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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.

    8. REFERENCES

    [1] A. B. Tee, M. D. Bowman, and K. C. Sinha,

    "Application of fuzzy logic to conditionassessment of concrete slab bridges,"Transportation Research Record, pp. 22-30,

    1988.

    [2] S. D. P. Kaminaris, B.C.; Machias, A.V.,"Substation Maintenance Using Fuzzy Sets

    Theory And Expert System Methodology VO -2," presented at Athens Power Tech, 1993. APT

    93. Proceedings. Joint International PowerConference, 1993.

    [3] S. W. Mofizul Islam, T.; Ledwich, G., "A novel

    fuzzy logic approach to transformer faultdiagnosis,"Dielectrics and Electrical Insulation,IEEE Transactions on [see also Electrical

    Insulation, IEEE Transactions on], vol. 7, pp.177-186, 2000.

    [4] S. D. Kim and M. M. Morcos, "Diagnosis of

    useful life for ACSR conductors using a fuzzyinference system," IEEE Power EngineeringReview, vol. 22, pp. 61-64, 2002.

    [5] I. Hathout, "Safety evaluation of existingtransmission lines using fuzzy set theory,"

    presented at Uncertainty Modeling and Analysis,

    1990. Proceedings., First InternationalSymposium on, 1990.

    [6] I. Hathout, "Damage assessment of existing

    transmission towers using fuzzy weightedaverages," presented at Uncertainty Modeling

    and Analysis, 1993. Proceedings., Second

    International Symposium on, 1993.[7] C. Bayliss, Transmission and Distribution

    Electrical Engineering, 2nd ed. Manchester:

    Newnes, 2001.[8] J. Duxbury, "Foundation Steel Corrosion

    Inspection & Repair at BCHydro," presented at

    1st Annual Overhead Transmission LineEquipment, Inspection & Maintenance PracticesConference, Monterey, California, USA, 2002.

    [9] L. I. Persson, "Corrosion Attack in MidspanJoints in ACSR Transmission Conductors,"

    presented at 10th International Conference on

    Electricity Distribution, CIRED, 1989.

    [10] V. Polimac and J. Polimac, "Assessment ofPresent Maintenance Practices and Future

    Trends," presented at Transmission and

    Distribution Conference and Exposition, 2001.[11] A. Stewart, "Airborne Inspection Technology:

    Market Survey," presented at 1st Annual

    Overhead Transmission Line Equipment,Inspection & Maintenance Practices Conference,

    Monterey, California, USA, 2002.

    [12] G. Jaensch, H. Hoffmann, and A. Markees,

    "Locating Defects in High Voltage TransmissionLines," presented at IEEE 8th International

    Conference on Transmission & Distribution

    Construction, Operation & Live-Line

    Maintenance, 1998.

    [13] K. S. L. Leung, W., "Fuzzy concepts in expertsystems," Computer, vol. 21, pp. 43-56, 1988.

    [14] Fuzzy Logic Toolbox for use with Matlab. Natick,

    MA: The MathWorks Inc., 1998.