application of knowledge-based expert system
TRANSCRIPT
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Application of Knowledge-based Expert Systemto High-voltage Transmission Line Maintenance
Mohd Junaizee Mohd Noor*, David Birtwhistle* and Stewart C. Bell**
*School of Electrical & Electronic Systems Engineering
Queensland University of Technology
**Powerlink Queensland
Abstract
Development of a knowledge-based expert system applied to high-voltage transmission lines
maintenance is shown in this paper. The expert system runs on the Matlabplatform, utilizingits proprietary Fuzzy Logic Toolbox. The inputs to the expert system are transmission linecomponent inspection record as observed by the maintenance crew during routine inspections.The output of the system indicates the overall condition of the transmission lines. Application of
the expert system methodology on glass insulator inspection is shown as an illustration. The
expert system may be of valuable assistance to utility engineers or asset managers in making
strategic maintenance decisions such as emergency maintenance, condition-based
maintenance or do nothing.
1. INTRODUCTION
Tower inspection results from periodic inspection
provide critical information for utilities to formulatetransmission line strategies. The effectiveness of
these strategies relies very much upon the quality of
inspection so that defects can be detected and
addressed accordingly to avert failures. Inspectiondata may be collected in quantitative or qualitative
forms: quantitative by making certain measurements
using test instruments and qualitative by visual
assessment. Visual inspection, unfortunately, is
subjective as judgment with regards to the degree of
defect is strictly based on human intuition.
Evaluation of defect levels may vary as it depends on
personal experience, knowledge, and condition.Consequently, a technique that can incorporate the
subjective judgment (i.e. cognitive uncertainty)
which is inherent in tower inspection is required.
The technique which is proposed in this paper is inthe form of a knowledge-based expert system that
will accept qualitative and quantitative data from the
tower inspection and process them to produce a
quantifiable output that indicates the actual condition
of the tower. Quantitative data from measurements
using instruments are entered as numerical inputs.
Cognitive uncertainties associated with data collected
by visual inspection and assessment are handledusing fuzzy logic as it was specifically developed to
deal with the fuzziness of human perception and
decision making process. Furthermore, fuzzy logic
provides an organized framework for dealing withlinguistic quantifiers such as good, normal, bad,
rusty, fair, moderate, poor which comes up duringvisual inspection and defect evaluation of existing
transmission towers. Inference as used by the expert
system is by way of IF-THEN rules withappropriately assigned weighting to indicate the
gravity of the rules.
The concept of fuzzy rule-based system has beenused in many applications such as that used by Tee et
al [1] in assessing concrete slab bridges. Kaminaris et
al [2] utilized the concept in substation maintenance
management and subsequently used it in an expert
system design. The application was also used by
Islam [3] to diagnose transformer faults. Kim &
Morcos [4] applied the concept in assessing the
useful life of ACSR conductors. Hathout [5] appliedthe technique to determine how safe existing
transmission lines in Ontario Hydro networks are and
later [6] utilized it to assist engineers in transmissionlines damage assessment.
2. COMPONENTS OF TRANSMISSION
LINES: THEIR FUNCTIONS & FAILURE
MODES
The main purpose of the transmission tower is to
carry overhead transmission line conductors and
earthwires above the ground [7]. In fulfilling this
role, it has to withstand all the variety of forces that itis exposed to with regards to the environment it is
located in. These forces include wind loads, erection
loads, maintenance loads, and changing of conductor
sags as conductors expand and contract with dailyload variations. In addition, the tower must also
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maintain the electrical clearances between live
conductors and any earthed body in the vicinity ofthe tower such that the lines do not induce anyhazardous voltage that could be harmful and
dangerous to the public. Finally, the tower also
provides a path to earth lightning current that
occasionally strikes the earthwire.
Transmission lines are basically made up of four
components [7]. Their functions and correspondingfailure modes are as follows:
2.1 FoundationsTower foundations provide anchorage for the tower
to ground. The type and depth of foundations dependon the soil condition and the orientation of the tower
(to cater for the compression and tension forces the
tower is subjected to). Most foundations areconstructed by using galvanized-steel reinforced
concrete (also termed grillage foundations). Since
the foundations are buried in soil, they are exposed to
corrosive elements of the ground. When the steel
reinforcement becomes rusty and brittle, they lose
their strength and the tower may become unstable.Rusty steel reinforcements can be detected by using
the half-cell measurement device [8]. Excessive soil
movement may result in displacement of the tower
foundations and making the tower unstable.
2.1 StructuresThe tower structure makes up the entire body of the
transmission tower. It can be categorically divided
into tower legs, tower body, and crossarms.
Depending on factors such as public concerns,
erection techniques and inspection practice, the
structure design can be of self-supporting lattice,cantilever or guyed poles, or framed structures. Most
structures are made of galvanized steel because of its
convenience for fabrication, relatively easy
transportation and strength. Steel towers are very
prone to corrosion which can result in structuralfailure. Other failure symptoms include
deformations, differential settlement and excessive
deflections.
2.3 Insulators
Insulators are utilized to attach the conductors to the
crossarms of transmission towers and serve two mainpurposes: to provide sufficient mechanical support
and to isolate live parts from earth potential. They aretherefore made up of a combination of dielectric and
mechanical strength materials. Typical dielectric
materials used are glass, porcelain, and polymers.
Steel or fiberglass rod provides mechanical strength.Failure modes can therefore be made up of either
electrical or mechanical depending on the damage to
these materials.
2.4Conductors/EarthwiresConductors carry rated current up to their maximum
design temperatures within their mechanical design
limits. In satisfying this purpose, they need to
maintain even sags throughout the line route so that
ground clearances stay within statutory limits.Conductors are mainly made up of stranded
aluminum alloys with galvanized steel
reinforcements. Because conductor construction islimited by drum lengths, conductors are joined
together using aluminum and steel compression
sleeves during construction. Conductors are prone tocorrosion especially in heavily industrialized areas
which have high levels of contaminants in the
atmosphere. The joints present critical weak pointsalong the conductor as corrosive contaminants
accumulate more easily in them than in the
conductors. Symptoms that show deteriorating joints
can be detected by measuring their temperature and
resistance [9].
3. LINE INSPECTION PRACTICES
Utilities maintain transmission lines by conducting
periodic inspection with the main intention being to
detect incipient defects that could lead to failures.These inspections are mainly based on visualexamination by trained linesmen. Most utilities
combine aerial and ground line inspections as part of
their maintenance regime [10]. Aerial inspection
using helicopters facilitates detection of major
component deterioration and macro inspection of the
structures in a circuit. Typically defects such as
broken insulator disks, severe tower deformation,conductor caging and foreign materials like birds
nests can be detected from the air. Ground line
inspections can detect defects such as weathering and
corrosion of steel structures, lattice member
deformation, concrete foundation cracks anddisplacements due to ground movement. Normally
visual aids such as binoculars and video/still cameras
are utilized to assist the inspection process [11].
Specific measuring instruments such as joint
resistance meters and thermovision cameras are
invariably used to detect conductor joint defects.
Conditions of the components are normallycategorized based on the severity of the defect as
perceived by the inspector. This can be made ofeither a grading scale such as from 1 to 5 (bad 1
good 5) or small-medium-large range. The results of
the inspection are normally used to trigger such
corrective actions as one of the following [12]: (1)requires immediate action (emergency maintenance);
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(2) requires inspection in next maintenance cycle
(condition-based maintenance); (3) no further actionrequired.
4. DEALING WITH UNCERTAINTY
Since defect severity assessment is based on human
judgment, there is a considerable level of uncertainty
about the results of the inspection. There are several
ways to handle uncertainties, such as [13]:
4.1 Bayesian inference:This method calculates the probability of occurrence
based on prior conditional or joint probabilities. It
also requires a set of hypotheses to initially suggestthe actual condition before inferring it to the prior
probabilities. In practicality, reference data may not
be available for calculation.
4.2 Certainty factors:A certainty factor is a numerical value between 0 and
1 that stands for the degree of confirmation of a
hypothesis based on certain evidence.
4.3 Dempster-Shafer theory of evidence:This method calculates belief functions -
measurements of the degree of belief. This approachinvolved many numerical computations and couldbecome very complex if there are many variables
involved.
4.4 Fuzzy logic:This method assigns degrees of membership from 0
to 1 to a function: 0 means not a member to afunction, 1 means a member of the function and
anything in between denotes partial memberships.
Membership functions can be specified using
linguistic properties inherent in human reasoning i.e.
tall, fast, high, and low.
The methods presented above have their own merits
and each would have its applications suitable for
specific reasons. Fuzzy logic is used in the expert
system application presented in this paper due to:
(a) its ability to represent and process databased on linguistic information
(b) its usability in a rule-based system
5. THE EXPERT SYSTEM
The expert system which is presented in this paper
attempts to capture the preceding information with
regards to the practice of transmission line
maintenance. The system accepts inputs in terms of
the condition of the major components of
transmission lines, namely the foundations,structures, insulators and conductors as reported by
the maintenance crew, processes the information in a
fuzzy inference system, and provides an output thatinfers the overall condition of the tower. The
inspection data is both qualitative and quantitative.
Figure 1 shows the structure of the expert system.
The system is designed to accept inputs based on the
condition of grillage foundations, steel latticestructures, glass cap and pin insulator strings andAluminum Conductor Steel Reinforced (ACSR)
conductors and earthwires. However, these inputs
may be changed based on the type of tower or types
of component used by the utility.
The system runs in the MATLABprogram platform
and utilizes its proprietary Fuzzy Logic Toolbox as
the inference engine. MATLAB is chosen because
of its flexibility, relatively simple programming andGUI facilities that make it easy to design.
Information about component defects and knowledge
about the effects of component defects are captured
and transformed into fuzzy IF-THEN rules in the
fuzzy inference system.
Each Fuzzy Rule block in the expert system
represents a fuzzy inference system. It contains input
and output membership functions and the rules
relating the inputs and outputs of the respectiveitems. Appropriate weighting is assigned to the rules
depending on the intensity of the relationship.
Mamdani-type fuzzy inference method is selected asit combines the fuzzy sets from the consequent of all
the rules through Matlabs built-in aggregation
operator and the resulting fuzzy sets are defuzzified
to yield a crisp output [14]. Defuzzification in thesystem is by way of centroid (center-of-area)
calculation.
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Figure 1: Structure of transmission line maintenance expert system
6. EXAMPLE APPLICATION:
ASSESSMENT OF INSULATORS
To illustrate the method, consider the information
gathered regarding the condition of glass cap & pininsulators on the tower.
During an inspection, the maintenance crew would
look at whether there are any broken insulator sheds
in a string. The crew would also check whether there
is sign of rust on the pins. These two observations are
critical to the operation of the insulators as they
indicate both the electrical and mechanical propertiesof the insulator respectively.
The number of insulators in a string is a function of
the transmission voltage: the higher the operating
voltage, the larger the number of insulators in a
string. For example, a 132kV suspension insulatorstring would normally have 10 standard insulators in
a string. Any broken insulator in a string wouldreduce the insulation level of the transmission lines.
There is nevertheless a certain level of tolerance thatutilities normally practice when they encounter
broken insulators in a string i.e. 1 or 2 broken
insulators in a 10-insulator string is still acceptablewhereas 5 broken insulators would require immediate
replacement. Therefore, what is significance is the
percentage of broken insulators in a string. Referring
to Figure 1, when the number of broken glass
insulators is entered into the expert system, it
consequently converts the number into percentageand enters the figure into Fuzzy Rule 1.
Pin corrosion on an insulator can be determined bylooking at pin diameter and appearance of rust on the
pin. Both are fuzzy values since during an inspection,
the crew would only be able to estimate the pin
diameter and judge the appearance of rust on the pin.The crew would scan the string to determine the
worst insulator in the string (i.e. the smallest pindiameter and the rustiest) and record them. An
example cap and pin insulator showing broken
porcelain disc and badly corroded pin is shown in
Figure 2. Referring to Figure 1, when these data are
fed into the system, they are processed by Fuzzy
Rules 2. The output of Fuzzy Rules 2 is in turn fedinto Fuzzy Rules 1. The output of Fuzzy Rules 1
indicates the overall condition of the insulators on the
tower.
Figure 2: Example of a broken cap and pin insulator
with arrow showing corroded pin
Inputs
Insulators
Glass sheds
Pin corrosion
% broken
Pin diameter
RustAppearance
FuzzyRules 2
FuzzyRules1
Conductors
Joint resistance
Joint temperature
FuzzyRules 3
Structure
Deformation
Corrosion
RustAppearance
Rust size
FuzzyRules 4
FuzzyRules5
FoundationsCorrosion
Displacement
FuzzyRules6
FuzzyRules 7 Tower
Condition
Output
Fuzzy inference system
Components
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6.1 Knowledge-base for insulator inference
engine
The knowledge-base for the insulator inference
engine is represented by the membership functions
and rules of Fuzzy Rules 1. These parameters can be
defined based on past operational records, industry
best-practice, technical specifications or
manufacturers recommendations through knowledge
acquisition techniques. The parameters used torepresent insulator condition are shown in
membership functions in Figures 3, 4 and 5. The
popular triangular and trapezoidal membershipfunctions are used [3]. In Figure 3, as discussed in the
previous section, it is shown that more than 50%
insulator shed breakage represents membershipfunction for large percentage of broken insulator
sheds.
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
%broken
Degreeofmembership
small medium large
Figure 3: Membership functions for percentage of
broken insulator sheds
0 1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
pin
Degreeofmembership
Bad Average Good
Figure 4: Membership functions for rust condition on
insulator pins
0 1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
insulator
Degreeofmembership
bad medium good
Figure 5: Membership functions for the output -
condition of insulator
The rules that are used in Fuzzy Rules 1 are as
follows:
1. IF [% broken is small] AND [pin is good] THEN
[insulator is good] (weight: 1)
2. IF [% broken is small] AND [pin is average]
THEN [insulator is good] (weight: 0.6)
3. IF [% broken is small] AND [pin is bad] THEN
[insulator is bad] (weight: 0.9)
4. IF [% broken is medium] AND [pin is good]THEN [insulator is medium] (weight: 1)
5. IF [% broken is medium] AND [pin is average]
THEN [insulator is bad] (weight: 0.6)6. IF [% broken is medium] AND [pin is bad] THEN
[insulator is bad] (weight: 0.9)
7. IF [% broken is large] AND [pin is good] THEN
[insulator is bad] (weight: 0.7)8. IF [% broken is large] AND [pin is average]
THEN [insulator is bad] (weight: 0.8)9. IF [% broken is large] AND [pin is bad] THEN
[insulator is bad] (weight: 1)
The rules are configured to relate the condition of
both the inputs (% broken insulators and condition of
the pin) to the output (condition of insulators) of thesystem. The weights associated to each rule indicate
the gravity of the relationship. For instance, thecombination of a large percentage of broken
insulators and bad pin reflects a higher degree of
severity compared to a large percentage of brokeninsulators and average pin condition. The output,after defuzzification by the system, is a single
number which is compared against a scale of 1 to 10
(1 bad 10 good) to indicate the overall condition of
the insulator.
6.2. Example operation
To illustrate the operation of the insulator fuzzy
inference system (Fuzzy Rules 1), consider a 132kVtower with 10-unit standard glass suspension
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insulator strings attached on it. During inspection, it
is noticed that 2 insulator units are broken in thestring. The worst pin diameter in the insulator stringis observed to be approximately 5 cm and rust
appearance on a scale of 1 (worse) - 10 (good) is 9.
One would expect that the insulator string is still
serviceable. When the inspection data are used as
input to Fuzzy Rule Block 1, it gives an output
measure of 8.756 denoting it is still within the
membership function for good (Refer to Figure 6).This measure would be then used by the transmission
lines engineer to suggest taking no action.
The same would be done on the rest of the
components of the transmission lines when all of the
components are used as inputs to Fuzzy Rules 7. Theoutput of Fuzzy Rules 7 will indicate the overall
condition of the tower in question (refer to Figure 1).
Note here that appropriate weighting is assigned inthe rules to indicate, for example, bad foundations
would have a more serious impact on the integrity of
the tower compared to bad conductors. The
transmission lines engineer would then be able to use
this measure to assist in making certain strategic
decisions such as emergency maintenance,attention in next periodic maintenance, or do
nothing.
7. CONCLUSIONS
Data collected visually during tower inspectionscontain high levels of cognitive uncertainty human
interpretation. This type of uncertainty, based on the
available methods mentioned in this paper, can be
best handled by fuzzy logic. The concept is applied in
a knowledge-based expert system that converts
subjective observations of tower component
inspection into a more objective and usefulrepresentation of overall tower condition. Application
of part of the expert system concerning the evaluation
of glass cap and pin insulators is shown in this paper
as an example. The output of the expert system can
be utilized as a basis for utility asset managers tomake maintenance decisions thus improving
maintenance quality and overall reliability.
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