempted to observe the following requirements: The forward chainer of abductive reasoning requires a predefined rule which should process any similar uncertainty from the new SCADA messages 0 Multiple pattern rules should function in the parallel function Does display of irrelevant or poorly processed information lead to confusion?
fault location and the collection of recycling the alarm messages every 4 seconds. These three behavioural functions were utilised to design the abductive inference engine whether they respond to the three requirements. 3.1.3
Results
Each requirement was allocated to the two criteria, and classified as to whether it drew a satisfactory conclusion. The results are discussed, as follows:
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3.1.2
Procedure
The experiment was carried out employing the use of the editor h c t i o n s in the tool, including Knowledge, Math, String, Lists, Logical, File, Control, Classes, Instances, Rules, and Goals, which covered the three expected design requirements as aforementioned. The editor functions provide an object-oriented inference engine which was based on the following observation: SCADA system integration with the inference engine: provides the interface between SCADA messages and the messages interpreted by the KBS, i.e. ‘Object=Slot=Value ’ Process representation: consists of Functions and Methods that develops certain reasoning paradigms, on condition that the alarm messages/objects reach a conclusion through forward chaining and backward chaining Temporal representation: operates a task scheduler to run in parallel utilising timer function, i.e. the use of GIS (Geographical Information System) to pinpoint the
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Figure 2 An empirical sampling of the mean for 11 samples of N=4 in the user friendly system Figure 2 illustrates the performance of the user-friendly inference engine which appears to be quite effective. The criteria 1, and 2 were applied to the four different expertise’ opiniodN4. Individual editor functions were tested against they respond to the requirements of the inference engine design. The significance of implementing the knowledge base process is that of an abductive model. This provides a plausible solution to the problem of concurrent faults while rule based diagnostic systems are inconvenient to perform data abstraction, since the use of production rules may result in one rule per special case. Thus, the abductive model provides a solution intuitive similar to the way a power system operator is likely to solve the problem.
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Adaptability to fuzzy set theory
The plausible substation events/outages from the inference engine have been attempted in conjunction with a fuzzy set theory which consists of five benefits over traditional methods, as follows [8]: 1) It provides alternatives for the many attributes of objectives selected. 2) It resolves conflicting objectives by designing weights appropriate to a selected objective. 3) It provides capability for handling ambiguity expressed in diagnostic process which involves symptoms and causes. 4) It develops process control as a fuzzy relation between information about the condition of the process to be controlled. 5) It improves human reliability models in cases where many people perform multiple tasks. These five benefits are to improve the diagnostic process where protective equipment has physical and operational limits. They are described as hard inequality constrains in
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mathematical formulations. Since power systems are large non-linear systems, simplifying assumptions are necessary in the diagnostic process. This implementation is discussed in the proceeding sections. 3.2.Z Tasks An adaptable fuzzy system is concerned with the rules
governing fuzzy system and their weightings in use. They can be modified according to experience, which can be achieved by an expert. The criteria of designing this scheme were considered, as follows: The discrepancy between the plausible and Criterion 1. the actual configuration is used as a clue. Criterion 2. Equipment malfunctions are caused by many factors Criterion 3. Disturbances are determined by subjective conjectures based on experience These criteria were applied to assess the benefits of adaptive fuzzy system in the developed system. A number of points were attempted to test the following requirements: The inference engine should provide the effectslobserved disorders from the incoming SCADA messages The displays should respond to the knowledge acquisition interface The knowledge base should be minimum use of production rules as bulky production rules causes knowledge base maintenance problem Fuzzy rules should reduce a number of expert rules
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Procedure This experiment was camed out employing the use of the faulted feeder, including lines, buses, and relays. This covered the four expected requirements as described in section 3.2.1. Figure 3 has provided a fuzzy set theory for the faulted feeder operation. This picture incorporates the faulted feeder operations with a fuzzy set that are defined via membership functions, as follows [9]: 3.2.2
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degree to which the elements x belong to the fuzzy sets. In addition, The apex b of the triangle abc in figure 3 proposes the possibility that the components are the fault considered. The base of the triangle such as range [a,c] represents the amount of inexactness and the uncertainty of the heuristic knowledge itself in the generalised ‘abductive system. The faulted feeder was determined using interpolative reasoning, which provides capability for handling ambiguity expressed in the fuzzy set operation. This consists of two phases, the estimated plausible fault location, and fault detectors employing transmission bus impedances and effects-causes. In the combining membership functions, the algebraic sum is used. The new grade of membership of X in Ffaultwas deployed, as fol1ow.s: pFfaultUdX)= ~ F f a u I t ( x ) + ~ R i ( x ) - ~ F f a u l ~ x ) ~ R i ( x* ) . * (4) 1.
This algebraic s u m increases the degree of possibility of a component x. This is because there is one more reason why the component x could be the place of the fault. The details are described in the proceeding section. Results Each requirement was allocated to the three criteria, and tested on the SCADA data 4 that was determined uncertain by the KBS update. The membership of Fhult was applied to the following results: 3.2.3
Relay operation Lines Rank 0.7 Line 9 22 L i n e 10 22 0.5 22 JLine 1 1 10 Plausible s u b s t a t i o n faults o n S C A D A d a t a t y p e 4
Table 3 Heuristic based confidence value on SCADA data type 4 Relay operation 22
ILines !Line 9 ILine 10 JLine 1 1 isp s u b s t a t i o n faults o n S C A D A
IRank 10.9 10.9
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Table 3.1 Observed configuration on SCADA data type 4 .
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Figure 3 the membership function of the faulted feeder Intersection: p h (x)=min ~ ( ~ A ( x ) , ~ B ( .x.)..).. (1) Union: PAUB(XFmax (PA(x), PB(x))..... (2) Complement: j i A ( ~ ) = l - p A (x). ................. (3) These basic principles such as p m (x), ~ L A ~(x), B and p ~ ( x ) are the membership functions or grades. They describe the
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Figure 4 Adaptive fuzzy sets on Line 9 fault location
These results were based on the four requirements which responded to the three criteria. The membership function provides the degree of possibility between L9 and L10, which resolves conflicting objectives. This is supported by Seevers (1991),[10], who has experienced a conflict of transmission bus impedances between normal and abnormal relay operations using fault locators. This suggests that the fault location between L9 and L10 is caused by a Busbar failure at Bus9. Figure 4 demonstrates the possible adaptability applied by the formula (4) between plausible L9 and observed L9 fault location. The developed system responded to the requirement utilising object-orientation.
Figure 5 illustrates the automatic KBS update, when the system was connected to the SCADA. Each data type was tested on four different symbolic processes. The results are shown, as follows: -0 $ 2
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Urgent process
-Non-urgent
SCADA
SCADA
SCADA
SCADA
data type1
data type2
data type3
data type4
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KBS update
3.3
A major goal of designing a KBS update is to determine effective alarm processing [ 111. The developed system was tested on a hybrid KBS, case based and model based reasoning, based on automatic update. This system is discussed in the proceeding sections. 3.3.1 Tasks F S update is concerned with the automatic update with
SCADA connection, and detecting uncertainty inferred by the reasoning technique to the KBS. The KBS should behave where it has not been encountered before. The criteria of designing the KBS update were considered, as follows: Criterion 1. Criterion 2. Criterion 3. Criterion4.
3.3.2
Is the KBS consistent when similar events are processed? Does the inference engine provide observed disorders? Does the heuristic-based certainty factor provide plausible faulted feeder membership from the observed disorders? Does the fuzzy logic serve observed membership of the actual power system changes from the plausible membership?
Procedure
Figure 5 the results of the automatic.updatekhe hybrid KBS The results on this experiment present quite effective when combining the case based reasoning and model based reasoning were camed out. 3.4
To process the rules of urgent alarms To process the rules of non-urgent alarms To process the rules of minor alarms To process the rules of unstreamed alarms
These four requirements were applied to evaluate the KBS update which would respond to the topological fault arrangement design in the tool. For example, the objectoriented fault arrangement used in the system is stored as Classes and Instances, which should respond to the KBS update inference engine. 3.3.3
Hypothesis tests on the abductive fuzzy knowledge based system for fault diagnosis in a power system
The overall AI system on a power system should behave effectively in situations it has not previously encountered. The diagnostic system in condition monitoring should cope with novel combinations of SCADA based vague data that employs intelligent hybrid systems in OOP. Figure 6 illustrates the hypothesis tests on the developed system and they measure the new uncertainty, as follows: Step 1 Step 2 Step 3 Step 4
This task was applied to the symbolic process of the inference engine. This is based on four samples that were collected to carry out the four criteria, as below: a) b) c) d)
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Step 5
Step 6 Step 7 Step 8
The API activates a text retrieval of the event log. The OODB receives and stores the information in the form of class and instance. The information is retrieved by Kappa-PC functions and created by the behaviour of encapsulation. The rules in Kappa-PC knowledge base are expected to find all the events, in which the opening of a circuit breaker might be expected to occur, utilising forward chain, method, and function. The derived events enter the KBS session of the tool, the lists of pre-coded hypothesis are examined as to whether the uncertain information is conclusive. The information moves on into fuzzy set rules to determine an optimum solution if step 5 is not satisfactory. Adaptive defuzzification is attempted whether the hybrid object-oriented method serves the tuned message interpretation. The processor concludes the-input phase to read the next message.
Results
Each requirement was allocated to the four criteria, and classified as to whether it drew a satisfactory conclusion.
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First, three data types such as Data typel, Data type 2, and Data type 3 were pre-coded in the KBS and tested randomly. They provide conclusive interpretation as a satisfactory case based reasoning, i.e. resulted in Step 5. Second, the Data type 4 was inferred using fuzzy set rules as the uncertain messages by the rule-based system produced many plausible explanations. In this respect, operation of fuzzy set rules was attempted to determine an approximate interpretation and concluded in Step 8.
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Figure 6 Hypothesis tests on the abductive fUZZy knowledge based system for fault diagnosis. These 8 steps are tested on four different SCADA data provided by the operators (see section 3) in HE PLC. A
Conclusions
Three criteria were tested on the novel method of abductive fuzzy knowledge based system. This is applied to the optimum interpretation of real-time data fiom the 132kV substations. The performance assessment presented an effective method of dealing’with the uncertainty of fault location in transmission networks. The determination of plausible substation events and membership functions with their degrees of possibility will enhance the information and knowledge available to the operators.
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5 References
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[l] Ramsay B., Hasan K., Ranade S., Ozveren C. S., “An Object-Oriented Expert System for Power System Alarm Processing and Fault Identification”,IEEE, 1994 [2] Ringrose D.F. System operation memorandum No 15”, Scottish Hydro Electric PLC, 1995 [3] EPSRC, “ERCOS (Electricity Research CO funding Scheme) workshop on research opportunities for artificial intelligence in the electricity generation and supply industry”, EA technology, 1997 [4] Kandel A, “Fuzzy Expert Systems” CRC Press, 1991
Figure 7 A sampling test of data type4 using fuzzy set operation
Figure 7 demonstrates an effective fuzzy inference engine which provides the user friendly interface for operators. There were two manifestations applied, plausible substation faults, and crisplobserved substation faults using fault detectors. The following figure 8 presents the performance level when the different SCADA messages are input, which consists of case based reasoning and model based reasoning. There are two distinctive results observed.
[5] Zadeh L A, “Fuzzy sets”, Information and Control, Vol 8, ~ ~ 3 3 8 - 3 1965 8, [6] Russel T, Pye M, “Intelligent Energy Management systems-an operator’s view, IEE,Vo14 ppl54- 159, 1996 [7] Peng Y., Reggia J. A., ”Abductive inference models for diagnostic problem-solving”, Springer-verlag,1990 [8] Warwick K, Ekwue A, Raj A, “Artificial Intelligence Techniques in Power Systems”, IEE, 1997 [9] Partanen J.,Verho P.,Jarventausta P., “Using fuzzy sets to model the uncertainty in the fault location process of distribution”, IEEE Trans,Power Delivery,Vol.9 No2 April 1994 [lo] Seevers 0. C., ”Power system Handbook; design, operation & maintenance”, the Fairmont press, Inc., pp.230-232,199 1
Figure 8 A performance test on the diagnostic tool
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[ I l l McDonald J. R., Burt G. M., McArthur S . D. J. “Intelligent knowledge based systems in electrical power engineering”, Chapman & Hall, 1997
