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8/2/2019 Chen_Fault Section Estimation Using Fuzzy Matrix-Based Reasoning Methods_2011
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011 205
Fault Section Estimation Using FuzzyMatrix-Based Reasoning Methods
Wen-Hui Chen, Member, IEEE
AbstractA technique of fuzzy reasoning via rule matrix trans-formations has been developed to estimate fault sections for dis-tribution substations. This study extended the application of faultdiagnosis from binary reasoning to fuzzy reasoning. In the infer-ence procedures, the causalities of fault sections and the actions ofprotective devices were first represented by the fuzzy cause-effectnetworks (FCE-Nets). After performing some simple matrix oper-ations, the possible fault sections were estimated. This proposedapproach offers a clear framework, rapid reasoning, the ability tohandleuncertainty, and it has no problem with convergence duringthe diagnosis procedure.
Index TermsBoolean rule matrix, cause-effect networks, fault
diagnosis, fuzzy reasoning.
I. INTRODUCTION
THESE DAYS, most power systems are equipped with
supervisory-control-and- data-acquisition (SCADA)
systems for operational improvement applications. When a
fault occurs, it is imperative to limit the impact of outages to
the minimum and to restore the fault area as soon as possible.
This requires that the fault segment be identified from informa-
tion provided by protective devices. Various approaches have
been proposed to address fault diagnosis problems for powersystems. Among those techniques, rule-based expert systems,
artificial neural networks (ANNs), genetic algorithms (GAs),
and their hybrid models are commonly used. Although many
works have been performed for power system fault diagnosis,
recent research interests are especially focused on how to deal
with uncertainty inherent in the operation of protective devices.
In [1] and [2], the authors used fuzzy relations represented by
sagittal diagrams to deal with uncertainty for power transmis-
sion networks, and good results were obtained even in device
malfunctions. In order to apply an approach adaptive to network
topology change, the authors in [2] extended their work by in-
tegrating time-sequence information, protective schemes, and
network topology to a 3-D matrix. In [3], the authors presenteda hybrid fuzzy rule-based expert system for transmission-level
fault diagnosis. The fuzzy concept in this paper is to assign each
rule a subjective assigned possibility for the expert system to
Manuscript received March 26, 2010; revised July 10, 2010; accepted July13, 2010. Date of publication September 07, 2010; date of current version De-cember 27, 2010. This work was supported by the National Science Councilof Taiwan (NSC98-2221-E-027-072 and NSC98-2218-E-002-012). Paper no.TPWRD-00222-2010.
The author is withthe Graduate Institute of Automation Technology, NationalTaipei Universityof Technology, Taipei, Taiwan (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TPWRD.2010.2061873
perform reasoning. The fuzzy reasoning process was carried out
by performing max-product composition.
Petri nets are a useful tool for event modeling in a concur-
rent structure. However, it lacks the ability to handle uncer-
tainty in information. As such, the authors in [4] applied fuzzy
Petri nets approach for fault diagnosis in transmission networks.
They combined fuzzy logical reasoning with Petri nets to deal
with uncertainty existing in the operation of protective devices.
The analysis of cause-and-effect relationships for fault diag-
nosis has been a favorite application in [5] and [6]. In [7] and [8],
a fast fault diagnosis algorithm based on cause-effect networks
(CE-Nets) was proposed for distribution substations. Fault diag-
nosis can be considered as a classification task. The authors in
[9] have successfully applied a data-mining-based fuzzy classi-
fication algorithm to handle fault cause identification problems
and demonstrated good results.
To summarize, previous research tended to use a fuzzy re-
lation to deal with the uncertainty in the operation of protec-
tive devices. In addition, they preferred using subjective pos-
sibility given by the operators as a certainty factor in a rule.
Note that many previous works assumed that there is no uncer-
tainty between the operations of the relays and circuit breakers
(CBs). Fault diagnosis problems for power systems range from
power equipment [10], [11]; substations [12], [13]; distributionnetworks [14]; and transmission networks [15] to power plants
[16]. For different applications, the characteristics of a problem,
domain knowledge, and concerns would vary accordingly. To
endow CE-Nets with inexact reasoning ability, in this paper, a
new technique using the fuzzy rule matrix transformation was
proposed for fault diagnosis in distribution substations.
II. FUZZY CAUSE-EFFECT NETWORKS (FCE-NETS)
A. Review of Cause-Effect Networks (CE-Nets)
CE-Nets are graphic-modeling methods for representing the
causalities between faults and the actions of protective devices[7], [8]. They consist of three kinds of nodes: 1) fault section
node, 2) relay node, and 3) CB node. If two nodes and
are related, a directed arc is placed on the graph from node
to node , indicating the relation. There are three kinds of di-
rected arcs: protected-by arc, cause arc, and backup-by arc, that
describe different connections between two nodes.
A CE-Net can be represented by a binary matrix. The trans-
pose of the matrix is equivalent to the reversal of all arrows
in the associated CE-Net. Therefore, we can use this property
to achieve backward reasoning to estimate the possible fault
causes. A CE-Net can be easily derived from the system con-
figuration.
0885-8977/$26.00 2010 IEEE
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206 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011
Fig. 1. Simple model distribution system.
Fig. 2. CE-Net that represents the model system.
A simple model distribution system, as shown in Fig. 1, is em-ployed to illustrate the concept. The model system is protected
by overcurrent (CO) relays, low-energy over current (LCO) re-
lays, and CBs. Suppose that a fault occurs at the feeder F1
and causes the action of the relay CO1, which trips the circuit
breaker CB1. If CB1 fails to open at this moment, the backup
protective relay CO3 will operate to trip the circuit breaker CB3.
This event is described in the portion of the dotted outline in
Fig. 2. After considering all possible fault sections, the CE-Net
that represents the model system can then be derived.
B. Concept of FCE-Nets
Since human knowledge can contain linguistic terms withsome degree of uncertainty, it is desired to express the degree
of certainty of a rule as a real number between 0 and 1, and in-
corporate such an expression in the inference.
A rule consists of an antecedent part and a consequence part,
with the antecedent parts describing causes and the consequent
parts describing effects. For example, a rule could be IF (relay
CO1 operates) THEN (circuit breaker CB1 tripped). However,
in some situations, CB1cannot be asserted to have tripped, when
CO1 operates due to incorrect settings or equipment malfunc-
tion. In the classic (nonfuzzy) inference, when the condition in
the antecedent part matches the effect, its consequent part is as-
serted. This is not necessarily true for protective device opera-
tion. This uncertainty regarding the operation of protective de-vices should be considered for practical applications.
Fig. 3. Trapezoidal fuzzy number.
Fig. 4. Membership functions for the linguistic term set.
The certainty factor refers to the degree of confidence thatthe event will occur. As operators knowledge may contain lin-
guistic terms with some degree of uncertainty, the use of cer-
tainty factor is a good way to describe the uncertainty in nu-
merical expression. In this study, the certainty factor is used to
represent the uncertain characteristic in conditions and rules.
The assignment of a certainty factor can be intuitively given
by experts using a linguistic term set [17] or based on some
mathematical operations, such as the frequency of occurrences
from historical data. For example, the operators may say that a
fault causing the main protective relay to operate is extremely
true or that the CB that will be tripped is very true. They cannot
precisely tell you how true it is in a quantitative way. Table Ilists the linguistic terms and their corresponding fuzzy num-
bers. A fuzzy number can be characterized as a four-tuple
, where denotes the interval in which the mem-
bership value is equal to 1, and and indicate the left and
right width of the trapezoidal distribution. Fig. 3 shows a trape-
zoidal fuzzy number parameterized by . The cor-
responding membership functions are given in Fig. 4.
The other way to decide the certainty factor is based on the
frequency of occurrences from historical data. For example,
from historical data, the frequency of occurrences of a fault
hitting the feeder F1 was up to 20 times, and only in 19 times,
the fault has caused the operation of relay CO1. According to
the statistical data, we can calculate the certainty factor of therule as 19/20 or 0.95. With the certainty factor, the rule can
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CHEN: FAULT SECTION ESTIMATION USING FUZZY MATRIX-BASED REASONING METHODS 207
TABLE IA NINE-MEMBER LINGUISTIC TERM SET
Fig. 5. Associated graph for fuzzy implication rules.
Fig. 6. Basic node-arc relations in FCE-Nets.
be expressed as IF (a fault at F1) THEN (relay CO1 operates)
0.95)
Suppose that and are node conditions in a given FCE-
Net. The general formulation of a fuzzy implication rule can be
denoted as
(1)
This infers that the truth of condition implies the truth of
condition with a certainty factor . The value of a certainty
factor is between 0 and 1, which indicates the strength of thebelief in the rule. The larger the value is, the more reliable the
rule is. The associated graph for (1) is shown in Fig. 5.
There are three basic node-arc relations of FCE-Nets, as
shown in Fig. 6.
Generally, the choice of a good certainty factor needs some
expertise. In order to avoid a bias in assigning the certainty fac-
tors, it is suggested to determine the value based on experts or
senior operators experience and historical data.
Since operators knowledge can contain some degree of un-
certainty, a rule expressed for the linguistic knowledge can be
described as
IF (a fault at F1) THEN (relay CO1 operates)
For different occasions, the same rule may change to
IF (a fault at F1) THEN (relay CO1 operates)
or we can express it in a general form:
IF (a fault at F1) THEN (relay CO1 operates) (
or ET or VT or T, )
The determination of which linguistic terms
to be used is based on oper-
ator experience and the frequency of occurrences. In otherwords, according to confirmed cases and operator experience,
the value of a certainty factor can be different in a rule.
Examples of node-arc relations are given below.
IF (a fault at F1) THEN (CO1 operates)
IF (CO1 operates) THEN (CB1 tripped) .
A SCADA system typically consists of a master station,
communication networks, remote terminal units (RTUs), and a
number of transducers. A transducer is a device which provides
a transformed output in response to a specific quantity, such
as feeder currents and bus voltages. For example, a current
transducer provides the RTU with current information by con-
verting current transformer (CT) signals to the value that can be
handled by a RTU, and then the RTU transmits this information
to the control center through communication networks.
In the signal transmission process, there may be a data mis-
match between the measured quantity of a substation and the
SCADA computer of the control center due to transducer im-
perfection or software conversion errors. For example, if a fault
occurs at feeder F1 in a substation, the circuit breaker connected
to feeder F1 will be tripped, which leads to the current of feeder
F1 reaching zero. In normal situation, this zero value will be dis-
played in the control center. When the current transducer asso-
ciated with feeder F1 has improper installation or ill-calibration,
the practical feeder current could not correctly be displayed in
the SCADA computer. As a result, it may show a very small
value instead of zero.
Conventional rule-based approaches do not allow linguistic
variables to appear in the condition of a rule, and have very
little power in dealing with imprecise information. In order to
deal with this situation, the proposed approach utilizes fuzzy
membership functions to represent measured quantities derived
from SCADA systems instead of specifying a fixed value, and
use fuzzy inference techniques to perform inexact reasoning in
the proposed inference algorithm.
As an opened CB leads to no current passing through it, the
degree of truth in the condition circuit breaker CB1 is tripped
can be determined by the degree of truth that the value of currentpassing through CB1 is near zero. A membership function deter-
mining the degree of CB in open status is shown in Fig. 7. For
example, suppose the value of a current passing through CB1
is 150 A. One can therefore infer that the degree where CB1
tripped is 0.
The certainty factor can be viewed as the degree of confidence
that the event will occur. The assignment of a certainty factor
can be intuitively given by experts or based on some mathematic
operations, such as the frequency of occurrences from historical
data. Generally, the choice of a good certainty factor needs some
expertise. In order to avoid a bias in assigning the certainty fac-
tors, it is suggested to determine the value based on experts or
senior operators experience and historical data.
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208 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011
Fig. 7. The membership function for CB in open status.
Fig. 8. The associated FCE-Net for the model system.
C. Matrix Representation of FCE-Nets
The rules with linguistic certainty factors can be represented
as the fuzzy rule matrix . This matrix describes the relations
between causes and effects of FCE-Nets. Once the fuzzy rule
matrix is established, the diagnosis algorithm can then be per-
formed by matrix operations.
A fuzzy rule matrix associated with conditions is a -by-
matrix with all ones on the diagonal by reflexivity because each
condition implies itself. The entry means that con-
dition implies condition with the certainty factor , and
indicates that there is no implication between
and . A fuzzy value in the entry of the fuzzy rule matrix givesthe degree of confidence in how condition implies the truth
of condition . The simplemodelsystem inFig. 1 isused again
as an example to illustrate the concept of matrix representation
of FCE-Nets. The associated FCE-Net for the model system is
shown in Fig. 8. The set of conditions is listed in Table II.
According to the connectivity of each node in Fig. 8, the fuzzy
rule matrix that represents the FCE-Net can be built as: see equa-
tion at the bottom of the page
TABLE IITHE SET OF CONDITIONS AND THEIR DESCRIPTIONS
III. REASONING WITH FUZZY RULE MATRICES
In this section, an inexact reasoning algorithm via fuzzy rule
matrix transformation for FCE-Nets is presented. Assume that
a FCE-Net contains a set of conditions. Each condition con-
tains a high level of linguistic expression for humans to use (e.g.,
relay CO1 operates). However, this string expression is in-
convenient and inefficient for computers to process. Therefore,
we defined some vectors to transform string based conditions
into numerical vectors for reasoning and computation. The fol-
lowing vectors and matrix operations are defined to develop the
inference procedures.
1) Truth State Vector : The truth state vector is em-
ployed to represent the fault symptom with the status of protec-
tive devices. This vector contains the truth values for a set of
conditions, . Each component is defined by
, where is the truth value of condition .
2) Fault Node Vector : The fault node vector is de-
fined to represent the fault section nodes in a given FCE-Net.
This vector contains Boolean valued components for a set of
conditions. If node condition is associated with a fault sec-
tion node, the value of is assigned to 1, otherwise is 0
if
otherwise
This vector is defined for extracting the nodes that belong to
the fault section node through fuzzy intersection operation.
3) Backup Node Vector : The backup node vector is
employed to represent the backup relay nodes in a given FCE-
Net. This vector contains Boolean valued components for a
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CHEN: FAULT SECTION ESTIMATION USING FUZZY MATRIX-BASED REASONING METHODS 209
set of conditions. If node condition is associated with a
backup relay node, the value of is assigned to 1, otherwise
is 0.
if
otherwise
This vector is defined for extracting operated backup relaysthrough fuzzy intersection operation.
4) Fuzzy Min-Operator : The fuzzy min-operator for two
column vectors and on the corresponding entry is written
as . For example,
In the inference procedures, there is a process to remove the
status of operated backup relays in the truth state vector if the
action of the backup relay is caused by a main relay failure.
This process is used to discriminate the source that activatesbackup relays. This discrimination can be carried out by using
the intersection operation. As such, we introduced this operator
to perform the required computation for deriving a systematic
inference algorithm.
5) Fuzzy Multiplication Operator : The fuzzy multipli-
cation operator for two matrices and is written as
The row-by-column matrix product is perfromed by replacing
multiplication and addition with the min and max operations,
repectively. For example,
The fuzzy multiplication operator is used for performing truth
state transformation on the transpose of the fuzzy rule matrix
and a truth state vector that contains the degree of truth in its en-
tries. The entry of a fuzzy rule matrix represents an implication
between two conditions. As truth state contains in-
formation of fault symptoms, the function of this operator is to
perform a composition transformation that propagates the truth
state leading backward into the fault cause. In this study, we
adopted max-min operation to replace real multiplication and
addition in arithmetic matrix multiplication. The inference pro-cedures are summarized as below.
At first, we built a -by- fuzzy rule matrix according to
the given FCE-Nets, with rows and columns indexed by nodes.
If an arc presented, we put the certainty factor in cell
; otherwise we left as zero. denotes a
directed arc from node to node . According to fault symp-
toms, we put the truth value of each condition in the entry
to derive the truth state vector. Calculate transformation vector,
using (2). The meaning of this transformation is to propa-
gate the truth state of given fault symptoms leading backward
into the fault cause. The vector contains information that
causes the fault symptoms.
(2)
Then, wecompared with . The step ofcomparing
with with was to check if there was a device failure. If
equaled with , this means that there was no failure operation
at feeder protection; otherwise, failures did occur. If did
not equal with , the process went to update and assign it
to using (3); otherwise, assigned to .
(3)
Fuzzy min-operator in (3) is used to remove the status of op-
erated backup relays in the truth state vector when the action of
the backup relays is caused by a main relay failure. The fuzzy
multiplication operator is used for performing truth state trans-
formation on the transpose of the fuzzy rule matrix and the truth
state vector operated on a backup relay node by fuzzy min-op-
erator. As such, the updated transformation vector using (3) is
to remove the status of backup relays from fault section candi-
dates.
As the vector contained information about fault causes,
we selected only fault section nodes with the entry value greaterthan a threshold as estimated fault sections. The selection
of fault section nodes from can be achieved by computing
. As suggested in [3], the possibility of a solution below
0.8 would be meaningless. The threshold value for selecting
fault sections was set to 0.5 in this study. This value was suit-
able after testing on many runs. The flow chart of the inference
procedures is shown in Fig. 9.
IV. CASE STUDY
A typical Taipowers secondary substation, as shown in Fig.
10, is employed to illustrate the reasoning process of the pro-
posed approach. The substation is composed of three sub-trans-mission lines, three main transformers, two tie circuit breakers,
one 69 kV primary bus bar, and three 11.4 kV secondary bus
bars. Each secondary bus bar contains five radial distribution
feeders. Each feeder is protected by three CO relays and one
LCO relay. For example, feeder F1 is protected by three CO
relays: CO1-A, CO1-B, and CO1-C as well as one LCO relay:
LCO1. The protective relays for 11 kV bus bars also serve as the
back-up protection for their connected feeders. This study case
is the same as case 2 in [8] except there is missing information
in this case.
Operated relays: CO3-A, CO3-B, CO3-C, COM1-A,
COM1-B, COM1-C, CO8-B, CO8-C
Tripped circuit breakers: CB-M1, CB-8
Failure devices: LCO8, CB-3
Missing information: the status of LCO8, CO8-B, CO8-C
A. Fault Diagnosis by FCE-Nets Approach
In this example, transformer TR #2 has de-energized for
maintenance and CB Tie-1 is in closing status. A three-phase
fault occurs at the feeder F3. Relays CO3-A, CO3-B and
CO3-C operate correctly while the circuit breaker CB-3 fails
to trip. Owing to the tripping failure of CB-3, the backup
breaker CB-M1 is tripped by the relays COM1-A, COM1-B
and COM1-C. Meanwhile, a double-line to ground fault at
phases B and C happens at feeder F8, which causes CO8-B andCO8-C operating to trip CB-8. However, the status of relays,
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210 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011
Fig. 9. The flow chart of the proposed inference procedures.
Fig. 10. The distribution substation for the study case.
LCO8, CO8-B and CO8-C, is missing. The set of conditions is
listed in Table III.
The certainty factor is determined by operators according to
their knowledge and the frequency of occurrences from con-
firmed events of historical data, given in Table IV. It is assumed
that the event in the same category has the same occurrence fre-quency.
TABLE IIITHE SET OF NODE CONDITIONS
TABLE IVCONFIRMED EVENTS FROM SCADA SYSTEM DATABASE
The corresponding FCE-Net associated with this fault case is
shown in Fig. 11. The inference procedures are described step
by step as follows.
Step 1: Step 1 At first, we build the fuzzy rule matrix
according to Fig. 11. As the dimension of the matrix
is 51-by-51, only nonzero entries of the matrix are
listed in Table V.
Step 2: Step 2 The currents for the related devices are listed
in Table VI.
The possibility of a condition that a relay operates
is evaluated according to the device current and
the membership value that the device is in its open
status, set in Fig. 7. The nonzero entries of truthstate vector are listed in Table VII.
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CHEN: FAULT SECTION ESTIMATION USING FUZZY MATRIX-BASED REASONING METHODS 211
Fig. 11. The associated FCE-Net of the study case.
Step 3: Step 3 The fuzzy truth state transformation can be
obtained using (2), and the results are listed in
Table VIII.
Step 4: Step 4 As these two vectors, and , are not
equal, the inference procedure goes to update the
fuzzy truth state transformation using (3). Then, we
assign the updated values to . The value of vector
is listed in Table IX.
Step 5: Step 5 As the vector contains information about
fault causes, the fault sections can be retrieved by
selecting fault section nodes from with entryvalue greater than 0.5. This can be achieved by first
performing , and then selecting the entry
value greater than 0.5 as estimated fault sections.
The nonzero entries of the fault node vector are
listed in Table X.
The results after selecting the fault section nodes by
performing are listed in Table XI
From Table XI, the estimated fault sections can be
obtained by selecting the entry value greater than
0.5. Therefore, feeders F3 and F8 are selected as
fault sections, i.e., multiple faults at F3 and F8 are
estimated. The inference results are summarized in
Table XII.
B. Fault Diagnosis by CE-Nets Approach
In order to make comparisons with CE-Nets based approach,
the method presented in [8] was applied to this fault case. The
associated CE-Nets and the rule matrix are the same as those of
case 2 in [8]. The inference results using CE-Nets approach are
summarized in Table XIII.
Note that the fault situation has missing signals, which is dif-
ferent and more complicated than that in case 2 of [8]. From
the experimental results, as shown in Tables XII and XIII, the
proposed FCE-Nets can correctly identify the fault sections, F3
and F8, while the approach using CE-Nets fails to find the faultsection F8 due to incomplete information.
TABLE VNONZERO ENTRIES OF FUZZY RULE MATRIX
TABLE VIDEVICE CURRENTS OF THE STUDY CASE
TABLE VIINONZERO ENTRIES OF TRUTH STATE VECTOR
TABLE VIIIENTRY VALUES OF TV
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212 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 1, JANUARY 2011
TABLE IXENTRY VALUES OF T
TABLE XNONZERO ENTRIES OF FAULT NODE VECTOR
TABLE XIENTRY VALUES OF T F
In general, CE-Nets approach has some advantages in fault
diagnosis but only work well when the fault information iscomplete and certain. FCE-Nets approach is an extension of
TABLE XIIESTIMATED RESULTS USING FCE-NETS APPROACH
TABLE XIIIESTIMATED RESULTS USING CE-NETS APPROACH
CE-Nets approach with the ability of handling uncertain and
incomplete information that may happen in power system oper-
ation. This example demonstrates that the proposed FCE-Nets
approach outperforms the method presented in [8].
When the information is complete, both fuzzy (FCE-Nets)
and non-fuzzy (CE-Nets) approach can obtain the correct re-sults. In the situation where a status signal is missing, CE-Nets
approach fails to find the fault sections. From this study, it is ob-
served that the proposed algorithm has the ability to infer mul-
tiple faults even when a failure device and incomplete informa-
tion or missing signals occur. The merits of the proposed ap-
proach compared to the approach in [8] are summarized below.
1) The proposed approach can handle linguistic terms in con-
ditions and rules. It is more flexible than that in [8] as it al-
lows the use of certainty factors in a rule rather than crisp
numerical values.
2) The proposed approach provides the results with quantita-
tive confidence level for all estimated fault sections, which
makes the results more conclusive.3) The proposed approach successfully integrates fuzzy rea-
soning with graphical model representation. Therefore, the
proposed approach has the ability to handle incomplete and
uncertain information in practical problems with fast com-
putation speed.
V. CONCLUSION
In this study, we developed fuzzy CE-Nets and presented a
new reasoning algorithm for fault diagnosis in distribution sub-
stations. The proposed approach is capable of representing un-
certain knowledge and performing fuzzy reasoning through ma-
trix based transformation. Since knowledge representation withthe fuzzy CE-Nets model is based on graphical methodology, it
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CHEN: FAULT SECTION ESTIMATION USING FUZZY MATRIX-BASED REASONING METHODS 213
is easy to understand the relationship between the rules and con-
ditions. Also, it is possible to predict the inference results in ad-
vance by observing the flow of truth state in the FCE-Nets when
some conditions are specified. Since the proposed reasoning al-
gorithm requires only simple matrix operations, it is well suited
for integration with existing SCADA systems for on-line appli-
cations.ACKNOWLEDGMENT
The author would like to thank Prof. C.-W. Liu of National
Taiwan University for his guidance and Prof. C.-J. Chou of
National Taipei University of Technology for his insightful
conversations.
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Wen-Hui Chen (M04) was born in Taiwan in 1965.He received his B.S. degree from National TaiwanUniversity of Science and Technology, and the M.S.and Ph.D. degrees from National Taiwan University,all in electrical engineering. From year 1992 to2000, he held the position as a senior engineer andreceived numerous employee outstanding awards atTaiwan Power Company (Taipower Co.), Taiwanslargest power utility company. He is currently an
associate professor at the Graduate Institute ofAutomation Technology, National Taipei University
of Technology.